cs phd essay

Writing a Statement of Purpose

Tips on writing an sop - by arun vasan.

Universities in the US appear to believe that the logical end of education is enabling an individual to think on her own. This is why they ask you for a "personal" statement to give an account of experiences which you believe helped you decide to apply, your expectations from graduate school, what you propose to do in school.... Of course, the typical personal statement is not so personal, and is usually edited by half a dozen people at least.

The main idea behind this exercise is to give the admissions committee an idea of what to expect from a prospective student apart from her scores, GPA, and letters of recommendation. Who would've thought that actually asking a student to tell what he thinks of himself was a good idea ? The SOP, though it sounds unparliamentary, is a crucial part of the application process and needs a lot of time and effort.

I have put a few points down with my tongue firmly in my cheek. These are CS specific, but may apply to other disciplines as well. There are sharp differences of opinion among the graduate student/ professor community as to what makes an SOP tick. I take responsibility for neither the grammar nor the contents of this page. Use these tips at your own risk.

In case you are wondering, NO, I am not jobless. Yeah, I know, my soul just cries out to be blessed. Now, now, wipe the tears of gratitude, and prepare for the most enlightening sermon you will hear about grad school admissions.

Algorithm S

• 0. RTFAF: Read The Fine Application Form. Don't write a one-size-fits-all-univs SOP.
• 1. State upfront who you are and what you want. One should not have to search using a word processor whether you want an MS or a Ph.D.
• 2. Tell what you intend doing with your degree. Inevitably, this boils down to a suitable permutation of words from the set {creative, career, industry, academia, research, professor, university, lab,startup}.
• 3. Avoid hot air. Adjectives like thrill, passion, excitement, joy, etc., should be avoided like the plague. Explain what you expect from grad school. Of course, we all want a job, but try putting it down as politically correctly as you can.
• 4. Avoid quotations. You may have "miles to go before you sleep", "chosen the road less travelled", or "your-favourite-cliche-quote-from-high-school-here", but it ain't a personal statement unless you are quoting yourself, is it ?
• 5. Use simple English. Resist the temptation to use your new-found vocabulary from the GRE word lists.
• 6.Describe your experience. Don't say you were introduced to CS as a suckling infant, you started speaking in Python before your mother tongue, yada, yada, yada... No one actually cares for your experience as a kid, so keep it brief.
• 7. The most important experience you would've had would be your undergrad. Of course, I mean academic work. As an aside, I firmly believe that the day you really graduate is the day you realise you wasted four years. Describe your coursework tersely.
• 8. Explain a select few projects you did in gory detail and why that got you interested in research. This is a point of much debate. Personally, I like explaining things in detail while many people prefer "high level" stuff. The catch with my way is that you could say something blatantly wrong and possibly screw up your chances completely. Again, I feel that if someone knows what the hell she is talking about, she should be confident enough to sell what she did. I suggest you show your SOP to profs, preferably those who are writing your letters, to make sure you are not shooting yourself in the foot with amazing accuracy.
• 9. Articulate why you choose to work in the area you want to work on. For example, kernel hacking gives you the high, your best buddy is the memory allocator, etc., so you want to work in O/S. Or, you increase your treadmill speed like TCP increases its  cwnd , you do a packet sniff to find out protocols used instead of chatting in a messenger, your concept of networking is making computers talk, so you want to work in networks. In particular, it will be ideal if it was something you did best. I've heard of a case where someone said the thing she did best was cooking. The story goes she baked a cake and sent it to the admissions committee. Harvard, rumour has it, fell hook, line, and sinker for this. The professors in CS@UM most likely don't care for your culinary expertise, in case this gives you ideas.
• 9.1 You could possibly angle for more than one area. If you can show some prior work, or what you can do, in more than one area, you are good. However, you should avoid things like "I like theory, systems, AI, and NC very much. Graphics and Software, a little less".
• 10. Once you've explained why you like some area(s), explain how you will fit in with work being done in *that* univ. Say how you, Prof.Foo, and Prof.Bar can attain the holy grail of networking together. You should appear in awe of them, yet appear indispensable to their work. Avoid mentioning persons alone, i.e., qualify a professor by the group he leads/is part of. You can rest assured any CS prof will be part of some group with what she thinks is a cool abbreviated name. This way you won't antagonise a rival professor in the same area who actually sits on the committee.
• 11. Market yourself with concrete statistics. I won't believe it myself if you claim you are the second coming of Knuth. It is very unlikely that the profs of a dept. will. After all, it is their fate to have seen a billion SOPs before yours and see many more after yours. That said, mention things like "I was ranked in the top 0.123% of the FOO exam conducted by the BARs." exactly once.
• 12. Try being humorous without sounding like a clown. Wit is something which really can't be forced into writing. So it is perfectly fine if you write something totally serious as long as it is cogent and forceful. Finally, finish off with a flourish.
• 13. If (you aren't sick and tired) goto step 0.
• 14. Stop reading this page this minute and go work on your application.

I most definitely assert my rights to be identified as the author of this work in accordance with all known, unknown, and yet-to-be-framed copyright laws of this planet. Any resemblance to work produced by a certain Don of the west coast is entirely intended. © Arunchandar Vasan.

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CS Statement of Purpose Guide

This page provides advice on writing your “statement of purpose” and other graduate school and fellowship essays. Many programs may ask for several essays.

Keep in mind that your essay (and all of your application materials), will be read by professors of computer science and not by admissions officers. You should assume that your readers are very well-trained in computer science.

What are they looking for?

In general, your statement of purpose should convey the following:

• You are genuinely interested in research;
• You have an idea of the subdiscipline(s) that you would like to do research in graduate school and you can speak about them with some level of sophistication;
• You’ve had some research experience(s);
• You understand the research process from selecting a problem to solving it to presenting your results;
• You’ve done your homework about this particular school and have some ideas of which faculty members and/or research groups you’d like to work with.

Organization of the Essay

There are many ways to write a good statement of purpose essay, but here’s one possible structure that works well:

• In the first paragraph, describe the area or areas of computer science that you plan to study in computer science. The more specific you can be, the better. This is not a contract that forces you to study that subdiscipline and you might change your mind later. However, most graduate schools are more more inclined to accept students who have a good idea of an area that they plan to study. After describing the area, give a summary of your prior research experiences and an overview of the contents of the rest of your statement.
• In the next several paragraphs, describe the research projects that you’ve worked on, the challenges, the approach, and your contributions to the project. A typical situation for a Mudder applying to graduate school is one summer of research, one summer of an internship at a company, and a clinic project (which you will just be starting as you write your statement of purpose). Your summer research is the most relevant part for graduate school and should come first; It merits at least one substantial paragraph. Your clinic project is likely to have enough of a research component to merit a second paragraph. If your summer internship had some aspect of research, that can be a third paragraph. We encourage you to consult with your adviser in developing the structure for your essay based on your own experiences.
• Next, one paragraph can be devoted to describing the research that you would like to conduct in graduate school in some detail and your long-term career ambitions.
• The last paragraph should be customized to indicate why you want to go to this particular graduate school. You should spend some time looking at the web pages and publications of researchers at that university and mention the faculty and the research projects that you would be interested in joining. Generally, it’s wise to mention at least a few faculty members and projects.

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PhD in Computer Science

Our doctoral program is a full-time program: Admitted doctoral students have only the Ph.D. as their degree objective, and they have guaranteed funding for up to five years.

GRE Required for the Fall 2024 cycle

Please note: students applying to graduate programs in Fall 2024 are required to take the GRE. However, students applying for the 4+1 programs do not need to take the GRE.

Applying to the PhD Program

2024 admission requirements.

Questions about our PhD program should be directed by email to [email protected] .

The required components of your application are:

• The online application form
• Three (3) letters of recommendation
• All applicants should upload a scanned copy of their transcripts.  Applicants should not send an official transcript to the Graduate Office unless they are admitted and accept our offer of Admission.
• All applicants may self report GRE and TOEFL scores. Official test scores are not required unless you are offered admission into the program, and you accept our offer of admission.
• TOEFL or IELTS scores (required for non-native English speakers, photocopy accepted, original required upon admission)
• The $40 application fee

These items must reach us by December 15, 2023  in order for your application to receive full consideration.

If English is not your native language, we require that you take the TOEFL (Test Of English as a Foreign Language) or the IELTS (International English Language Testing System exam). An IELTS band score of 7.0 or above is required for Dartmouth Programs, but we have no specific test score requirements for the TOEFL or GRE. If you are transferring from a U.S. university, we may waive the language test. As with the GRE, we will accept a photocopy until the official report is available, but the photocopy must reach us by  December  15, 2023 .

We prefer that your recommenders provide their recommendations online. Once you have listed their names on the application, an email will be sent to them with a link to the application site. If necessary, we will also accept recommendations in sealed envelopes through the mail.

We require a $40.00 application fee, to be paid by credit card online. The fee helps to cover the cost of processing your application and is non-refundable. The application fee will not be waived.

Like many graduate programs, we base our admissions decisions primarily on the information requested above. We have no minimum test scores. We recognize that you may have talents and experience that do not shine through the forms and test scores.

Use the Application Essay section of the online application to give additional information. Do you have a specific reason for coming to Dartmouth? Specific goals for your study? Significant work experience? Why do you want to do advanced training in computer science? Include any publications you have authored.

In the Computer Science Supplement section of the online application, attach examples of your work. This is also the place where you can attach a resume.

At the discretion of the admissions committee, we award stipends as well as tuition grants without stipend. The stipend awards cover tuition waiver and a payment for living expenses for nine months of each year for five years, as long as satisfactory progress is made toward the Ph.D. The stipend for 2023-2024 is $3,333.33 per month, and it generally increases every year. Health insurance cost for the academic year is covered by the college for full-time students. Graduate students who receive support contribute to the program by teaching or grading undergraduate courses, assisting with advanced courses, and participating in research projects. Additional stipend is provided for the summer months if the student performs research or teaching assistance during that period.

There is no separate application for financial aid. Every applicant is considered for financial aid, unless you say that you do not need financial support. If you do not need support, please indicate this on the application form, and tell us how you expect to be supported.

Frequently Asked Questions

Answers to additional FAQs about grad admissions, including how to obtain a fee waiver, update an application, and waive TOEFL based on a degree from an English institution, are available at the grad school site .

Q. What are the institution and department codes for the GRE?

The GRE institution code is 3351, and the GRE department code is 0402.

Q. What are the institution and department codes for the TOEFL?

The TOEFL institution code is 3351, and the TOEFL department code is 78.

Q. If my GRE scores are low, but I have good grades, can I still get admission?

Our decision takes into account all components of your application. Therefore, it is usually impossible to predict the outcome until you apply and the admissions committee goes over your entire application.

Q. Can I be admitted for the Winter term?

No. We only admit one class each year, matriculating in the Fall term.

Q. Where should I have the transcripts and scores sent?

Send to: Guarini School of Graduate and Advanced Studies Dartmouth College Attn: Computer Science Graduate Admissions Anonymous Hall 64 College St, Suite 6062, Room 102 Hanover NH 03755 Phone: (603) 646-8193

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See also the MIT EECS Comm Lab’s advice on how to write a Graduate School Statement of Purpose .

Hal Abelson

I’m looking for leadership and initiative. My group has a large number of undergraduate students and I look to our grad students to be role models and leaders. Compelling application essays should talk about actual accomplishments: applications you’ve created that others are using, technical organizations you’ve started or where you play a major role. There of course needs to be a track record of academic excellence. But the centerpiece of my group is empowering people of all ages through technology, as users and creators. That passion should come through in the essay–and it’s even better if there’s a track record to point to.

Karl Berggren

An application essay provides a number of useful information points when I’m reading a candidate’s application. I’m mostly looking to see if the person can communicate clearly. Second, I’m trying to find out a bit about the person, especially their personality and motivation, and how they think about science and engineering. Finally, I’m interested in learning a bit about what circumstances have shaped the candidate’s life. This is the place where I want to know if someone has faced exceptional challenges or took advantage of a unique opportunity to do something special. Because of the importance of writing in an academic environment, I’m looking to see if there is structure to the essay, and if paragraphs are well formed. For me, the essay is really not usually the main compelling reason to admit a student, but an essay that is over the top, or is poorly written or poorly structured, sometimes puts me off a candidate who otherwise would be a possible admit.

Adam Chlipala

I think it’s useful to think of PhD applications as more like job applications than earlier educational applications. You are applying to be an apprentice researcher, and thus concrete research experience (attested to by knowledgeable supervisors in letters) is most useful to give evidence that you will succeed. Then the specifics vary by research area. I’m looking for specific technical skills and bigger-picture direction-setting skills. In my area, the former are a mix of software/hardware implementation at a decently large scale and comfort with rigorous math and logic. The latter come down to finding ways that computer systems are developed ineffectively today, and thinking up ways we could change the development tools–ideally applicants can point to cases where they were the ones driving that brainstorming, not just implementing ideas coming from supervisors. Giving some examples of project directions you want to explore is helpful both for showing that kind of initiative, and for helping potential advisors gauge fit with their own interests. However, don’t worry that anyone will hold you to working on any of the specific ideas you list!

Frédo Durand:

Mostly two things:

1/ Can the applicant think and write deeply and intelligently about state-of-the-art technical issues?

2/ What kind of technical area (very broadly speaking) are they interested in?

One challenge for applicants is that the two answers sometimes conflict: the first question requires narrowness, but you probably want to show you’re interested in a broader set of topics to maximize the number of faculty members who feel they may want to work with you. So, I usually advise students not to restrict their essay to their past research, but have a paragraph or two at the end or beginning to list the areas that appeal to them. Ideally, the letter would give me a sense of how they attack an open problem, through the example of past projects (ideally research projects).

Regarding recommendation letters, I want to read about a candidate’s potential to do research. The most insightful letters are from people who have supervised you on a research project, or on a project that involves a fair amount of independence and creativity. I find letters from people who only know a candidate in a class context less useful, unless the student has done a particularly remarkable project. Letters from mentors in industry can be useful, especially if they do or have done research. However, not all applicants (including successful ones) have three letters that are equally thorough, and it’s quite common to have 1 or 2 letters from people who really know you well from a research perspective, and 1 or 2 that are a little more superficial.

Polina Golland

The essay should focus on your interests and look to the future. Describe what problem you would like to tackle in the future and approaches you might want to take. Even if I disagree with what the applicants write, it is revealing on how they think and gets me interested. Keep the description of your (very impressive) past projects to the minimum, mention them only as support for what you want to do in the future. Your CV, other sections of the applications, and recommendation letters will talk about past projects, and it’s a pity to use your essay to rehash it again.

