google phd data scientist

The Ultimate Guide to Become a Data Scientist at Google

Want to land a data scientist job at Google? This guide will walk you through every step of how to become a Google data scientist.

It’s no joke to become a data scientist at Google. Checking out their job requirements for a current post includes a minimum of a Masters degree (though a PhD is preferred), experience with statistical software, and two years of work experience in a data analysis related field.

In other words, it’s not a casual whim. If you want to be a data scientist at Google, it has to be something you really, really want, not something you trip into by accident because your other gig as a cat-sitter came to an end.

This guide will walk you through every step of how to become a Google data scientist. First, I’ll cover what Google looks for in all employees, and how you can demonstrate that. Then we’ll go over what you need to be proficient in data science, and how you can gain and show those proficiencies.

Finally, I’ll end with how to nail the ideal next step - the interviews. Google has multiple steps in their application process, and too many applicants think they can show up and be done there. At the interview, you’ll need to once again demonstrate all those key competencies, both technical and personal.

Let’s get started!

1. So you want a job at Google.

Google, the G in the FAANG group of technology companies , is a very prestigious employer. Many future data scientists want to work for Google purely through brand name recognition. Others know that the cutting edge analysis there will make it a fascinating workplace.

But it’s more than just the name. Google has cultivated a reputation for an incredible workplace culture, whether you want to be a data scientist at Google or something else. Motivated, passionate, curious people are actively recruited, which means that if you’re hired, you’re surrounded by a really cool workforce. The consequence is that Glassdoor summarizes the pros by saying that it’s the people that make Google a great place to work.

But the benefits are more tangible than just culture. As a Google employee, you’d enjoy a higher-than-average salary. Payscale lists the average as $118,705 per year across all jobs, with an average Google Data Scientist salary as $133,122. There’s also an abundance of perks, like free gourmet food and snacks and bringing pets to work, according to Inc .

How Google describes their interview process

Part of what makes Google such an attractive employer is the care they take in selecting their workforce. Their online guidelines on how to be hired at any role, not just a data scientist, can be summarized like this:

• Take a beat and reflect on what you want. They’re looking for people who have a clear view on what they’re good at and what they enjoy, and want a job in the overlap of those fields.
• Review what it’s like to work at Google. They want a team that meshes well together at every level, so they ask that you do some legwork and understand what it’s like to work there, from reading their blog , their YouTube channel , their team sites , and anywhere else you can read or learn about the experience of working at Google.
• Draft a new, custom resume from scratch. It’s interesting that they recommend leaning on data, no matter what job you’re applying for. The formula they recommend is: “accomplished [X] as measured by [Y], by doing [Z].”
• Apply to multiple roles, more than once. They specifically make a point of mentioning that many Googlers who ended up in their current role applied for something else, first. “[N]ot getting a role can often be a matter of timing, rather than a reflection of your skills or qualifications,” they write.
• Complete the assessments. Like a lot of other tech employers, Google asks that you complete a battery of tests no matter what the job. They include online assessments, short phone calls, project work, and in-depth interviews in the gamut you’ll complete. It’s good to understand that they built all these tests to be a way to measure how you solve problems. They’re not trying to catch you out, but understand how you think and work.

This set of guidelines helps you understand that if you do ultimately get picked to work as a Data Scientist at Google, it’s not by accident or luck: it’s because you’re a perfect fit.

2. How to demonstrate you have the personality Google wants

If you review the guidelines above, you’ll understand that at Google, they’re looking for a highly specific set of job-dependent skills, but they’re also looking for a perfect culture fit.

This section is not about how to pretend to have those skills and talents - if you don’t have them, you won’t be a good fit - but rather about how best to show them off in the application to Google. Especially as a Data Scientist, you might be tempted to focus on your technical qualifications. Getting hired at Google is a lot more than that.

Google is actually really open about their data-driven selection process and came to two conclusions: employees need to trust one another and believe in what they’re doing. That’s what brings success to any applicant.