Piotr Indyk

For me, the most important aspect of the application is the evidence of research skills. In the ideal case, it would take the form of publications/manuscripts describing the research project(s) and results, which I can read and review. Descriptions of research projects in recommendation letters and/or the research statement are less optimal, but also OK. However, it is understandable that not all applicants have the same opportunities to pursue undergraduate research. In such cases, I try to infer from other parts of the applications, such as grades (esp. for relevant technical subjects), recommendation letters and other activities like olympiad participation. Regarding the research statement, I find it to be useful as a broad indicator of applicant research interests, but since interests of many (most?) applicants evolve, I do not put that much weight on it.

Daniel Jackson

When I think about taking on a graduate student, I ask myself: is this a good match? I want to be sure that we’ll enjoy each other’s company and be successful working together. That means you’ll be excited by the kind of work I do, and have a reason to think that working with me will be better for you than working with someone else. The students I tend to gel most with want to reconsider how we design and build software, and like to think deeply (and even a bit philosophically) about the fundamental problems. So I read the statement carefully, looking for someone who thinks clearly and creatively, knows a little bit about what I do, isn’t too distracted by technology or formalism for its own sake, and is eager to pursue big ideas. And if there’s some project you’ve done that shows some promise (especially if one of your letter writers can talk about it), so much the better.

Leslie Kaelbling

For me, the most important thing in an application is the best letter of recommendation, by a large margin. The main research letter should speak to the candidate’s creativity, independence, bravery, and ability to get things done. The other letters usually don’t matter much. I like unusual candidates, and am generally more interested in someone who has done something on their own, or in an unusual place, than someone with a lot of papers who spent four years in a very productive and prominent research group. Most essays are neither a positive nor a negative for the application. The ones I remember and value are ones that I learned something from–essays that are actually interesting to read because they have a strong or novel view or that articulate a clear vision. I also like to get the feeling that the candidate really values research intrinsically–that they are not simply applying to do a PhD because it seems like a good stepping stone to something else, or something that is highly esteemed by others. I don’t worry about a few poor grades, if they have an explanation: early in the student’s career, or during one rough semester, or as the result of exploration. I’d rather see a student with a few Bs or a C, who has taken challenging classes, than someone with a perfect GPA and completely standard undergraduate curriculum. I am completely unimpressed by a student who takes twice the normal course load—they should have been doing research!

David Karger

There are two questions that I ask when I’m looking at an application.

(1) Will this student be interested in working on the kinds of things that excite me?

And (2) do they have the independence and organization necessary to work for a laid back, disorganized advisor like me?

For the first question, I like to hear what specific problems interest you, and why. Not why you’ve loved computer science since you got your first PC at age 4, but why you consider certain specific problems important and interesting, and how you might go about trying to solve them. It certainly doesn’t hurt to have looked at some of the work my group is doing, talk about why it’s interesting to you (not just that it is), and maybe give some thoughts on where it might be interesting to take it further. But hearing your own ideas is wonderful too. You need not have solved them already, although it is great to talk about a problem you have already worked on.

The second question is specific to my advising style. I provide a lot of support and feedback to my students, but I don’t do a lot of management. So it’s important for me to know that a student will take initiative, make choices about what to work on, make decisions about designs and implementations, set their own deadlines and meet them, and come to meetings with ideas and questions to move the work forward. Just claiming this in your statement isn’t particularly meaningful, but I look for signs of it in past work (and recommendation letters).

Manolis Kellis

I’m looking to see several things:

(1) Clarity of thought: this comes through in the essay; the vision they have (for the field, how their works fits in, the broader perspective within, next steps, etc); the way they describe their accomplishments (organization, background, clarity of innovation, are they able to explain the problem, the challenge, the novelty, etc); and, of course, their grades and accomplishments.

(2) Research accomplishment: Show that they can innovate, invent, find problems, frame them, and bring things to completion, writing papers, completing projects, packaging up code, creating tools.

(3) Letters: Evidence of standing out, innovation, novelty, ability to make progress independently, yet team spirit and collaboration.

(4) Technical: Of course, their training, the rigor, the background, grades, competitions, etc.

(5) Passion: Especially for an applied field like genomics/biology/medicine, showing that they truly care about the application area, not just about the algorithms, but that they truly have sought to find something novel in the specific application area that they have chosen, and been able to interpret their results and make conclusions about the applied field.

Stefanie Mueller

For me, the most important is that the applicant can show that they have research experience in my research field. Hiring a PhD student is a 5-6 year commitment, so it is very important for me that the applicant can show me that we will produce exciting research together. When I read an application, I first check if the applicant has publications in my research field on topics related to what my research group works on. After this, I look at the recommendation letter writers and see if they come from faculty in my research field and if they talk about that the applicant can conduct research in my field. Letters from outside my research field are not very useful in determining if the applicant can do research in my area. After this, I read the statement of purpose to see if the applicant has ideas that I would also be excited to work on.

Will Oliver

When I read a graduate student applicant’s research statement, I look to obtain a picture of the student and their research interests. This includes the student’s motivations for research. Who is this person, where did they come from, what sparks their interest in science and engineering, how has that been reflected in their lives and their trajectory? I then look for examples of research experience, broadly defined. This could be an experience as an undergraduate researcher, a summer internship, or even a substantial hobby project (to name a few). I look for tangible outputs from those projects, such as a peer-reviewed publication. I then look for what the student wants to accomplish in graduate school. I appreciate a genuine exposition of intellectual curiosity and enthusiasm in describing these goals. While this approach naturally leads to some specificity in research topics, I also look for some flexibility and breadth. For example, even if the student has one top-choice topic or group, it is a good idea to articulate other (often related) areas that would also be of interest.

Al Oppenheim

A long time ago one of my graduate students asked me what I look for in choosing the students to work with. My quick and somewhat playful answer was that I have four criteria: intelligent, creative out of the box thinker, enjoyable to interact with, and coachable. From applications on paper and without personally meeting the applicant, it’s often hard to assess these and particularly the second, third, and fourth. An approximation to the first can be based somewhat on the transcript. The other three, perhaps mostly from the reference letters and personal statement and when possible personal interaction. The two areas in which applications are often weak are in the choice of references and the lack of detail in the reference letters, and in the crafting of the personal statement. In writing the personal statement I’ve typically advised potential applicants to use it as an opportunity to truly show their motivations, goals and personality, rather than trying to pattern match to what they think readers will give high marks to. If the personal statement is genuine and honest, it shows. And if it isn’t, it also shows.

Gerald Jay Sussman

The problem is that we have too many “excellent” applicants, most of whom would do fine in our graduate program. Most would do good, publishable, but incremental research. We accept plenty of those excellent people. But I am looking for the candidates that could break a paradigm and open up a new field of research.

So what I look for in an application is evidence that the candidate has an unusual perspective, perhaps in conflict with the conventional wisdom of the field. I am open to considering crackpots, but I also look for evidence of technical skill and clarity of thought and expression that separates the interesting characters from crackpots.

Additionally, the most persuasive information in an application is reference letters from previous supervisors or teachers who attest to the skill and creativity of the applicant.

George Verghese

Most of my reading of graduate folders necessarily happens at the initial stage of evaluations, when I’m looking for applicants who seem like they would thrive in, and contribute strongly to, our department or a broad research area within it. The application folders that emerge from this reading then get passed on to other faculty for more focused evaluation. So at the initial stage I am not necessarily looking for a good match to my personal research interests or style; that can come later, when I look at short-listed folders, perhaps sent my way by other faculty who think I might want to take a look.

For the initial reading, I first examine the applicant’s academic record, to be reassured that they will be able to handle at least the course work in our graduate program. A few blemishes in early years may be fine, but anything less than a strong overall academic record is likely to be a non-starter (though I will read quickly all the way through the application, feeling I owe at least that much to a hopeful applicant who has paid their application fee!).

I then look for tangible, documented outcomes of activities that go beyond standard academic efforts, whether unusual and independent projects (not standard lab projects in a class), or in research or internships. An important part of the substantiation is in the letters of reference, which have to reflect genuine, specific, modulated knowledge of the applicant and their work, and corresponding enthusiasm. A letter that sounds generic, though filled with superlatives and rating the candidate as Truly Exceptional, will not count for much. I want to know that the letter writers see stellar achievement (in academics and beyond) and potential, based on the specifics of their interaction with and knowledge of the applicant.

Finally, I turn to the student’s statement to get a sense of their voice, how they see and present themselves and their accomplishments, and what they’re looking to find in/with their graduate work. A well-crafted statement that comes across as mature, genuine, and reasonably aware of the field in which they hope to concentrate counts for a lot.

Ryan Williams

When reading a grad school application, I focus on several things. The first (obvious) thing is whether my interests align well with the applicant. This doesn’t necessarily mean that the applicant is working on the same exact problems as me; it means that I try to understand their taste in problems and topics, and how this fits with what we study in my research group. Another important thing is independence (in research, thought, etc): we get many applications from many talented students from all over the world, but we don’t see too many who showed a significant degree of independence in their thought and behavior, different from those around them (including their mentors). This can come out in the statement of purpose, but often more so in the letters. “Independence” can have various interpretations, and I’m deliberately leaving the term somewhat vague, because I think any of those interpretations can be important. Another important thing is the quality of their communication, especially their writing. I always read the statements of purpose carefully, as well as any writing samples the applicant has provided. Of course, letters of recommendation which attest to all of these qualities are also very helpful.

Ph.D. Admissions: How to Apply

Apply online.

All PhD application materials are submitted electronically through the online application portal and must be received by December 15th at 11:59pm, Pacific Time. We recommend leaving yourself enough time to completely navigate the submission process (e.g., 1 hour). There is only one admission cycle each year . Decisions come out in late February with students expected to enroll in the following autumn quarter.

Application Materials

The information below describes the materials required for your application to the Ph.D. Program in Computer Science. All materials are submitted electronically and prospective students are encouraged to review the program’s eligibility requirements for computing background carefully before applying.

The GRE is not required and any scores received will not be used for evaluation. There is no benefit to providing GRE scores during the application process as any scores that are received will not be referenced during application reviews.

Application checklist

• Applicant profile and program information

Academic history

• Research Interests and Faculty Advising

Recommendation letters

Resume or cv, personal statement, proof of english proficiency, application fee.

• Submit application
• Profile Information
• Research Interests and Faculty

Create your applicant profile and start your application

The application for graduate study at UW is hosted by the UW Graduate School. Create an application account and fill out your applicant profile. Complete the "Profile Information," "Contact Information," and "Ethnicity Information."

Official transcripts are not required during the application process; you will only have to submit official documents to the UW Graduate School if you accept an admission offer. At that time, you should provide your documents according to the Graduate School's official transcript requirements .

Research Interests & Faculty Advising

Applicants are given the opportunity to indicate up to 3 interest areas our research areas of expertise .

If you are confident that you'd like to work with any particular faculty member(s), you may indicate their name(s) in the supplemental question. This question is optional but is strongly encouraged to make sure your application is reviewed by the appropriate reviewers during the admissions process. For a list of faculty and a description of their research, see our faculty directory . You do not need to contact faculty prior to applying, nor is it expected.

When you designate someone as a recommender, the application system will automatically send them an email with a link to follow to upload their letter. The email will indicate the application deadline of your desired entry quarter and specify that letters need to be received by that date.

Upload a current copy of your CV that provides detailed descriptions of your research accomplishments and other technical skills. There are no requirements for length or formatting; you may be as descriptive as you would like and may utilize more than one page.

Submit a personal statement of that includes: a) how you became interested in doing research, b) a relevant project or research experience that shows your technical knowledge and skill, and c) your plans for the future in computer science. You may wish to include information about what you feel are the strengths of your application, such as special interests and abilities, or give explanations for what you feel are any weaknesses in your academic record. If you have background that might particularly contribute to the intellectual and social enrichment of the program, please describe it. Examples include unique educational or cultural opportunities (or lack of them), social and economic disadvantages that you may have had to overcome, and interesting or unusual influences on your intellectual development.

• Having earned a degree in the United States in which English was the language of instruction; or, having earned a degree in Canada, the United Kingdom, Australia, or certain other countries specified in Policy 3.2 and where English was the language of instruction.
• Documentation from your undergraduate degree-granting institution, if outside the US or one of the countries specified in Policy 3.2 , verifying that all instruction is in English (for example, transcript notation or attested document issued by the institution).
• TOEFL scores showing a minimum total score or MyBest score of 92 or higher. UW's ETS institution code is 4854 .
• Academic IELTS scores showing a total score of 7.0 or higher. Applicants using IELTS test scores must submit official scores electronically via the IELTS system (E-TRF), using the University of Washington’s organization ID 365 .

Application fee waivers are available from the UW Graduate School to some domestic students who demonstrate financial need. The PhD does not offer fee waivers at the program/department level.

Submit your application

Both steps - making payment and then submitting the application - must be completed in order for your application to be finalized and viewable to application reviewers .

After You Apply

Admitted students.

At any time, feel free to contact us at grad-advising [at] cs [dot] washington [dot] edu with questions.

CS PhD Course Guidelines

The following program guidelines (a.k.a model pogram) serve as a starting point for a discussion with the faculty about areas of interest.   This description of the Computer Science PhD course guidelines augments the school-wide  PhD course requirements .   Students should make themselves familiar with both.

Course Guidelines for Ph.D. Students in Computer Science

We expect students to obtain broad knowledge of computer science by taking graduate level courses in a variety of sub-areas in computer science, such as systems, networking, databases, algorithms, complexity, hardware, human-computer interaction, graphics, or programming languages.

Within our school, CS courses are roughly organized according to sub-area by their middle digit, so we expect students to take courses in a minimum of three distinct sub-areas, one of which should be theory (denoted by the middle digit of 2, or CS 231). Theory is specifically required as we expect all students to obtain some background in the mathematical foundations that underlie computer science. The intention is not only to give breadth to students, but to ensure cross-fertilization across different sub-disciplines in Computer Science.

Just as we expect all students obtaining a Ph.D. to have experience with the theoretical foundations of computer science, we expect all students to have some knowledge of how to build large software or hardware systems , on the order of thousands of lines of code, or the equivalent complexity in hardware. That experience may be evidenced by coursework or by a project submitted to the CHD for examination. In almost all cases a course numbered CS 26x or CS 24x will satisfy the requirement (exceptions will be noted in the course description on my.harvard). Students may also petition to use CS 161 for this requirement.   For projects in other courses, research projects, or projects done in internships the student is expected to write a note explaining the project, include a link to any relevant artifacts or outcomes, describe the student's individual contribution, and where appropriate obtain a note from their advisor, their class instructor, or their supervisors confirming their contributions.  The project must include learning about systems concepts, and not just writing many lines of code.   Students hoping to invoke the non-CS24x/26x/161 option must consult with  Prof. Mickens ,  Prof, Kung,  or  Prof. Idreos  well in advance of submitting their Program Plan to the CHD.  

Computer science is an applied science, with connections to many fields. Learning about and connecting computer science to other fields is a key part of an advanced education in computer science. These connections may introduce relevant background, or they may provide an outlet for developing new applications.

For example, mathematics courses may be appropriate for someone working in theory, linguistics courses may be appropriate for someone working in computational linguistics, economics courses may be appropriate for those working in algorithmic economics, electrical engineering courses may be appropriate for those working in circuit design, and design courses may be appropriate for someone working in user interfaces.

Requirements

The Graduate School of Arts & Sciences (GSAS) requires all Ph.D. students to complete 16 half-courses (“courses”, i.e., for 4 units of credit) to complete their degree. Of those 16 courses, a Ph.D. in Computer Science requires 10 letter-graded courses. (The remaining 6 courses are often 300-level research courses or other undergraduate or graduate coursework beyond the 10 required courses.)

The requirements for the 10 letter-graded courses are as follows:

• Of the 7 technical courses, at least 3 must be 200-level Computer Science courses, with 3 different middle digits (from the set 2,3,4,5,6,7,8), and with one of these three courses either having a middle digit of 2 or being CS 231 (i.e., a “theory” course).   Note that CS courses with a middle digit of 0 are valid technical courses, but do not contribute to the breadth requirement.
• At least 5 of the 8 disciplinary courses must be SEAS or SEAS-equivalent 200-level courses. A “SEAS equivalent” course is a course taught by a SEAS faculty member in another FAS department. 
• For any MIT course taken, the student must provide justification why the MIT course is necessary (i.e. SEAS does not offer the topic, the SEAS course has not been offered in recent years, etc.). MIT courses do not count as part of the 5 200-level SEAS/SEAS-equivalent courses. 
• 2 of the 10 courses must constitute an external minor (referred to as "breadth" courses in the SEAS “ Policies of the Committee on Higher Degrees [CHD] ”) in an area outside of computer science. These courses should be clearly related; generally, this will mean the two courses are in the same discipline, although this is not mandatory. These courses must be distinct from the 8 disciplinary courses referenced above.
• Students must demonstrate practical competence by building a large software or hardware system during the course of their graduate studies. This requirement will generally be met through a class project, but it can also be met through work done in the course of a summer internship, or in the course of research.
• In particular, for Computer Science graduate degrees, Applied Computation courses may be counted as 100-level courses, not 200-level courses.
• Up to 2 of the 10 courses can be 299r courses, but only 1 of the up to 2 allowed 299r courses can count toward the 8 disciplinary courses. 299r courses do not count toward the 5 200-level SEAS/SEAS-equivalent courses. If two 299r’s are taken, they can be with the same faculty but the topics must be sufficiently different.
• A maximum of 3 graduate-level transfer classes are allowed to count towards the 10 course requirement.
• All CS Ph.D. program plans must adhere to the SEAS-wide Ph.D. requirements, which are stated in the SEAS Policies of the Committee on Higher Degrees (CHD) . These SEAS-wide requirements are included in the items listed above, though students are encouraged to read the CHD document if there are questions, as the CHD document provides further explanation/detail on several of the items above.
• All program plans must be approved by the CHD. Exceptions to any of these requirements require a detailed written explanation of the reasoning for the exception from the student and the student’s research advisor. Exceptions can only be approved by the CHD, and generally exceptions will only be given for unusual circumstances specific to the student’s research program.

Requirement Notes

• Courses below the 100-level are not suitable for graduate credit.
• For students who were required to take it, CS 2091/2092 (formerly CS 290a/b or 290hfa/hfb may be included as one of the 10 courses but it does not count toward the 200-level CS or SEAS/SEAS-equivalent course requirements nor toward the SM en route to the PhD.

Your program plan  must always comply  with both our school's General Requirements, in addition to complying with the specific requirements for Computer Science. All program plans must be approved by the Committee on Higher Degrees [CHD]. Exceptions to the requirements can only be approved by the CHD, and generally will only be given for unusual circumstances specific to the student’s research program

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Statement of Purpose

A statement of purpose describes your study interests, goals, and program fit

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Your statement of purpose should be a typewritten, double-spaced, well-organized statement explaining why you wish to pursue graduate study through your program of interest at NYU. This is your opportunity to introduce yourself and to inform the department about your goals, interests, and career plans as they relate to your intended academic pursuits. Please make sure to include your name as it appears on your application, the program you are applying to, and the date. This document can be uploaded directly to your online application portal.

For those applying as a Cyber Fellow: Applicants to the NYU Cybersecurity M.S. program who are also applying to be considered for the NYU Cyber Fellow scholarship must submit a well-organized statement of purpose that is a maximum of 250 words . 

For all other applicants: Applicants to any other program or applicants to the Cybersecurity M.S. who are not interested in being considered for the Cyber Fellows scholarship must submit a well-organized statement of purpose that is a maximum of one- to two-pages .

How to write a strong Statement Of Purpose

Your statement of purpose should assure readers—primarily the faculty on the selection committee—that your background and experience will support your success in graduate study. Think of the statement of purpose as a composition with five key parts:

Share your interests — what sparked your desire for graduate study? This should be short and to the point; you don’t need to spend a great deal of time on your autobiography. You can elaborate on your areas of academic interest later in the statement.

Include details such as:

• Research you have conducted. Indicate with whom, the title of the project, what your responsibilities were, and the outcome. The graduate admissions committee is composed of faculty so write technically, or in the style of your discipline.
• Important papers or thesis projects you’ve completed, as well as related extracurricular activities.
• Awards or recognitions you’ve received for the scholarly achievements discussed.
• Related internship experience, especially if you’ve had any responsibility for testing, designing, researching or interning in an area similar to what you wish to study in graduate school.

If you have ongoing projects or work experience, indicate the scope of that work. Whether for a company, non-profit, design team, etc, include your responsibilities, what you learned, etc. You can also indicate how this will help you focus your graduate studies. Cyber Fellows applicants: You can skip this portion for brevity!

Use this part of your statement to indicate what you would like to study in graduate school in enough detail to show the graduate admissions committee that you understand the scope of research in a specified discipline. This can include engagement with current research themes, and/or reasons why this specific program would be a good fit for you. Indicate your area(s) of interest. Ideally, pose a question, define a problem, or indicate a theme that you would like to address and questions that arise from contemporary research that you would like to investigate. This is a key paragraph!

End your statement in a positive manner, indicating your excitement and readiness for the challenges ahead.

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The PhD is designed to prepare students for academic careers and careers in government and industry research labs. Computer science is a vigorous and exciting field of research and study that continues to grow in importance.

Departmental research strengths include:

• Artificial Intelligence (machine learning, multiagent systems, planning and problem solving),
• Bioinformatics,
• Computational Theory (computational learning theory, design and analysis of algorithms, computability theory),
• Compiler Optimization and Compilation for Parallel Machines,
• Natural-Language Processing, (discourse and dialogue, generation, information extraction, summarization),
• Systems (parallel and distributed computing, grid and volunteer computing, algorithm and architecture design for massive parallelism),
• Networks (distributed computing, transport layer protocols, mobile and wireless networks, algorithm and architecture design for massive parallelism, networks management, security performance modeling, simulation),
• Graphics and Computer Vision,
• Rehabilitation Engineering (augmentative communication, speech recognition and enhancement),
• Software Engineering (program analysis and testing),
• Symbolic Mathematical Computation (algebraic algorithms, parallelization), and

The CIS graduate program provides a solid foundation in the fundamental areas of computer science and provides numerous advanced courses and seminars to acquaint the student with current computer science research.

Degree Requirements

In addition to satisfying the general requirements of the University, candidates for the Doctor of Philosophy degree must satisfy several departmental requirements. One objective of these requirements is to provide flexibility in designing an appropriate plan of study. The PhD is an individualistic degree. As soon as possible in the program, each candidate should find a faculty member to act as adviser and be in charge of the candidate’s research.

The candidate and advisor design a plan of study that satisfies the University and Department requirements. The Department requirements as listed below specify a minimum amount of necessary work. It is expected that additional course work will normally be required by the adviser. A minimum set of requirements provides a large degree of flexibility for each individual candidate.

A. Departmental General Requirements

• A minimum grade average of 3.0 is required in the graduate courses used to satisfy the degree requirements. The University also requires a minimum GPA of 3.0 in all graduate courses taken including any not used towards the degree requirements. Students are encouraged to explore graduate courses (600 level or higher) in other areas such as electrical engineering, mathematics, linguistics, statistics, and business and economics. Graduate courses outside of Computer and Information Sciences to be used towards meeting degree requirements require written approval of the Graduate Committee.
• Each semester all graduate students must explicitly register for CISC 890 – Colloquium and sign up and satisfactorily participate in one of the Department’s special research interest groups. One faculty member for each group will be responsible for overseeing satisfactory participation for each student on an individual basis (e.g., simply attending, giving a presentation) and will assign a pass/fail grade accordingly.

The Department requires the following:

• Each candidate must complete all requirements of a University of Delaware MS degree in Computer and Information Sciences. A candidate with a master’s degree in a related field (e.g., EE, Math) must put together a program that meets the CIS Graduate Committee’s approval. Using courses taken for the related graduate degree plus courses taken at Delaware, the candidate must satisfy the Computer Science course requirements for the MS degree, and show the equivalent of the 30 credit MS degree offered by the CIS Department.
• Each candidate is required to complete a minimum of 6 additional credits beyond the master’s degree. At least 3 of the 6 additional credits must be in 800-level CISC courses. The 6 additional credits do not include the following courses: CISC 666, CISC 866, CISC 868, CISC 969. Normally, in meeting the University’s requirement for a major area, a candidate will be required by the adviser to complete more than 6 credits. (Note that the University requires a candidate to complete 9 credits of CISC969 after admission to candidacy.)
• Research Ability . PhD candidates are strongly encouraged to get involved in research as early as possible in their program. As part of the process of finding an adviser, and as early as possible, candidates must demonstrate the potential to perform research. Demonstration may be in the form of independent study ( CISC 666 , CISC 866 ), research ( CISC 868 ), working as a research assistant, or writing an MS thesis.
• Preliminary Requirements . These requirements ensure that each Ph.D. candidate (1) has significant breadth of knowledge in core areas of computer science, and (2) has demonstrated the ability to perform research in a specific computer science area. The breadth requirement is met by taking 5 breadth courses, which may include the 4 breadth courses from the breadth requirement of the MS degree, and obtaining a minimum 3.5 GPA on these breadth courses. See Prelim Course Selection Process for detail. The research requirement is met by working with a committee of 2 CIS faculty members on a research project, culminating in a written report and presentation/oral exam. A pass or fail decision for the preliminary exam will be made by the faculty in a faculty meeting that will take place after the end of each semester. Candidates must fulfill the Preliminary Requirements within 2 years, counted from the date the student enters the graduate program. Candidates may request an extension in exceptional circumstances (such as serious illness or injury) subject to approval by the Faculty. The student will be dismissed from the Ph.D. program if the Preliminary Requirements are not satisfied within the allowed time period. ( further information )
• Advisory Committee . Each candidate, with the advice of the PhD advisor, needs to establish an advisory committee (usually following the successful completion of the preliminary exam). In accordance with the University requirements, the committee consists of 4-6 members nominated and approved by the CIS Department faculty. The committee chair is the candidate’s PhD advisor in charge of the candidate’s research and dissertation and must be a member of the CIS faculty. The candidate may have a co- advisor who must be a UD faculty, possibly from another department. A co-advisor is a member of the advisory committee. At least two members represent the area of proposed research. The committee must also include at least one member of the CIS faculty working outside the main area of the proposed research. At least one member must be from outside the CIS Department. The proposed advisory committee must be submitted to the Graduate Committee for approval. It must then be approved by the CIS faculty. In the above, CIS faculty means tenure-track faculty whose primary appointment is in the CIS Department or who have a joint appointment in CIS, but not including continuing track faculty, research faculty, affiliated faculty, visiting faculty, secondary faculty, or adjunct faculty.
• Qualifying Examination . Each candidate must pass a qualifying exam. The advisory committee prepares an examination (oral and/or written) testing a candidate’s knowledge in the area of proposed research. Part of the examination includes an oral presentation of a candidate’s proposed dissertation research. A student passes the qualifying exam as long as there is no more than one negative vote. Prior to taking the qualifying exam, candidates must submit a dissertation proposal and a written plan describing their background and research interests. The proposal and plan are submitted to the advisory committee and are considered as input to the qualifying examination. Copies of “Discussion on PhD Thesis Proposals in Computing Science” are available in the CIS Department Office. The qualifying exam is normally taken one year after passing the preliminary exam. During this year a student should actively investigate research possibilities and select a dissertation topic.
• Dissertation . Each candidate must complete a dissertation demonstrating results of original and significant research written in a scholarly and competent manner worthy of publication. Upon completion of the dissertation, a final oral public examination must be passed, consisting of a defense of the dissertation and a test of the mastery of a candidate’s research area. The final oral examination is directed and evaluated by the student’s advisory committee.
• Facility of Expression in English . As part of satisfying the University’s requirement that PhD graduates demonstrate an ability to orally express themselves clearly and forcefully, each candidate must present his or her research results in a departmental colloquium, or one of the Department’s special research interest groups within six months of the defense.
• Foreign Language . There is no foreign language requirement.

Graduate Recruitment Contacts

Li Liao Email: cis [email protected] Phone : 302-831-2783

Chiamesha Carey Graduate Academic Advisor II Email: [email protected] Phone : 302-831-4467

UD Graduate Admissions Email : [email protected] Phone : 302-831-2129

What are your chances of acceptance?

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How to Write the “Why Computer Science?” Essay

What’s covered:, what is the purpose of the “why computer science” essay, elements of a good computer science essay, computer science essay example, where to get your essay edited.

You will encounter many essay prompts as you start applying to schools, but if you are intent on majoring in computer science or a related field, you will come across the “ Why Computer Science? ” essay archetype. It’s important that you know the importance behind this prompt and what constitutes a good response in order to make your essay stand out.

For more information on writing essays, check out CollegeVine’s extensive essay guides that include everything from general tips, to essay examples, to essay breakdowns that will help you write the essays for over 100 schools.