The four traits Google is looking for

Here’s how you can showcase the traits that will reflect your future success at Google:

• Cooperation . The old lone-star ideal of the perfect employee has been shown to be a load of garbage. No matter what your job is, but especially as a data scientist at Google, you need to showcase your cooperative skills. Can you think of an example when you worked well in a team? How did you help? What was your contribution? Equally, was there ever a bad team you were part of? What was the cause?
• Resilience . There are a lot of questions that will have hard answers, as a Data Scientist at Google or any other job. Google is not looking for people who get the answer right on the first try, because that’s not realistic. They’re looking for workers who will keep going when the going gets tough. Can you think of a time when you struggled? How did you overcome that struggle? What other areas of mental toughness can you demonstrate?
• Belief in your mission . Google is looking for employees who will believe in what they’re doing. It’s worth understanding Google’s mission, and your interest in helping them achieve that, but you should also think of previous examples where you did work for a cause you truly believed in. When you think about previous places you worked for, try to figure out the why beyond a paycheck that kept you there.
• Dependability . Google wants to build teams that can rely on each other. To make sure you show this trait, think of times that you did what you said you would on time, or took on an additional burden to help out your team. They’re not looking for hotshots, but individuals who will help a team perform better as a whole. You should think of times that your involvement helped the bigger picture, not just you as a single player.

In short, when you’re writing your resume and applying to these roles, keep those four traits firmly front of mind and you’ll be sure to align with what Google is looking for. Remember, if you can’t think of any good examples from work, take examples from school or daily life. And if you’re really struggling, then it might just be the case that you’re not a good fit.

3. What Google is looking for in a Data Scientist

So far, we’ve covered generic Google application advice. While all of what I’ve written is relevant for data scientists at Google, this next section is going to go into Data Scientist-specific detail, which is the same for many other companies that want data scientists . Google has a base set of traits they look for in all employees - and an additional set of qualifications on top of that.

I checked out a couple of recent job listings of theirs to become a Data Scientist at Google in any field to translate what they’re looking for. Here’s the breakdown:

Hard skills/qualifications:

These are all the hard skills or experiences you’ll need to show in order to be considered a good fit as a Data Scientist at Google in any team.

• As a minimum, you’ll need a Masters degree in Statistics, Computer Science, or another Science. Some ask only for a Bachelors in a similar field, but since all these job listings say a PhD is preferred, it’s safe to assume a Masters (or extremely relevant job experience) is a must.
• You’ll also want at least some relevant work experience . They range between just saying “Experience” to asking for eight years. Due to the range, it makes sense to focus on relevance rather than years of work. Make sure your resume shows the most relevant details possible for the field, whether operations, engineering, or shopping.
• Strength in one statistical language , like R or Python. All “Data Scientist at Google” job listings ask for this.
• Strength in one database language , like SQL. All “Data Scientist at Google” job listings ask for this.
• Beyond that, there are other job-specific requirements that differ based on which data science role you’re applying to.

There’s currently an open Data Scientist, Business and Marketing job nicely demonstrating these requirements.

Soft skills/qualifications

These are all the softer skills or experiences you’ll need to show in order to be considered a good fit as a Data Scientist at Google. There’s a bit more creativity on offer here as to how you demonstrate them.

We’ll use the same job ad as earlier to showcase this.

• Example: “ gPTO partners closely with gTech’s Support, Professional Services, Product Management, and Engineering teams to innovate and simplify our Ads products and build the productivity tools ecosystem for gTech users.”

• Example: “ Leverage critical thinking and problem statement definition, decomposition, and problem solving to ensure efforts are focused on delivering impactful and actionable outcomes.”

• Examples: “Collaborate across cross-functional stakeholder teams, managing opportunities and challenges that improve processes and help stakeholders become more data savvy .”

Additional considerations

So far, this is all freely available on the job listings. But we can also get a sneak preview at the interview questions to understand what data science traits they specifically ask for at that stage to successful applicants. This blog post discussing questions for the Data Science SQL interview looks into what Google asks at the interview stage. If Google Data Scientists are being asked this at interview, you can bet it matters at every other stage, too.

Google’s Data Scientist questions break down into six categories. The top three are: Coding, Algorithms, and Statistics. However, even though business case questions are relatively low in frequency, it’s nearly 2x the average of other tech companies. It’s a clear focus for Google.