Colleges ask you to write a “ Why Computer Science? ” essay so you may communicate your passion for computer science, and demonstrate how it aligns with your personal and professional goals. Admissions committees want to see that you have a deep interest and commitment to the field, and that you have a vision for how a degree in computer science will propel your future aspirations.

The essay provides an opportunity to distinguish yourself from other applicants. It’s your chance to showcase your understanding of the discipline, your experiences that sparked or deepened your interest in the field, and your ambitions for future study and career. You can detail how a computer science degree will equip you with the skills and knowledge you need to make a meaningful contribution in this rapidly evolving field.

A well-crafted “ Why Computer Science? ” essay not only convinces the admissions committee of your enthusiasm and commitment to computer science, but also provides a glimpse of your ability to think critically, solve problems, and communicate effectively—essential skills for a  computer scientist.

The essay also gives you an opportunity to demonstrate your understanding of the specific computer science program at the college or university you are applying to. You can discuss how the program’s resources, faculty, curriculum, and culture align with your academic interests and career goals. A strong “ Why Computer Science? ” essay shows that you have done your research, and that you are applying to the program not just because you want to study computer science, but because you believe that this particular program is the best fit for you.

Writing an effective “ Why Computer Science ?” essay often requires a blend of two popular college essay archetypes: “ Why This Major? ” and “ Why This College? “.

Explain “Why This Major?”

The “ Why This Major? ” essay is an opportunity for you to dig deep into your motivations and passions for studying Computer Science. It’s about sharing your ‘origin story’ of how your interest in Computer Science took root and blossomed. This part of your essay could recount an early experience with coding, a compelling Computer Science class you took, or a personal project that sparked your fascination.

What was the journey that led you to this major? Was it a particular incident, or did your interest evolve over time? Did you participate in related activities, like coding clubs, online courses, hackathons, or internships?

Importantly, this essay should also shed light on your future aspirations. How does your interest in Computer Science connect to your career goals? What kind of problems do you hope to solve with your degree?

The key for a strong “ Why This Major? ” essay is to make the reader understand your connection to the subject. This is done through explaining your fascination and love for computer science. What emotions do you feel when you are coding? How does it make you feel when you figure out the solution after hours of trying? What aspects of your personality shine when you are coding? 

By addressing these questions, you can effectively demonstrate a deep, personal, and genuine connection with the major.

Emphasize “Why This College?”

The “ Why This College? ” component of the essay demonstrates your understanding of the specific university and its Computer Science program. This is where you show that you’ve done your homework about the college, and you know what resources it has to support your academic journey.

What unique opportunities does the university offer for Computer Science students? Are there particular courses, professors, research opportunities, or clubs that align with your interests? Perhaps there’s a study abroad program or an industry partnership that could give you a unique learning experience. Maybe the university has a particular teaching methodology that resonates with you.

Also, think about the larger university community. What aspects of the campus culture, community, location, or extracurricular opportunities enhance your interest in this college? Remember, this is not about general praises but about specific features that align with your goals. How will these resources and opportunities help you explore your interests further and achieve your career goals? How does the university’s vision and mission resonate with your own values and career aspirations?

It’s important when discussing the school’s resources that you always draw a connection between the opportunity and yourself. For example, don’t tell us you want to work with X professor because of their work pioneering regenerative AI. Go a step further and say because of your goal to develop AI surgeons for remote communities, learning how to strengthen AI feedback loops from X professor would bring you one step closer to achieving your dream.

By articulating your thoughts on these aspects, you demonstrate a strong alignment between the college and your academic goals, enhancing your appeal as a prospective student.

Demonstrate a Deep Understanding of Computer Science

As with a traditional “ Why This Major? ” essay, you must exhibit a deep and clear understanding of computer science. Discuss specific areas within the field that pique your interest and why. This could range from artificial intelligence to software development, or from data science to cybersecurity. 

What’s important is to not just boast and say “ I have a strong grasp on cybersecurity ”, but instead use your knowledge to show your readers your passion: “ After being bombarded with cyber attack after cyber attack, I explained to my grandparents the concept of end-to-end encryption and how phishing was not the same as a peaceful afternoon on a lake. ”

Make it Fun!

Students make the mistake of thinking their college essays have to be serious and hyper-professional. While you don’t want to be throwing around slang and want to present yourself in a positive light, you shouldn’t feel like you’re not allowed to have fun with your essay. Let your personality shine and crack a few jokes.

You can, and should, also get creative with your essay. A great way to do this in a computer science essay is to incorporate lines of code or write the essay like you are writing out code. 

Now we will go over a real “ Why Computer Science? ” essay a student submitted and explore what the essay did well, and where there is room for improvement.

Please note: Looking at examples of real essays students have submitted to colleges can be very beneficial to get inspiration for your essays. You should never copy or plagiarize from these examples when writing your own essays. Colleges can tell when an essay isn’t genuine and will not view students favorably if they plagiarized.

I held my breath and hit RUN. Yes! A plump white cat jumped out and began to catch the falling pizzas. Although my Fat Cat project seems simple now, it was the beginning of an enthusiastic passion for computer science. Four years and thousands of hours of programming later, that passion has grown into an intense desire to explore how computer science can serve society. Every day, surrounded by technology that can recognize my face and recommend scarily-specific ads, I’m reminded of Uncle Ben’s advice to a young Spiderman: “with great power comes great responsibility”. Likewise, the need to ensure digital equality has skyrocketed with AI’s far-reaching presence in society; and I believe that digital fairness starts with equality in education.

The unique use of threads at the College of Computing perfectly matches my interests in AI and its potential use in education; the path of combined threads on Intelligence and People gives me the rare opportunity to delve deep into both areas. I’m particularly intrigued by the rich sets of both knowledge-based and data-driven intelligence courses, as I believe AI should not only show correlation of events, but also provide insight for why they occur.

In my four years as an enthusiastic online English tutor, I’ve worked hard to help students overcome both financial and technological obstacles in hopes of bringing quality education to people from diverse backgrounds. For this reason, I’m extremely excited by the many courses in the People thread that focus on education and human-centered technology. I’d love to explore how to integrate AI technology into the teaching process to make education more available, affordable, and effective for people everywhere. And with the innumerable opportunities that Georgia Tech has to offer, I know that I will be able to go further here than anywhere else.

What the Essay Did Well 

This essay perfectly accomplishes the two key parts of a “ Why Computer Science? ” essay: answering “ Why This Major? ” and “ Why This College? ”. Not to mention, we get a lot of insight into this student and what they care about beyond computer science, and a fun hook at the beginning.

Starting with the “ Why This Major? ” aspect of the response, this essay demonstrates what got the student into computer science, why they are passionate about the subject, and what their goals are. They show us their introduction to the world of CS with an engaging hook: “I held my breath and hit RUN. Yes! A plump white cat jumped out and began to catch the falling pizzas. ” We then see this is a core passion because they spent “ Four years and thousands of hours ,” coding.

The student shows us why they care about AI with the sentence, “ Every day, surrounded by technology that can recognize my face and recommend scarily-specific ads ,” which makes the topic personal by demonstrating their fear at AI’s capabilities. But, rather than let panic overwhelm them, the student calls upon Spiderman and tells us their goal of establishing digital equality through education. This provides a great basis for the rest of the essay, as it thoroughly explains the students motivations and goals, and demonstrates their appreciation for interdisciplinary topics.

Then, the essay shifts into answering “ Why This College? ”, which it does very well by honing in on a unique facet of Georgia Tech’s College of Computing: threads. This is a great example of how to provide depth to the school resources you mention. The student describes the two threads and not only why the combination is important to them, but how their previous experiences (i.e. online English tutor) correlate to the values of the thread: “ For this reason, I’m extremely excited by the many courses in the People thread that focus on education and human-centered technology. ”

What Could Be Improved

This essay does a good job covering the basics of the prompt, but it could be elevated with more nuance and detail. The biggest thing missing from this essay is a strong core to tie everything together. What do we mean by that? We want to see a common theme, anecdote, or motivation that is weaved throughout the entire essay to connect everything. Take the Spiderman quote for example. If this was expanded, it could have been the perfect core for this essay.

Underlying this student’s interest in AI is a passion for social justice, so they could have used the quote about power and responsibility to talk about existing injustices with AI and how once they have the power to create AI they will act responsibly and help affected communities. They are clearly passionate about equality of education, but there is a disconnect between education and AI that comes from a lack of detail. To strengthen the core of the essay, this student needs to include real-world examples of how AI is fostering inequities in education. This takes their essay from theoretical to practical.

Whether you’re a seasoned writer or a novice trying your hand at college application essays, the review and editing process is crucial. A fresh set of eyes can provide valuable insights into the clarity, coherence, and impact of your writing. Our free Peer Essay Review tool offers a unique platform to get your essay reviewed by another student. Peer reviews can often uncover gaps, provide new insights or enhance the clarity of your essay, making your arguments more compelling. The best part? You can return the favor by reviewing other students’ essays, which is a great way to hone your own writing and critical thinking skills.

For a more professional touch, consider getting your essay reviewed by a college admissions expert . CollegeVine advisors have years of experience helping students refine their writing and successfully apply to top-tier schools. They can provide specific advice on how to showcase your strengths, address any weaknesses, and generally present yourself in the best possible light.

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Doctoral (PhD)

T he objective of the PhD program is to help students develop research proficiency in conceptualizing and implementing computer models and tools that address societal needs. This proficiency will enable students to analyze and review critically the scientific work in their area of interest and in the broader field. Moving through the program students will demonstrate original thought by expanding the boundaries of their field, communicating their findings in a convincing and affirming manner.  

Admission will be based on strength of applicant, evidenced in a portfolio comprising an essay in context with Loyola’s values and mission, samples of work in the field, recommendation letters, and academic performance in prior degree programs. Admission requirements will be in line with Grad School guidelines, but in general we will consider waiving the GRE if the applicant falls in any of these categories:   

• Graduate of our BS in CS with a GPA of 3.5 or greater
• Graduate of any MS in CS or closely related degrees offered by the department or a peer/aspirational program with a GPA of 3.5 or higher
• Earned a BS in CS or closely related degrees from a peer program, with a GPA of 3.5 or higher
• Earned most recent degree over 5 years ago and has demonstrated a significant record of accomplishment (and progression) as a professional in the field of computing
• Research experience such as peer-reviewed publication, REU participation, or equivalent  

The doctoral program will comprise 60 credits equally divided between waivable coursework and dissertation research. The program will comprise courses at the 400 level (Masters courses including COMP 488 Advanced Topics) and 500 level (directed study, doctoral research, dissertation supervision, and doctoral study). The 500-level courses will be under the supervision of qualified faculty. These courses will focus on the development and defense of a dissertation. Prior to registering for these course, students must complete a Qualifying Event (QE). The QE is a comprehensive assessment of an individual’s preparation for completing the dissertation work.   

A minimum of 60 credits will be required. Up to 30 credits may be waived for qualified students, subject to CAS and Graduate School policies. This waiver does not relieve students from the courses they must take to establish a successful Qualifying Event.  

Areas covered by COMP 488 Advanced Topics in Computer Science  

The topics in this course will be determined by the student’s advisor. Areas to consider include Instruction, Learning, and Curriculum Design, Mathematical and Statistical Methods for Computing, Research Methods, Algorithms and Complexity, Computer Systems, Software Engineering, Machine Learning, Natural Language Processing, Programming Languages and Compilers, Cybersecurity, Internet of Things, Architecture, Data Science, and other areas of interest to faculty members.   

Dissertation development courses 

As students complete their coursework and fulfill their qualifying event, they may enroll in COMP 600 Thesis Supervision. This is a highly directed course under the supervision of the student’s dissertation advisor. Students are expected to continue enrolling in this course as they progress with their dissertation work, going through the following stages:  

• Review open problems in computing and identify ones  
• Work with library resources and prepare a literature review  
• Draft a dissertation proposal
• Develop research methodology  
• Refine research methodology based on committee feedback  
• Draft an outline of thesis  
• Development of thesis and research studies/prototypes  
• Focus on findings  
• Review of results  
• Final draft  
• Defense  

Typical curriculum plan and Qualifying Event

The figure below shows the typical curriculum plan for a Ph.D. in the proposed program. Students are required to take 21 credits of MS-level coursework. This amounts to 7 courses, four of which will be at the Advanced Topics level available only to graduate students. Early on, PhD students may take MS-level courses that are open to select undergraduate students as well.   

Students that meet Graduate School and departmental requirements, may transfer up to 21 credits of coursework from a previous MS program. This transfer will shorten their path to the PhD and will place them closer to the next stage of the curriculum plan.

The next stage of the plan includes coursework to establish qualifications for doctoral research. This stage is akin to a qualifying exam and no transfer credit can be accepted.  The qualifying process spreads over courses that cover any three of the four pillar areas of computer science (theory, systems, software, and AI). Courses are selected from the advanced 400-level courses including COMP 488 Advanced Topics in CS. A grade of A is required in each of these three courses covering three areas, to establish a Qualifying Event.

After successful completion of the Qualifying Event, students may begin registering for 500-level courses that focus on their chosen research direction and the successful development and defense of a PhD thesis. No credit transfers can be accepted here.  

Sample Courses for the PhD program  

The following courses, currently offered by the department of computer science, can help prepare prospective PhD students for their research work and establish a Qualifying Event:

• Current MIT Graduate Students

Doctoral Programs in Computational Science and Engineering

Application & admission information.

The Center for Computational Science and Engineering (CCSE) offers two doctoral programs in computational science and engineering (CSE) – one leading to a standalone PhD degree in CSE offered entirely by CCSE (CSE PhD) and the other leading to an interdisciplinary PhD degree offered jointly with participating departments in the School of Engineering and the School of Science (Dept-CSE PhD).

While both programs enable students to specialize at the doctoral level in a computation-related field via focused coursework and a thesis, they differ in essential ways. The standalone CSE PhD program is intended for students who plan to pursue research in cross-cutting methodological aspects of computational science. The resulting doctoral degree in Computational Science and Engineering is awarded by CCSE via the the Schwarzman College of Computing. In contrast, the interdisciplinary Dept-CSE PhD program is intended for students who are interested in computation in the context of a specific engineering or science discipline. For this reason, this degree is offered jointly with participating departments across the Institute; the interdisciplinary degree is awarded in a specially crafted thesis field that recognizes the student’s specialization in computation within the chosen engineering or science discipline.