Check out our data science interview guide that includes 900+ real interview questions from 80 different companies in 2020 and 2021 - Data Science Interview Guide ​ .

4. How to get the skills a data scientist for Google should have

If you want a data scientist job at Google, you’ll need the education, the work experience, the coding skills, and the right personality. If you don’t have the education or work experience, it may be worth looking into alternate routes to becoming a data scientist at Google, such as these transition routes . We’ve also already covered that if you’re not the right personality type, then a job as a Google data scientist probably isn’t right.

Beyond that, there are things you can do to brush up on your hard skills. This section will focus on the best way to get the coding skills and the database proficiency that are absolute musts for data scientist jobs at Google.

Statistical language

The two most popular statistical languages, also referenced by name in Google Data Scientist job applications, are conveniently open source: R and Python. This means you don’t have to pay anything if you want to teach yourself.

I learned R partially through school, but also a large part on my own. I find it a fun language to use, and R is actively trying to get new users, so there is an absolute wealth of free and comprehensive information on how to learn R online. I’d recommend using Swirl(), which is a package in R that interactively helps you learn. I’d also suggest checking out the free textbooks such as R for Data Science . The RStudio blog is also a good place to stay up to date with new packages, datasets, and opportunities to learn. There are definitely better places to learn such skills for data science .

Python was a language I taught myself without too much difficulty. The basics are extremely easy to pick up. Like R, the creators and community of Python have a deeply vested interest in gaining more pythonistas, so there’s a huge quantity of amazing online resources to learn Python. I’d recommend beginning with the website, boot.dev .

If you are wondering how much python is required for a career in data science, check out our article on how much Python is required for data science .

There’s a lot of chatter about R versus Python. I prefer R for ease of use, but Python is great for machine learning. It depends on what you personally prefer. I recommend trying both and seeing which one is easiest and most fun for you to learn.

To truly learn a language (and to tick a box on the application) you should have a self-driven project. For example, I really started learning R when I found a set of numbers I cared about. I started learning Python when I wanted to use a bot to automate Instagram activity. Find something you care about, and learn about it with your language.

Database language

I’ll focus this section on SQL since that was the database language most often mentioned by job applications for becoming a Data Scientist at Google. Unlike R and Python, SQL is not open source, so there aren’t quite as many resources to learn it. Most tutorials are business-focused.

Because it’s such a business-focused language, I recommend you start with Google’s own resource, SQL for Data Analysis . Once you have the basics, the best way to continue is by pursuing the areas that will likely come up in the job.

Luckily for you, StrataScratch has a huge amalgamation of 500+ SQL interview questions from Google and other big tech companies. Start completing those, noting which areas are toughest for you. That will tell you where you should focus your efforts to stand out during the real interview.

5. How to show those data science skills off for Google

Assuming you’re a crack coder, you know everything there is to know about SQL, and you’re a shoo-in for the job, there’s still one big obstacle: presentation on your resume.

Once you get to the interview, of course, you can blow them away with your technical and analytical thinking. But before that, you need to persuade Google that you have what it takes to be a Data Scientist even from just the resume perspective.

Beyond a diploma and a few lines on your resume showing certificates, you have to showcase your technical skills. The best and most persuasive way to do so is with a portfolio. More than ever, Data Scientists are using Github as a way to give specific evidence of their coding background.

Many job listings for Google Data Scientists mention the need to demonstrate a passion and skill with analysis. Data Flair lists five potential options to use as Data Analytics projects , but be sure to use these for inspiration. To have a real chance, you should be inspired by something in your own life that you want to understand and analyze.

One possible obstacle is where to get data sources from - I recommend checking out The MockUp Blog by Tom Mock, the founder of Tidy Tuesday . It’s a great way to get familiarized with R, and to find good data sources.

Once you have thought of and hopefully done this passion project, think back to the original section where Google makes it clear the traits they value. How can you demonstrate your passion project fulfils those technical and personality characteristics that they’re looking for? Keep those traits front of mind as you describe the project, your motivations for doing it, and the outcomes.