Applicants to the standalone CSE PhD program are expected to have an undergraduate degree in CSE, applied mathematics, or another field that prepares them for an advanced degree in CSE. Applicants to the Dept-CSE PhD program should have an undergraduate degree in a related core disciplinary area as well as a strong foundation in applied mathematics, physics, or related fields. When completing the MIT CSE graduate application , students are expected to declare which of the two programs they are interested in. Admissions decisions will take into account these declared interests, along with each applicant’s academic background, preparation, and fit to the program they have selected.  All applicants are asked to specify MIT CCSE-affiliated faculty that best match their research interests; applicants to the Dept-CSE PhD program also select the home department(s) that best match. At the discretion of the admissions committee, Dept-CSE PhD applications might also be shared with a home department beyond those designated in the application. CSE PhD admissions decisions are at the sole discretion of CCSE; Dept-CSE PhD admission decisions are conducted jointly between CCSE and the home departments.

Please note: These are both doctoral programs in Computational Science and Engineering; applicants interested in Computer Science must apply to the Department of Electrical Engineering and Computer Science .

Important Dates

September 15: Application Opens December 1: Deadline to apply for admission* December – March: Application review period January – March: Decisions released on rolling basis

*All supplemental materials (e.g., transcripts, test scores, letters of recommendation) must also be received by December 1. Application review begins on that date, and incomplete applications may not be reviewed. Please be sure that your recommenders are aware of this hard deadline, as we do not make exceptions. We also do not allow students to upload/submit material beyond what is required, such as degree certificates, extra recommendations, publications, etc.

A complete electronic CSE application includes the following:

• Three letters of recommendation ;
• Students admitted to the program will be required to supply official transcripts. Discrepancies between unofficial and official transcripts may result in the revocation of the admission offer.
• Statement of objectives (limited to approximately one page) and responses to department-specific prompts for Dept-CSE PhD applicants;
• Official GRE General Test score report , sent to MIT by ETS via institute code 3514 GRE REQUIREMENT WAIVED FOR FALL 2024 ;
• Official IELTS score report sent to MIT by IELTS†  (international applicants from non-English speaking countries only; see below for more information)
• Resume or CV , uploaded in PDF format;
• MIT graduate application fee of $75‡.

‡Application Fee

The MIT graduate application fee of $75.00 is a mandatory requirement set by the Institute payable by credit card. Please visit the MIT Graduate Admission Application Fee Waiver page for information about fee waiver eligibility and instructions.

Please note: CCSE cannot issue fee waivers; email requests for fee waivers sent to [email protected] will not be considered.

Admissions Contact Information

Email: [email protected]

► Current MIT CSE SM Students: Please see the page for Current MIT Graduate Students .

GRE Requirement

GRE REQUIREMENT WAIVED FOR FALL 2024 All applicants are required to take the Graduate Record Examination (GRE) General Aptitude Test. The MIT code for submitting GRE score reports is 3514 (you do not need to list a department code). GRE scores must current; ETS considers scores valid for five years after the testing year in which you tested.

†English Language Proficiency Requirement

The CSE PhD program requires international applicants from non-English speaking countries to take the academic  version of the International English Language Testing System (IELTS).  The IELTS exam measures one’s ability to communicate in English in four major skill areas: listening, reading, writing, and speaking.  A minimum IELTS score of 7 is required for admission.  For more information about the IELTS, and to find out where and how to take the exam, please visit the IELTS web site .

While we will also accept the TOEFL iBT (Test of English as a Foreign Language), we strongly prefer the IELTS. The minimum TOEFL iBT score is 100.

This requirement is waived for those who can demonstrate that one or more of the following are true:

• English is/was the language of instruction in your four-year undergraduate program,
• English is the language of your employer/workplace for at least the last four years,
• English was your language of instruction in both primary and secondary schools.

Degree Requirements for Admission

To be admitted as a regular graduate student, an applicant must have earned a bachelor’s degree or its equivalent from a college, university, or technical school of acceptable standing. Students in their final year of undergraduate study may be admitted on the condition that their bachelor’s degree is awarded before they enroll at MIT.

Applicants without an SM degree may apply to the CSE PhD program, however, the Departments of Aeronautics and Astronautics and Mechanical Engineering nominally require the completion of an SM degree before a student is considered a doctoral candidate. As a result, applicants to those departments holding only a bachelor’s degree are asked in the application to indicate whether they prefer to complete the CSE SM program or an SM through the home department.

Nondiscrimination Policy

The Massachusetts Institute of Technology is committed to the principle of equal opportunity in education and employment.  To read MIT’s most up-to-date nondiscrimination policy, please visit the Reference Publication Office’s nondiscrimination statement page .

Additional Information

For more details, as well as answers to most commonly asked questions regarding the admissions process to individual participating Dept-CSE PhD departments including details on financial support, applicants are referred to the website of the participating department of interest.

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• Statement of Purpose, Personal History, Diversity

Found a good Computer Science example SoP. (Accepted to MIT, Princeton, Washington)

By zep December 1, 2011 in Statement of Purpose, Personal History, Diversity

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I found what I think is a good example computer science PhD Statement of Purpose. Using this Statement of Purpose , Mihai Patrascu was accepted to MIT, Princeton, and U Washington. I hope this is helpful to others who are applying to CS PhD programs.

I think this SoP is "pretty good." It uses good grammar to tell a coherent story. The author's admission to some really good schools suggests that this is a good SoP. However, I have a few questions about this SoP...

1. This SoP begins by discussing the Romanian National Olympiad. Winning this competition several times is very impressive, but I wonder if it would be better to begin the essay with 2-3 sentences about research goals. My rationale is that, if the introduction doesn't discuss research, the reviewer might not finish reading the SoP. What do other people think?

2. I've been fortunate to have had the opportunity meet with with a department head at Stanford. She told me that, despite the typical 2 to 3 page SoP space limitation, I should make sure to limit my SoP to one page. The rationale is that reviewers are busy, and they probably won't read the SoP carefully if it's very long. Does Mihai Patrascu's SoP strike other people as being too long?

3. This SoP talks a lot about past and current research, which is good. However, no potential graduate advisors or research groups are discussed. Is this a problem?

Also, if anyone has found successful (e.g. admitted) computer science statements of purpose, feel free to share them here!

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Am I the only person who doesn't like this SoP? I find it very impersonal and uninteresting. All I know about Mihai from reading this is that he was an exceptional researcher and earned many awards. I could have found that out by looking at his C.V. I assume...

Good to know. How would you make it better? Would you add more about the motivation for pursuing research and why the research is enjoyable?

I mean, I think he got into all of those schools based on his excellent research experience. If I were on the adcom for those schools, I would have like to been able to get to know him on a personal level through his SoP. I feel like it's difficult to get an idea of what his personality is like and what it would be like to work with him through his SoP.

• 1 year later...

aakritisrikanth

I found what I think is a good example computer science PhD Statement of Purpose. Using this Statement of Purpose , Mihai Patrascu was accepted to MIT, Princeton, and U Washington. I hope this is helpful to others who are applying to CS PhD programs. I think this SoP is "pretty good." It uses good grammar to tell a coherent story. The author's admission to some really good schools suggests that this is a good SoP. However, I have a few questions about this SoP... 1. This SoP begins by discussing the Romanian National Olympiad. Winning this competition several times is very impressive, but I wonder if it would be better to begin the essay with 2-3 sentences about research goals. My rationale is that, if the introduction doesn't discuss research, the reviewer might not finish reading the SoP. What do other people think? 2. I've been fortunate to have had the opportunity meet with with a department head at Stanford. She told me that, despite the typical 2 to 3 page SoP space limitation, I should make sure to limit my SoP to one page. The rationale is that reviewers are busy, and they probably won't read the SoP carefully if it's very long. Does Mihai Patrascu's SoP strike other people as being too long? 3. This SoP talks a lot about past and current research, which is good. However, no potential graduate advisors or research groups are discussed. Is this a problem? Also, if anyone has found successful (e.g. admitted) computer science statements of purpose, feel free to share them here!

plz can you post this sop

plz can you post this sop - Mihai Patrascu was accepted  to MIT, Princeton, and U Washington. I hope this is helpful to others who are applying to CS PhD programs.

The SOP is way too long... I mean : typically graduate schools will limit the amount of words/characters in SOPs, this is almost 3 pages long ! I can't believe an admission committee read the whole thing...

His research experience is pretty strong though, perhaps his publications were read by professors before they even read his SoP and needed his experience.

• 3 weeks later...

OMG... Pătraşcu died in 2012 after suffering from brian cancer for a year and a half. He was only 30. RIP

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PhD Program

In many ways, the PhD program is the cornerstone of Computer Science at Boston University.  Our PhD students serve some of the most central roles of our department, from pursuing sponsored research together with supervising faculty members as Research Assistants, to serving as Teaching Fellows in support of our undergraduate and graduate curriculum.

Pursuing the PhD degree enables you to become an expert in a technical subfield of Computer Science and advance the state of the art by contributing original research in that discipline. Most PhD students also gain practical experience in the classroom, as well as, becoming a visible member of the research community by publishing research and delivering oral presentations at conferences and research seminars.

Upon completing your PhD degree, you will be able to set your own research direction, teach and advise students, and work at the forefront of cutting-edge research in academia or at an industrial laboratory.

Learning Outcomes

• Produce and defend original research in the field of Computer Science.
• Master broad knowledge of Computer Science across algorithms, software, systems, theory of computation, and in one of the areas of artificial intelligence, computer graphics, cryptography & security, and data science .
• Demonstrate in-depth knowledge of a particular subject area within Computer Science.
• Actively participate in the Computer Science research community, for example by attending academic conferences and submitting research results for publication in professional conferences and journals.
• Be able to effectively communicate the results of research.

We invite you to learn more about our program through the links below.

PhD Program Information

• Program Milestones
• Breadth Requirements
• Subject Exams
• Specimen Curriculum

Fellowships & Awards

• Computer Science Fellowship Opportunities
• Research Excellence Award
• Teaching Excellence Award
• Teaching Fellow Expectations

More Information

• PhD in Computer Science – Graduate School of Arts & Sciences (GRS) Bulletin
• Graduate School of the College of Arts and Sciences (GRS) PhD Requirements
• Graduation Calendar
• PhD Profile for Computer Science

Apply Today

To apply to the Ph.D. program, please fill out an online application .

Deadline: December 15 for Fall admission.

With questions about admissions, please contact us at [email protected] .

Computer Science Essay Examples

Explore 15+ Brilliant Computer Science Essay Examples: Tips Included

Published on: May 5, 2023

Last updated on: Jan 30, 2024

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Do you struggle with writing computer science essays that get you the grades you deserve?

If so, you're not alone!

Crafting a top-notch essay can be a daunting task, but it's crucial to your success in the field of computer science.

For that, CollegeEssay.org has a solution for you!

In this comprehensive guide, we'll provide you with inspiring examples of computer science essays. You'll learn everything you need to know to write effective and compelling essays that impress your professors and get you the grades you deserve.

So, let's dive in and discover the secrets to writing amazing computer science essays!

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Computer Science Essays: Understanding the Basics

A computer science essay is a piece of writing that explores a topic related to computer science. It may take different forms, such as an argumentative essay, a research paper, a case study, or a reflection paper. 

Just like any other essay, it should be well-researched, clear, concise, and effectively communicate the writer's ideas and arguments.

Computer essay examples encompass a wide range of topics and types, providing students with a diverse set of writing opportunities. 

Here, we will explore some common types of computer science essays:

Middle School Computer Science Essay Example

College Essay Example Computer Science

University Computer Science Essay Example

Computer Science Extended Essay Example

Uiuc Computer Science Essay Example [

Computer Science Essay Examples For Different Fields

Computer science is a broad field that encompasses many different areas of study. For that, given below are some examples of computer science essays for some of the most popular fields within the discipline. 

By exploring these examples, you can gain insight into the different types of essays within this field.

College Application Essay Examples Computer Science

The Future of Computers Technology

Historical Development of Computer Science

Young Children and Technology: Building Computer Literacy

Computer Science And Artificial Intelligence

Looking for more examples of computer science essays? Given below are some additional examples of computer science essays for readers to explore and gain further inspiration from. 

Computer Science – My Choice for Future Career

My Motivation to Pursue Undergraduate Studies in Computer Engineering

Abstract Computer Science

Computer Science Personal Statement Example

Sop For Computer Science

Computer Science Essay Topics

There are countless computer science essay topics to choose from, so it can be challenging to narrow down your options. 

However, the key is to choose a topic that you are passionate about and that aligns with your assignment requirements.

Here are ten examples of computer science essay topics to get you started:

• The impact of artificial intelligence on society: benefits and drawbacks
• Cybersecurity measures in cloud computing systems
• The Ethics of big data: privacy, bias, and Transparency
• The future of quantum computing: possibilities and challenges
• The Role of computer hardware in Healthcare: current applications and potential innovations
• Programming languages: a comparative analysis of their strengths and weaknesses
• The use of machine learning in predicting human behavior
• The challenges and solutions for developing secure and reliable software
• The Role of blockchain technology in improving supply chain management
• The use of data analytics in business decision-making.

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Tips to Write an Effective Computer Science Essay

Writing an effective computer science essay requires a combination of technical expertise and strong writing skills. Here are some tips to help you craft a compelling and well-written essay:

Understand the Requirements: Make sure you understand the assignment requirements, including the essay type, format, and length.

• Choose a Topic: Select a topic that you are passionate about and that aligns with your assignment requirements.
• Create an Outline: Develop a clear and organized outline that highlights the main points and subtopics of your essay.
• Use Appropriate Language and Tone: Use technical terms and language when appropriate. But ensure your writing is clear, concise, and accessible to your target audience.
• Provide Evidence: Use relevant and credible evidence to support your claims, and ensure you cite your sources correctly.
• Edit and Proofread Your Essay: Review your essay for clarity, coherence, and accuracy. Check for grammatical errors, spelling mistakes, and formatting issues.

By following these tips, you can improve the quality of your computer science essay and increase your chances of success.

In conclusion, writing a computer science essay can be a challenging yet rewarding experience. 

It allows you to showcase your knowledge and skills within the field and develop your writing and critical thinking abilities. By following the examples provided in this blog, you can create an effective computer science essay, which will meet your requirements.

If you find yourself struggling with the writing process, consider seeking essay writing help online from CollegeEssay.org. 

Our AI essay writer can provide guidance and support in crafting a top-notch computer science essay.

So, what are you waiting for? Hire our computer science essay writing service today!

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As a Digital Content Strategist, Nova Allison has eight years of experience in writing both technical and scientific content. With a focus on developing online content plans that engage audiences, Nova strives to write pieces that are not only informative but captivating as well.

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Working with faculty who are leaders in the field, our PhD students conduct research with real-world impact. Diversity, equity, and inclusion are core values for Brown CS, and we’ve integrated societal and ethical issues across our graduate and undergraduate curricula. Our new faculty positions focus on both core computer science and emerging CS + X areas.

This page is for prospective PhD students. Current students, go  here . 

After graduation, our alums contribute widely to science, learning, culture, and their communities. In the last year alone, our PhD alums were named an ACM Fellow and an IEEE Fellow and received a CCS Test-of-Time Award . Click the links that follow for recent news stories about our PhD students and alums .

"The breadth, depth, and the easy camaraderie of the computer science department made my stay at Brown for a PhD both a joy and an intellectually rewarding experience. I am also incredibly proud that both my sons also did computer science at Brown and have gone on to become amazing software engineers and entrepreneurs." – Sridhar Ramaswamy , SVP of AI at Snowflake, former SVP of Ads and Commerce at Google (for more testimonials, click here )  

We offer world-class research and education in an interdisciplinary environment (for more detail on the below, click here ):

A lower PhD student/faculty ratio than many of our peers

Full financial support while completing the degree: full tuition, health insurance, and a generous stipend

Teaching assistantships and course instructor opportunities for students who want to hone their teaching skills

Shared offices that overlook Brown's scenic campus

Opportunities to serve on important committees and organize seminars and other events

Students can take courses from Harvard, MIT, RISD, and other institutions without additional cost (see here for details and restrictions)  

"Attending Brown was an extraordinary and transformative experience for many reasons: the close-knit community of graduate students that led to the strongest friendships I ever made, the approachable faculty, the generous mentorship by professors, the remarkable undergraduate students I had the opportunity to teach and work with, and the department and university resources that provided a comfortable living in beautiful Providence and connections with industry and academia." – Alexandra Papoutsaki , Associate Professor in Computer Science, Pomona College (for more testimonials, click here )

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Home > Engineering > Computer Science > Computer Science Graduate Projects

Computer Science Graduate Projects and Theses

Theses/dissertations from 2023 2023.

High-Performance Domain-Specific Library for Hydrologic Data Processing , Kalyan Bhetwal

Verifying Data Provenance During Workflow Execution for Scientific Reproducibility , Rizbanul Hasan

Remote Sensing to Advance Understanding of Snow-Vegetation Relationships and Quantify Snow Depth and Snow Water Equivalent , Ahmad Hojatimalekshah

Exploring the Capability of a Self-Supervised Conditional Image Generator for Image-to-Image Translation without Labeled Data: A Case Study in Mobile User Interface Design , Hailee Kiesecker

Fake News Detection Using Narrative Content and Discourse , Hongmin Kim

Anomaly Detection Using Graph Neural Network , Bishal Lakha

Sparse Format Conversion and Code Synthesis , Tobi Goodness Popoola

Portable Sparse Polyhedral Framework Code Generation Using Multi Level Intermediate Representation , Aaron St. George

Severity Measures for Assessing Error in Automatic Speech Recognition , Ryan Whetten

Theses/Dissertations from 2022 2022

Improved Computational Prediction of Function and Structural Representation of Self-Cleaving Ribozymes with Enhanced Parameter Selection and Library Design , James D. Beck

Meshfree Methods for PDEs on Surfaces , Andrew Michael Jones

Deep Learning of Microstructures , Amir Abbas Kazemzadeh Farizhandi

Long-Term Trends in Extreme Environmental Events with Changepoint Detection , Mintaek Lee

Structure Aware Smart Encoding and Decoding of Information in DNA , Shoshanna Llewellyn

Towards Making Transformer-Based Language Models Learn How Children Learn , Yousra Mahdy

Ontology-Based Formal Approach for Safety and Security Verification of Industrial Control Systems , Ramesh Neupane

Improving Children's Authentication Practices with Respect to Graphical Authentication Mechanism , Dhanush Kumar Ratakonda

Hate Speech Detection Using Textual and User Features , Rohan Raut

Automated Detection of Sockpuppet Accounts in Wikipedia , Mostofa Najmus Sakib

Characterization and Mitigation of False Information on the Web , Anu Shrestha

Sinusoidal Projection for 360° Image Compression and Triangular Discrete Cosine Transform Impact in the JPEG Pipeline , Iker Vazquez Lopez

Theses/Dissertations from 2021 2021

Training Wheels for Web Search: Multi-Perspective Learning to Rank to Support Children's Information Seeking in the Classroom , Garrett Allen

Fair and Efficient Consensus Protocols for Secure Blockchain Applications , Golam Dastoger Bashar

Why Don't You Act Your Age?: Recognizing the Stereotypical 8-12 Year Old Searcher by Their Search Behavior , Michael Green

Ensuring Consistency and Efficiency of the Incremental Unit Network in a Distributed Architecture , Mir Tahsin Imtiaz

Modeling Real and Fake News Sharing in Social Networks , Abishai Joy

Modeling and Analyzing Users' Privacy Disclosure Behavior to Generate Personalized Privacy Policies , A.K.M. Nuhil Mehdy

Into the Unknown: Exploration of Search Engines' Responses to Users with Depression and Anxiety , Ashlee Milton

Generating Test Inputs from String Constraints with an Automata-Based Solver , Marlin Roberts

A Case Study in Representing Scientific Applications ( GeoAc ) Using the Sparse Polyhedral Framework , Ravi Shankar

Actors for the Internet of Things , Arjun Shukla

Theses/Dissertations from 2020 2020

Towards Unifying Grounded and Distributional Semantics Using the Words-as-Classifiers Model of Lexical Semantics , Stacy Black

Improving Scientist Productivity, Architecture Portability, and Performance in ParFlow , Michael Burke

Polyhedral+Dataflow Graphs , Eddie C. Davis

Improving Spellchecking for Children: Correction and Design , Brody Downs

A Collection of Fast Algorithms for Scalar and Vector-Valued Data on Irregular Domains: Spherical Harmonic Analysis, Divergence-Free/Curl-Free Radial Basis Functions, and Implicit Surface Reconstruction , Kathryn Primrose Drake

Privacy-Preserving Protocol for Atomic Swap Between Blockchains , Kiran Gurung

Unsupervised Structural Graph Node Representation Learning , Mikel Joaristi

Detecting Undisclosed Paid Editing in Wikipedia , Nikesh Joshi

Do You Feel Me?: Learning Language from Humans with Robot Emotional Displays , David McNeill

Obtaining Real-World Benchmark Programs from Open-Source Repositories Through Abstract-Semantics Preserving Transformations , Maria Anne Rachel Paquin

Content Based Image Retrieval (CBIR) for Brand Logos , Enjal Parajuli

A Resilience Metric for Modern Power Distribution Systems , Tyler Bennett Phillips

Theses/Dissertations from 2019 2019

Edge-Assisted Workload-Aware Image Processing System , Anil Acharya

MINOS: Unsupervised Netflow-Based Detection of Infected and Attacked Hosts, and Attack Time in Large Networks , Mousume Bhowmick

Deviant: A Mutation Testing Tool for Solidity Smart Contracts , Patrick Chapman

Querying Over Encrypted Databases in a Cloud Environment , Jake Douglas

A Hybrid Model to Detect Fake News , Indhumathi Gurunathan

Suitability of Finite State Automata to Model String Constraints in Probablistic Symbolic Execution , Andrew Harris

UNICORN Framework: A User-Centric Approach Toward Formal Verification of Privacy Norms , Rezvan Joshaghani

Detection and Countermeasure of Saturation Attacks in Software-Defined Networks , Samer Yousef Khamaiseh

Secure Two-Party Protocol for Privacy-Preserving Classification via Differential Privacy , Manish Kumar

Application-Specific Memory Subsystem Benchmarking , Mahesh Lakshminarasimhan

Multilingual Information Retrieval: A Representation Building Perspective , Ion Madrazo

Improved Study of Side-Channel Attacks Using Recurrent Neural Networks , Muhammad Abu Naser Rony Chowdhury

Investigating the Effects of Social and Temporal Dynamics in Fitness Games on Children's Physical Activity , Ankita Samariya

BullyNet: Unmasking Cyberbullies on Social Networks , Aparna Sankaran

FALCON: Framework for Anomaly Detection In Industrial Control Systems , Subin Sapkota

Investigating Semantic Properties of Images Generated from Natural Language Using Neural Networks , Samuel Ward Schrader

Incremental Processing for Improving Conversational Grounding in a Chatbot , Aprajita Shukla

Estimating Error and Bias of Offline Recommender System Evaluation Results , Mucun Tian

Theses/Dissertations from 2018 2018

Leveraging Tiled Display for Big Data Visualization Using D3.js , Ujjwal Acharya

Fostering the Retrieval of Suitable Web Resources in Response to Children's Educational Search Tasks , Oghenemaro Deborah Anuyah

Privacy-Preserving Genomic Data Publishing via Differential Privacy , Tanya Khatri

Injecting Control Commands Through Sensory Channel: Attack and Defense , Farhad Rasapour

Strong Mutation-Based Test Generation of XACML Policies , Roshan Shrestha

Performance, Scalability, and Robustness in Distributed File Tree Copy , Christopher Robert Sutton

Using DNA For Data Storage: Encoding and Decoding Algorithm Development , Kelsey Suyehira

Detecting Saliency by Combining Speech and Object Detection in Indoor Environments , Kiran Thapa

Theses/Dissertations from 2017 2017

Identifying Restaurants Proposing Novel Kinds of Cuisines: Using Yelp Reviews , Haritha Akella

Editing Behavior Analysis and Prediction of Active/Inactive Users in Wikipedia , Harish Arelli

CloudSkulk: Design of a Nested Virtual Machine Based Rootkit-in-the-Middle Attack , Joseph Anthony Connelly

Predicting Friendship Strength in Facebook , Nitish Dhakal

Privacy-Preserving Trajectory Data Publishing via Differential Privacy , Ishita Dwivedi

Cultivating Community Interactions in Citizen Science: Connecting People to Each Other and the Environment , Bret Allen Finley

Uncovering New Links Through Interaction Duration , Laxmi Amulya Gundala

Variance: Secure Two-Party Protocol for Solving Yao's Millionaires' Problem in Bitcoin , Joshua Holmes

A Scalable Graph-Coarsening Based Index for Dynamic Graph Databases , Akshay Kansal

Integrity Coded Databases: Ensuring Correctness and Freshness of Outsourced Databases , Ujwal Karki

Editable View Optimized Tone Mapping For Viewing High Dynamic Range Panoramas On Head Mounted Display , Yuan Li

The Effects of Pair-Programming in a High School Introductory Computer Science Class , Ken Manship

Towards Automatic Repair of XACML Policies , Shuai Peng

Identification of Unknown Landscape Types Using CNN Transfer Learning , Ashish Sharma

Hand Gesture Recognition for Sign Language Transcription , Iker Vazquez Lopez

Learning to Code Music : Development of a Supplemental Unit for High School Computer Science , Kelsey Wright

Theses/Dissertations from 2016 2016

Identification of Small Endogenous Viral Elements within Host Genomes , Edward C. Davis Jr.

When the System Becomes Your Personal Docent: Curated Book Recommendations , Nevena Dragovic

Security Testing with Misuse Case Modeling , Samer Yousef Khamaiseh

Estimating Length Statistics of Aggregate Fried Potato Product via Electromagnetic Radiation Attenuation , Jesse Lovitt

Towards Multipurpose Readability Assessment , Ion Madrazo

Evaluation of Topic Models for Content-Based Popularity Prediction on Social Microblogs , Axel Magnuson

CEST: City Event Summarization using Twitter , Deepa Mallela

Developing an ABAC-Based Grant Proposal Workflow Management System , Milson Munakami

Phoenix and Hive as Alternatives to RDBMS , Diana Ornelas

Massively Parallel Algorithm for Solving the Eikonal Equation on Multiple Accelerator Platforms , Anup Shrestha

A Certificateless One-Way Group Key Agreement Protocol for Point-to-Point Email Encryption , Srisarguru Sridhar

Dynamic Machine Level Resource Allocation to Improve Tasking Performance Across Multiple Processes , Richard Walter Thatcher

Theses/Dissertations from 2015 2015

Developing an Application for Evolutionary Search for Computational Models of Cellular Development , Nicolas Scott Cornia

Accelerated Radar Signal Processing in Large Geophysical Datasets , Ravi Preesha Geetha

Integrity Coded Databases (ICDB) – Protecting Integrity for Outsourced Databases , Archana Nanjundarao

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Home > CICS > CS > CS_DISS

Computer Science Department Dissertations Collection

Dissertations from 2024 2024.