6. Nail your data science interview at Google

If you’ve followed this guide so far, you hopefully have applied and received an interview invitation. That’s great - but it’s not the end goal. At the Google Data Scientist interview, your interviewers will be looking for different things: your personal attributes, your practical skills, and general interview etiquette. Let’s break it down.

Practical skills and example questions

You can practice the technical skills with any of the resources - there are plenty of places to look for the technical data science questions you’ll be asked. Even though you may feel confident, make sure you’ve brushed up on as many specific examples as you can find, so questions won’t catch you off guard. Even if you do perfectly well in the technical arena, Google is also looking for how well you think under pressure. The more practice you get, the smoother you’ll be.

Here’s a description and an example of each type of question asked at data science interviews. Where possible, I’ll include an actual example from Google, but since many of the questions at the FAANG group of companies are similar, I can replace with another company’s similar question if there isn’t one currently available for Google.

1. Coding: a) Definition: These are questions that require some sort of data manipulation (through code) to identify insights. b) Example from Google : Find the number of times the words 'bull' and 'bear' occur in the contents. We're counting the number of times the words occur so words like 'bullish' should not be included in our count. Output the word 'bull' and 'bear' along with the corresponding number of occurrences.

2. Algorithms: a) Definition: These need you to solve a mathematical problem using one of the programming languages. These questions involve a step-by-step process usually requiring adjustment or computation to produce an answer. b) Example: How would you count the number of occurrences of a letter in a word using Python? (Generic algorithm question)

3. Statistics: a) Definition: These need you to bone up on knowledge of statistical theory and associated principles. Google is testing you on founding theoretical principles which are used in data science processes. b) Example: What is the expectation of variance? ( Facebook )

4. Modeling: a) Definition: Questions related to machine learning and statistical modelling (regressions). They require the knowledge on how to use mathematical models and statistical assumptions to generate sample data and make predictions about real-world events. b) Example from Google : Why use feature selection? If two predictors are highly correlated, what is the effect on the coefficients in the logistic regression? What are the confidence intervals of the coefficients?

5. Business Case: a) Definition: Questions involving case studies as well as generic questions related to the business that would test a data science skill. b) Example from Google : How many cans of blue paint were sold in the United States last year?

6. Product: a) Definition: Questions related to evaluating the performance of a product/service through data. b) Example: If 70% of Facebook users on iOS use Instagram, but only 35% of Facebook users on Android use Instagram, how would you investigate the discrepancy? ( Facebook )

7. Technical: a) Definition: These questions ask about the explanation on various data science technical concepts. They’re similar to the coding ones, but require you to have more knowledge on the technology you’d be using at Google as a Data Scientist. b) Example: What is the difference between a linked list and an array? ( Amazon )

The main piece of feedback offered by Glassdoor’s info on Google’s Data Scientist interview is to understand the basics. Many candidates overthink, or panic into nervousness when they can’t immediately come up with an answer. The fundamentals are going to underpin every question you get.

Personal skills

Remember, Google wants passionate, collaborative, creative, driven people as their Data Scientists. Let that drive your answers. Even if it’s a technical question, try to demonstrate how you’d solve the problem while showcasing your collaboration skills, or explaining why you’d be driven to answer it in that way.

Basic interview skills

Definitely don’t forget the handshake, the follow-up, the eye contact and the confidence. But basic interview questions also includes items like:

• Pacing . As a Google Data Scientist interview, you’re going to have rounds and rounds of interviews - five in total, with just a lunch break between them. Speak slowly, drink water when you need to, and keep in mind it’s going to be a long day.
• Friendliness . You’re being tested on how well you fit into the team, as much as whether you have the right skills for the job. Google is looking for people who enjoy working with each other. Try to get into the mindset of enjoying the interviews and having fun with your interviewers.
• Good listening . It’s a fact that when people get nervous, they listen less well. Practice good active listening skills with your interviewers. This will help you better understand what you’re being asked, cut down on miscommunications, and build rapport with your interviewers. Good listening is a rare skill.

Many people dream of getting a data scientist job at Google - it’s an incredible opportunity to have a real impact on hundreds of millions of people in real life, work with an amazing company, and get incredible benefits like a hefty paycheck.