Enabling Privacy and Trust in Edge AI Systems , Akanksha Atrey, Computer Science

Generative Language Models for Personalized Information Understanding , Pengshan Cai, Computer Science

Towards Automatic and Robust Variational Inference , Tomas Geffner, Computer Science

Multi-SLAM Systems for Fault-Tolerant Simultaneous Localization and Mapping , Samer Nashed, Computer Science

Policy Gradient Methods: Analysis, Misconceptions, and Improvements , Christopher P. Nota, Computer Science

Data to science with AI and human-in-the-loop , Gustavo Perez Sarabia, Computer Science

Question Answering By Case-Based Reasoning With Textual Evidence , Dung N. Thai, Computer Science

Dissertations from 2023 2023

An Introspective Approach for Competence-Aware Autonomy , Connor Basich, Computer Science

Foundations of Node Representation Learning , Sudhanshu Chanpuriya, Computer Science

Learning to See with Minimal Human Supervision , Zezhou Cheng, Computer Science

IMPROVING USER EXPERIENCE BY OPTIMIZING CLOUD SERVICES , Ishita Dasgupta, Computer Science

Automating the Formal Verification of Software , Emily First, Computer Science

Learning from Sequential User Data: Models and Sample-efficient Algorithms , Aritra Ghosh, Computer Science

Human-Centered Technologies for Inclusive Collection and Analysis of Public-Generated Data , Mahmood Jasim, Computer Science

Rigorous Experimentation For Reinforcement Learning , Scott M. Jordan, Computer Science

Towards Robust Long-form Text Generation Systems , Kalpesh Krishna, Computer Science

Emerging Trustworthiness Issues in Distributed Learning Systems , Hamid Mozaffari, Computer Science

TOWARDS RELIABLE CIRCUMVENTION OF INTERNET CENSORSHIP , Milad nasresfahani, Computer Science

Evidence Assisted Learning for Clinical Decision Support Systems , Bhanu Pratap Singh Rawat, Computer Science

DESIGN AND ANALYSIS OF CONTENT CACHING SYSTEMS , Anirudh Sabnis, Computer Science

Quantifying and Enhancing the Security of Federated Learning , Virat Vishnu Shejwalkar, Computer Science

Effective and Efficient Transfer Learning in the Era of Large Language Models , Tu Vu, Computer Science

Data-driven Modeling and Analytics for Greening the Energy Ecosystem , John Wamburu, Computer Science

Bayesian Structural Causal Inference with Probabilistic Programming , Sam A. Witty, Computer Science

LEARNING TO RIG CHARACTERS , Zhan Xu, Computer Science

GRAPH REPRESENTATION LEARNING WITH BOX EMBEDDINGS , Dongxu Zhang, Computer Science

Dissertations from 2022 2022

COMBINATORIAL ALGORITHMS FOR GRAPH DISCOVERY AND EXPERIMENTAL DESIGN , Raghavendra K. Addanki, Computer Science

MEASURING NETWORK INTERFERENCE AND MITIGATING IT WITH DNS ENCRYPTION , Seyed Arian Akhavan Niaki, Computer Science

Few-Shot Natural Language Processing by Meta-Learning Without Labeled Data , Trapit Bansal, Computer Science

Communicative Information Visualizations: How to make data more understandable by the general public , Alyxander Burns, Computer Science

REINFORCEMENT LEARNING FOR NON-STATIONARY PROBLEMS , Yash Chandak, Computer Science

Modeling the Multi-mode Distribution in Self-Supervised Language Models , Haw-Shiuan Chang, Computer Science

Nonparametric Contextual Reasoning for Question Answering over Large Knowledge Bases , Rajarshi Das, Computer Science

Languages and Compilers for Writing Efficient High-Performance Computing Applications , Abhinav Jangda, Computer Science

Controllable Neural Synthesis for Natural Images and Vector Art , Difan Liu, Computer Science

Probabilistic Commonsense Knowledge , Xiang Li, Computer Science

DISTRIBUTED LEARNING ALGORITHMS: COMMUNICATION EFFICIENCY AND ERROR RESILIENCE , Raj Kumar Maity, Computer Science

Practical Methods for High-Dimensional Data Publication with Differential Privacy , Ryan H. McKenna, Computer Science

Incremental Non-Greedy Clustering at Scale , Nicholas Monath, Computer Science

High-Quality Automatic Program Repair , Manish Motwani, Computer Science

Unobtrusive Assessment of Upper-Limb Motor Impairment Using Wearable Inertial Sensors , Brandon R. Oubre, Computer Science

Mixture Models in Machine Learning , Soumyabrata Pal, Computer Science

Decision Making with Limited Data , Kieu My Phan, Computer Science

Neural Approaches for Language-Agnostic Search and Recommendation , Hamed Rezanejad Asl Bonab, Computer Science

Low Resource Language Understanding in Voice Assistants , Subendhu Rongali, Computer Science

Enabling Daily Tracking of Individual’s Cognitive State With Eyewear , Soha Rostaminia, Computer Science

LABELED MODULES IN PROGRAMS THAT EVOLVE , Anil K. Saini, Computer Science

Reliable Decision-Making with Imprecise Models , Sandhya Saisubramanian, Computer Science

Data Scarcity in Event Analysis and Abusive Language Detection , Sheikh Muhammad Sarwar, Computer Science

Representation Learning for Shape Decomposition, By Shape Decomposition , Gopal Sharma, Computer Science

Metareasoning for Planning and Execution in Autonomous Systems , Justin Svegliato, Computer Science

Approximate Bayesian Deep Learning for Resource-Constrained Environments , Meet Prakash Vadera, Computer Science

ANSWER SIMILARITY GROUPING AND DIVERSIFICATION IN QUESTION ANSWERING SYSTEMS , Lakshmi Nair Vikraman, Computer Science

Dissertations from 2021 2021

Neural Approaches to Feedback in Information Retrieval , Keping Bi, Computer Science

Sociolinguistically Driven Approaches for Just Natural Language Processing , Su Lin Blodgett, Computer Science

Enabling Declarative and Scalable Prescriptive Analytics in Relational Data , Matteo Brucato, Computer Science

Neural Methods for Answer Passage Retrieval over Sparse Collections , Daniel Cohen, Computer Science

Utilizing Graph Structure for Machine Learning , Stefan Dernbach, Computer Science

Enhancing Usability and Explainability of Data Systems , Anna Fariha, Computer Science

Algorithms to Exploit Data Sparsity , Larkin H. Flodin, Computer Science

3D Shape Understanding and Generation , Matheus Gadelha, Computer Science

Robust Algorithms for Clustering with Applications to Data Integration , Sainyam Galhotra, Computer Science

Improving Evaluation Methods for Causal Modeling , Amanda Gentzel, Computer Science

SAFE AND PRACTICAL MACHINE LEARNING , Stephen J. Giguere, Computer Science

COMPACT REPRESENTATIONS OF UNCERTAINTY IN CLUSTERING , Craig Stuart Greenberg, Computer Science

Natural Language Processing for Lexical Corpus Analysis , Abram Kaufman Handler, Computer Science

Social Measurement and Causal Inference with Text , Katherine A. Keith, Computer Science

Concentration Inequalities in the Wild: Case Studies in Blockchain & Reinforcement Learning , A. Pinar Ozisik, Computer Science

Resource Allocation in Distributed Service Networks , Nitish Kumar Panigrahy, Computer Science

History Modeling for Conversational Information Retrieval , Chen Qu, Computer Science

Design and Implementation of Algorithms for Traffic Classification , Fatemeh Rezaei, Computer Science

SCALING DOWN THE ENERGY COST OF CONNECTING EVERYDAY OBJECTS TO THE INTERNET , Mohammad Rostami, Computer Science

Deep Learning Models for Irregularly Sampled and Incomplete Time Series , Satya Narayan Shukla, Computer Science

Traffic engineering in planet-scale cloud networks , Rachee Singh, Computer Science

Video Adaptation for High-Quality Content Delivery , Kevin Spiteri, Computer Science

Learning from Limited Labeled Data for Visual Recognition , Jong-Chyi Su, Computer Science

Human Mobility Monitoring using WiFi: Analysis, Modeling, and Applications , Amee Trivedi, Computer Science

Geometric Representation Learning , Luke Vilnis, Computer Science

Understanding of Visual Domains via the Lens of Natural Language , Chenyun Wu, Computer Science

Towards Practical Differentially Private Mechanism Design and Deployment , Dan Zhang, Computer Science

Audio-driven Character Animation , Yang Zhou, Computer Science

Dissertations from 2020 2020

Noise-Aware Inference for Differential Privacy , Garrett Bernstein, Computer Science

Motion Segmentation - Segmentation of Independently Moving Objects in Video , Pia Katalin Bideau, Computer Science

An Empirical Assessment of the Effectiveness of Deception for Cyber Defense , Kimberly J. Ferguson-Walter, Computer Science

Integrating Recognition and Decision Making to Close the Interaction Loop for Autonomous Systems , Richard Freedman, Computer Science

Improving Reinforcement Learning Techniques by Leveraging Prior Experience , Francisco M. Garcia, Computer Science

Optimization and Training of Generational Garbage Collectors , Nicholas Jacek, Computer Science

Understanding the Dynamic Visual World: From Motion to Semantics , Huaizu Jiang, Computer Science

Improving Face Clustering in Videos , SouYoung Jin, Computer Science

Reasoning About User Feedback Under Identity Uncertainty in Knowledge Base Construction , Ariel Kobren, Computer Science

Learning Latent Characteristics of Data and Models using Item Response Theory , John P. Lalor, Computer Science

Higher-Order Representations for Visual Recognition , Tsung-Yu Lin, Computer Science

Learning from Irregularly-Sampled Time Series , Steven Cheng-Xian Li, Computer Science

Dynamic Composition of Functions for Modular Learning , Clemens GB Rosenbaum, Computer Science

Improving Visual Recognition With Unlabeled Data , Aruni Roy Chowdhury, Computer Science

Deep Neural Networks for 3D Processing and High-Dimensional Filtering , Hang Su, Computer Science

Towards Optimized Traffic Provisioning and Adaptive Cache Management for Content Delivery , Aditya Sundarrajan, Computer Science

The Limits of Location Privacy in Mobile Devices , Keen Yuun Sung, Computer Science

ALGORITHMS FOR MASSIVE, EXPENSIVE, OR OTHERWISE INCONVENIENT GRAPHS , David Tench, Computer Science

System Design for Digital Experimentation and Explanation Generation , Emma Tosch, Computer Science

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Email forwarding for @cs.stanford.edu is changing. Updates and details here . CS Commencement Ceremony June 16, 2024.  Learn More .

BS | Independent Study

Main navigation.

Undergraduate research is often done through CURIS, for academic credit, or through an informal arrangement with a professor.

Independent Study Research Courses

• CS191 or CS191W . These courses are each a one-quarter research project that fulfills the Senior Project requirement. CS191W is the Writing in the Major (WiM) version of this course. Students must set up their project, find a faculty sponsor, and submit a proposal.
• CS195.  These courses are one-quarter research project that fulfills a CS elective on the undergraduate program sheet (up to four units) for students who are not yet eligible to take CS191. Students must work under faculty supervision and get prior approval for their project.
• CS199 (Bachelor's) and CS399 (Master's). Are a one-quarter independent studies for students who have already taken four units of CS195 but are not eligible for CS191. These units will count towards the University-wide graduation requirements, but not toward the CS major itself.

For all of these classes, students can propose a project of their own or can find and receive approval for a project listed on the  undergraduate CS research  website. All students must get prior approval from their faculty sponsor, at the very least, before signing up for any of these classes.

Research Project Course

• CS294 or CS294W . These courses fulfill the Senior Project requirement and allow students to get involved in a major ongoing research project. CS294W is the Writing in the Major (WiM) version of the course.
• Note : Check the course listing to see which research areas are covered in 294 for any given year.

Research Course Flowchart

Still not sure which course is suitable for you? Consider this flow chart:

University of Chicago Computer Science Researchers To Present Ten Papers at CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems is a premier international conference where researchers and practitioners gather to discuss the latest research in human-computer interaction. Held annually, CHI brings together experts from academia and industry to present groundbreaking research, share insights, and explore future directions in the field.

This year’s conference, CHI 2024, will see a remarkable showcase of innovative research from students and faculty at the University of Chicago Department of Computer Science . Three papers, including one each from Associate Professor Blase Ur’s group, Associate Professor Pedro Lopes’ group, and Associate Professor Marshini Chetty’s group, received best paper awards. Another paper from Lopes’ group also received an honorable mention.

Some works, like papers featuring Neubauer Professor Nick Feamster and Associate Professor Marshini Chetty, are also collaborations with faculty from The Law School and the Harris School of Public Policy , highlighting the interdisciplinary work that often takes place in the department. Each paper span a diverse range of topics, including contextual notifications for highlighting fairness and bias in data science, in-depth studies of online content moderation policies, investigations into compliance with privacy regulations, AI for the well-being of workers, the introduction of a design space for writing assistants, groundbreaking advancements in haptic interfaces, and innovative approaches to promoting digital well-being through leveraging material receipts for screen-time reflection. The work reflects the department’s commitment to advancing knowledge and addressing real-world challenges in the realm of computing.

Bias In Data Science

Best Paper Award Harrison et al., 2024. JupyterLab in Retrograde: Contextual Notifications That Highlight Fairness and Bias Issues for Data Scientists.

Although the paper has won an award, the team will be presenting the paper virtually at the conference, rather than traveling to Hawai’i, as a show of solidarity with the community’s protests over the conference’s impact on the local community.

Content Moderation

Schaffner et al., 2024. Community Guidelines Make This the Best Party on the Internet: An In-Depth Study of Online Platforms’ Content Moderation Policies.

Compliance and Privacy Regulations

Tran et al., 2024. Measuring Compliance with the California Consumer Privacy Act Over Space and Time.

AI and Worker’s Well-being

Best Paper Award Das Swain et al., 2024. Sensible and Sensitive AI for Worker Wellbeing: Factors that Inform Adoption and Resistance for Information Workers.

Design Space for Writing Assistants

Lee at el., 2024. A Design Space for Intelligent and Interactive Writing Assistants.

Pushing Boundaries in Haptic Interfaces

Best Paper Award Nith et al., 2024. SplitBody: Reducing Mental Workload while Multitasking via Muscle Stimulation.

Honorable Mention Tanaka et al., 2024. Haptic Source-effector: Full-body Haptics via Non-invasive Brain Stimulation.

Teng et al., 2024. Haptic Permeability: Adding Holes to Tactile Devices Improves Dexterity. Marzursky et al., 2024. Stick&Slip: Altering Fingerpad Friction via Liquid Coatings.

The Human-Computer Integration Lab (directed by Associate Professor Pedro Lopes ) makes a significant impact with not one, but four papers showcasing groundbreaking advancements in haptic interfaces:

Tangible Intervention for Digital Well-being

Sathya et al., 2024. Attention Receipts: Utilizing the Materiality of Receipts to Improve Screen-time Reflection on YouTube.

The University of Chicago Department of Computer Science continues to make significant contributions to the field of human-computer interaction, as evidenced by the diverse and impactful research that will be showcased at CHI 2024 in May. These researchers’ papers exemplify the department’s dedication to advancing knowledge, fostering innovation, and addressing pressing societal issues through computing research.

Related News

Fabrobotics: the fusion of 3d printing and mobile robots, high school students in the collegiate scholars program get to know robots, uchicago computer scientists design small backpack that mimics big sensations, computer science class shows students how to successfully create circuit boards without engineering experience, uchicago cs researchers shine at chi 2023 with 12 papers and multiple awards, new prototypes aerorigui and throwio take spatial interaction to new heights – literally, computer science displays catch attention at msi’s annual robot block party, uchicago, stanford researchers explore how robots and computers can help strangers have meaningful in-person conversations, high school students find their place in computing through wearables workshop, uchicago cs researchers create living smartwatch to explore human-device relations, uchicago research tests whether robots or humans are better game partners, first in-person robotics class lets students see code come to (artificial) life.

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Computer Science > Machine Learning

Title: kan: kolmogorov-arnold networks.

Abstract: Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. For accuracy, much smaller KANs can achieve comparable or better accuracy than much larger MLPs in data fitting and PDE solving. Theoretically and empirically, KANs possess faster neural scaling laws than MLPs. For interpretability, KANs can be intuitively visualized and can easily interact with human users. Through two examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs.

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Natural language boosts LLM performance in coding, planning, and robotics

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Large language models (LLMs) are becoming increasingly useful for programming and robotics tasks, but for more complicated reasoning problems, the gap between these systems and humans looms large. Without the ability to learn new concepts like humans do, these systems fail to form good abstractions — essentially, high-level representations of complex concepts that skip less-important details — and thus sputter when asked to do more sophisticated tasks. Luckily, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have found a treasure trove of abstractions within natural language. In three papers to be presented at the International Conference on Learning Representations this month, the group shows how our everyday words are a rich source of context for language models, helping them build better overarching representations for code synthesis, AI planning, and robotic navigation and manipulation. The three separate frameworks build libraries of abstractions for their given task: LILO (library induction from language observations) can synthesize, compress, and document code; Ada (action domain acquisition) explores sequential decision-making for artificial intelligence agents; and LGA (language-guided abstraction) helps robots better understand their environments to develop more feasible plans. Each system is a neurosymbolic method, a type of AI that blends human-like neural networks and program-like logical components. LILO: A neurosymbolic framework that codes Large language models can be used to quickly write solutions to small-scale coding tasks, but cannot yet architect entire software libraries like the ones written by human software engineers. To take their software development capabilities further, AI models need to refactor (cut down and combine) code into libraries of succinct, readable, and reusable programs. Refactoring tools like the previously developed MIT-led Stitch algorithm can automatically identify abstractions, so, in a nod to the Disney movie “Lilo & Stitch,” CSAIL researchers combined these algorithmic refactoring approaches with LLMs. Their neurosymbolic method LILO uses a standard LLM to write code, then pairs it with Stitch to find abstractions that are comprehensively documented in a library. LILO’s unique emphasis on natural language allows the system to do tasks that require human-like commonsense knowledge, such as identifying and removing all vowels from a string of code and drawing a snowflake. In both cases, the CSAIL system outperformed standalone LLMs, as well as a previous library learning algorithm from MIT called DreamCoder, indicating its ability to build a deeper understanding of the words within prompts. These encouraging results point to how LILO could assist with things like writing programs to manipulate documents like Excel spreadsheets, helping AI answer questions about visuals, and drawing 2D graphics.

“Language models prefer to work with functions that are named in natural language,” says Gabe Grand SM '23, an MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and lead author on the research. “Our work creates more straightforward abstractions for language models and assigns natural language names and documentation to each one, leading to more interpretable code for programmers and improved system performance.”

When prompted on a programming task, LILO first uses an LLM to quickly propose solutions based on data it was trained on, and then the system slowly searches more exhaustively for outside solutions. Next, Stitch efficiently identifies common structures within the code and pulls out useful abstractions. These are then automatically named and documented by LILO, resulting in simplified programs that can be used by the system to solve more complex tasks.

The MIT framework writes programs in domain-specific programming languages, like Logo, a language developed at MIT in the 1970s to teach children about programming. Scaling up automated refactoring algorithms to handle more general programming languages like Python will be a focus for future research. Still, their work represents a step forward for how language models can facilitate increasingly elaborate coding activities. Ada: Natural language guides AI task planning Just like in programming, AI models that automate multi-step tasks in households and command-based video games lack abstractions. Imagine you’re cooking breakfast and ask your roommate to bring a hot egg to the table — they’ll intuitively abstract their background knowledge about cooking in your kitchen into a sequence of actions. In contrast, an LLM trained on similar information will still struggle to reason about what they need to build a flexible plan. Named after the famed mathematician Ada Lovelace, who many consider the world’s first programmer, the CSAIL-led “Ada” framework makes headway on this issue by developing libraries of useful plans for virtual kitchen chores and gaming. The method trains on potential tasks and their natural language descriptions, then a language model proposes action abstractions from this dataset. A human operator scores and filters the best plans into a library, so that the best possible actions can be implemented into hierarchical plans for different tasks. “Traditionally, large language models have struggled with more complex tasks because of problems like reasoning about abstractions,” says Ada lead researcher Lio Wong, an MIT graduate student in brain and cognitive sciences, CSAIL affiliate, and LILO coauthor. “But we can combine the tools that software engineers and roboticists use with LLMs to solve hard problems, such as decision-making in virtual environments.”

When the researchers incorporated the widely-used large language model GPT-4 into Ada, the system completed more tasks in a kitchen simulator and Mini Minecraft than the AI decision-making baseline “Code as Policies.” Ada used the background information hidden within natural language to understand how to place chilled wine in a cabinet and craft a bed. The results indicated a staggering 59 and 89 percent task accuracy improvement, respectively. With this success, the researchers hope to generalize their work to real-world homes, with the hopes that Ada could assist with other household tasks and aid multiple robots in a kitchen. For now, its key limitation is that it uses a generic LLM, so the CSAIL team wants to apply a more powerful, fine-tuned language model that could assist with more extensive planning. Wong and her colleagues are also considering combining Ada with a robotic manipulation framework fresh out of CSAIL: LGA (language-guided abstraction). Language-guided abstraction: Representations for robotic tasks Andi Peng SM ’23, an MIT graduate student in electrical engineering and computer science and CSAIL affiliate, and her coauthors designed a method to help machines interpret their surroundings more like humans, cutting out unnecessary details in a complex environment like a factory or kitchen. Just like LILO and Ada, LGA has a novel focus on how natural language leads us to those better abstractions. In these more unstructured environments, a robot will need some common sense about what it’s tasked with, even with basic training beforehand. Ask a robot to hand you a bowl, for instance, and the machine will need a general understanding of which features are important within its surroundings. From there, it can reason about how to give you the item you want. 

In LGA’s case, humans first provide a pre-trained language model with a general task description using natural language, like “bring me my hat.” Then, the model translates this information into abstractions about the essential elements needed to perform this task. Finally, an imitation policy trained on a few demonstrations can implement these abstractions to guide a robot to grab the desired item. Previous work required a person to take extensive notes on different manipulation tasks to pre-train a robot, which can be expensive. Remarkably, LGA guides language models to produce abstractions similar to those of a human annotator, but in less time. To illustrate this, LGA developed robotic policies to help Boston Dynamics’ Spot quadruped pick up fruits and throw drinks in a recycling bin. These experiments show how the MIT-developed method can scan the world and develop effective plans in unstructured environments, potentially guiding autonomous vehicles on the road and robots working in factories and kitchens.

“In robotics, a truth we often disregard is how much we need to refine our data to make a robot useful in the real world,” says Peng. “Beyond simply memorizing what’s in an image for training robots to perform tasks, we wanted to leverage computer vision and captioning models in conjunction with language. By producing text captions from what a robot sees, we show that language models can essentially build important world knowledge for a robot.” The challenge for LGA is that some behaviors can’t be explained in language, making certain tasks underspecified. To expand how they represent features in an environment, Peng and her colleagues are considering incorporating multimodal visualization interfaces into their work. In the meantime, LGA provides a way for robots to gain a better feel for their surroundings when giving humans a helping hand. 

An “exciting frontier” in AI

“Library learning represents one of the most exciting frontiers in artificial intelligence, offering a path towards discovering and reasoning over compositional abstractions,” says assistant professor at the University of Wisconsin-Madison Robert Hawkins, who was not involved with the papers. Hawkins notes that previous techniques exploring this subject have been “too computationally expensive to use at scale” and have an issue with the lambdas, or keywords used to describe new functions in many languages, that they generate. “They tend to produce opaque 'lambda salads,' big piles of hard-to-interpret functions. These recent papers demonstrate a compelling way forward by placing large language models in an interactive loop with symbolic search, compression, and planning algorithms. This work enables the rapid acquisition of more interpretable and adaptive libraries for the task at hand.” By building libraries of high-quality code abstractions using natural language, the three neurosymbolic methods make it easier for language models to tackle more elaborate problems and environments in the future. This deeper understanding of the precise keywords within a prompt presents a path forward in developing more human-like AI models. MIT CSAIL members are senior authors for each paper: Joshua Tenenbaum, a professor of brain and cognitive sciences, for both LILO and Ada; Julie Shah, head of the Department of Aeronautics and Astronautics, for LGA; and Jacob Andreas, associate professor of electrical engineering and computer science, for all three. The additional MIT authors are all PhD students: Maddy Bowers and Theo X. Olausson for LILO, Jiayuan Mao and Pratyusha Sharma for Ada, and Belinda Z. Li for LGA. Muxin Liu of Harvey Mudd College was a coauthor on LILO; Zachary Siegel of Princeton University, Jaihai Feng of the University of California at Berkeley, and Noa Korneev of Microsoft were coauthors on Ada; and Ilia Sucholutsky, Theodore R. Sumers, and Thomas L. Griffiths of Princeton were coauthors on LGA.  LILO and Ada were supported, in part, by ​​MIT Quest for Intelligence, the MIT-IBM Watson AI Lab, Intel, U.S. Air Force Office of Scientific Research, the U.S. Defense Advanced Research Projects Agency, and the U.S. Office of Naval Research, with the latter project also receiving funding from the Center for Brains, Minds and Machines. LGA received funding from the U.S. National Science Foundation, Open Philanthropy, the Natural Sciences and Engineering Research Council of Canada, and the U.S. Department of Defense.

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Ph.D. Qualifying Exam Presentation by Yuqi Fu

Computer Science Department

SEALS: A Self-Adaptive, Learned Scheduler for Serverless Functions

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Abstract:   

Serverless functions are ephemeral, highly concurrent, and bursty, with an execution duration ranging from a few milliseconds to a few seconds. The workload behaviors pose new challenges to kernel scheduling. However, Linux CFS (Completely Fair Scheduler) neglects the short-term demands of CPU time from short-lived serverless functions, severely impacting the performance of short functions.

In the work, we propose a novel application-aware kernel scheduler, SEALS (SElf-Aadptive, Learned Scheduler), based on the insights that a predictive or learned priority scheduler can achieve near-optimal performance by approximating SRTF (Shortest remaining time first). To this end, we design SEALS to have a decoupled scheduler frontend and backend architecture that unifies approximate SRTF with proportional-share scheduling. SEALS frontend sits in the user space and approximate SRTF-inspired priority scheduling by adaptively learning from an SRTF simulation on recent past workload. SEALS’s backend uses eBPF functions hooked to CFS to carry out the learned policies sent from the user space to inform scheduling decisions in the kernel. This report will provide details of SEALS design. We evaluate SEALS extensively using production FaaS workloads collected from Huawei and Azure platforms. Results show that SEALS achieves a reduction of 57.2% in average function duration, compared to CFS.

• Haiying Shen, Committee Chair , (CS, ECE/SEAS/UVA)
• Yue Cheng, Advisor  (CS/SEAS, SDS/UVA )
• Geoffrey Fox (CS, Biocomplexity/SEAS/UVA ) 
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Varadarajan wins graduate college's outstanding mentor award.

Kasturi Varadarajan, professor in the Department of Computer Science, received the Graduate College's Outstanding Faculty Mentor Award in 2024.

Varadarajan, an expert in algorithmic foundations, computational geometry, and combinatorial optimization, has served as a Director of Graduate Studies—leading the orientation of new graduate students and organizing professional development workshops for the department.

During his time at Iowa, Varadarajan has mentored 8 doctoral students to completion with a 9 th now in progress. He has also advised over 70 master’s students on course choices needed for completion of their programs, as well as several non-computer science students transitioning to the program.

His award nominators stressed that all of his students are better for having known him and he is admired for the lasting relationships he creates.

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Philosophy + CS undergraduate Max Fan awarded National Science Foundation Graduate Research Fellowship

Max Fan, a junior Philosophy + CS major, has recently been awarded a National Science Foundation Graduate Research Fellowship   which will provide three years of financial support for his graduate studies. Fan will graduate this May and begin his PhD at Cornell University in the fall.  

While at the University of Illinois, and during a summer internship at NASA in 2023, Fan proposed and developed a new temporal logic semantics, a topic he first encountered in a class taught by philosophy professor Kohei Kishida.  

Temporal logic is a system of rules for representing and reasoning about propositions that change over time. In computer science, and at NASA, it’s used to specify when safety conditions should be triggered and to determine when a system is behaving nominally.  

“They don’t like bugs, and they don’t like things going wrong,” Fan said. “And so, the idea is you have all these requirements for how your rocket or how your satellite ought to behave. So, if your satellite does this, it should then do this, and these logics have a temporal nature.”  

For example, a temporal property might say “after the parachute deploys then the machine should decelerate.” Once the temporal property is specified, it is important to monitor the system to verify if it is behaving as expected. When a property is violated, the error should be handled as quickly as possible. 

Fan soon recognized that the standard temporal logic semantics that most people in computer science work with were not sufficient for NASA’s purposes. His coursework in philosophy helped him realize he could develop his own semantics that better captured the problem he was trying to solve.  

“My philosophical training allowed me to think outside the box and develop a different way that didn’t rely on the standard approaches,” he said. “I think there’s a reason why no one else thought of this before because people generally take the standard temporal logic semantics and do other things with it. But they don’t think about changing the actual ground rules.” 

He proposed a non-standard temporal logic semantics that will work better to solve NASA’s and other computer science problems. He has a paper in progress that he is hoping to submit to a conference soon.  

Fan also regularly seeks out perspectives on logic from researchers working in fields outside his major. He organizes a weekly lunch for those working on logic in computer science that also attracts professors and students in philosophy, electrical and computer engineering, and math. 

“The idea is that people who are interested in logic or logic adjacent come and talk about research and what people are working on,” he said. “It’s quite nice to see everyone and to hear about a different perspective. The math department has a certain kind of flavor of the research, which is a little different than the philosophy department, which is different than the flavor in the CS department.” 

Fan’s research interests include the intersection of computer science and philosophy, epistemology, and logic and truth, which is why he decided to major in philosophy + CS . In addition to professor Kishida, he also cites philosophy professor Jonathan Livengood, and computer science professor Talia Ringer as mentors during his time at the university.  

The NSF fellowship is not the first accolade he has received for his work. In his sophomore year, he received a Barry M. Goldwater scholarship for his potential to contribute to the advancement of research in the natural sciences, mathematics, or engineering. A fellow philosophy junior, Sylvia E, was also recently honored with a Goldwater Scholarship.   

His favorite courses have been the graduate seminars he has participated in, including the philosophy of logic graduate seminar, which he took twice, and the conceptual engineering seminar taught by philosophy professor Kevin Scharp. 

“I think all the courses [in philosophy] are actually very well taught. Like some departments are hit and miss. But every course here has been a hit. …The quality of instruction is quite high,” he said.