The main thing Google is looking for in its Data Scientists is that they’ll be a good fit for the job. Why do you want to do Data Science at Google? If it’s just for the brand name, or because you don’t know what else to do, it’s not enough. If you have to lie or exaggerate, you’re not a good fit. If you don’t get the job, it’s probably for the best, or not yet your time.

But you can be confident: If you have the skills, the prep, and the passion, you’ll be a shoo-in. As long as you nail the fundamentals, present your skills appropriately through the resume and interview process, and demonstrate how good a culture fit you are, you’ve got a one-way ticket for one of the best jobs in the world.

Also, check out our comprehensive guide " How to Become a Data Scientist from Scratch " which will take you through every necessary step to become a successful data scientist.

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Announcing the 2022 PhD Fellows

Sep 01, 2022

[[read-time]] min read

In 2009, Google created the PhD Fellowship Program to recognize and support graduate students who are doing exceptional research in Computer Science and related fields, and who are poised to shape the future of technology. Since our first awardee cohort 13 years ago, these PhD Fellowships have helped support 654 graduate students from around the world across Africa, Australia & New Zealand, East Asia, Europe, India, North America and Southeast Asia.

Over the past 14 award cycles, our PhD Fellows have made some incredible contributions to their fields, and today we're checking in with three of our past alumni.

• Flora Tasse — Head of CV/AR Research at Streem specializing in AI applied to Computer Graphics and Vision problems faced in AR/VR
• Minsuk Kahng — Assistant Professor of Computer Science at Oregon State University whose research focuses on designing and developing novel visual analytics tools for people to interpret and interact with machine learning systems that use massive datasets
• Nicolas Papernot — Assistant Professor of Computer Engineering and Computer Science at the University of Toronto whose research interests span the security and privacy of machine learning

What was your motivation to apply to the program?

Flora: I started my PhD with the mission to seize every opportunity to surround myself with the best in the field and broaden my horizons. I was on the lookout for Fellowships that could provide that, and help me make an impact in my area of research. When I heard about the Google PhD Fellowship, I was impressed with all the support that went well beyond the financial. I was initially hesitant to apply because it is such a prestigious program. Thankfully, I did submit my application and it is one the best things I have ever done for my career.

Minsuk: Receiving a Google PhD Fellowship is a great honor for computer science PhD students. I deeply appreciated that Google recognized my research. I was particularly interested in applying for Google’s Fellowship program because Google researchers have been actively conducting research on human-centered approaches to Artificial Intelligence (AI), which is something I’m passionate about. The program provided me with an exciting opportunity to interact with them.

Nicolas: At the time, there were very few people working on my research topic (adversarial examples) so I wanted to apply to the Google Fellowship to find mentors and colleagues to discuss my ideas with. The Fellowship was a great accelerator for my research because it allowed me to meet with a number of people who ended up shaping my understanding of machine learning. This increased the pace of my research and led me to discover new areas of research that I am passionate about.

What impact did the Google PhD Fellowship have on your career trajectory and on technology?

Flora: The Google PhD Fellowship was a turning point in my career. It not only validated the research work I was doing, but also gave me visibility and support that opened so many doors. Through this experience, I formed valuable collaborations and expanded my professional network which proved fruitful in building my career. Thanks to my internship at Google Zurich, I gained valuable insights into innovation and the productization of research. I currently apply my research skills at Streem, where we are making the phone's camera intelligent. Acquired by Streem, my start-up Selerio was building AI agents that could understand images/videos and augment them with relevant interactive objects. This technology made a tangible difference in remote collaboration between experts and consumers to solve product issues which was particularly impactful at the height of the Covid-19 pandemic.

Minsuk: The Fellowship has allowed me to have a wonderful career. Thanks to invaluable advice from my mentors at Google, I completed my PhD with a Dissertation Award from Georgia Tech. I have recently decided to join Google’s People + AI Research (PAIR) team after working as faculty for three years. I have been developing data visualization tools for people to interpret AI systems. Along with my colleagues at Google I’ve created and open-sourced GAN Lab , an interactive tool for people to learn the inner-workings of deep learning models. It significantly broadens people's education access to AI, as learners can use it just with their browsers without the need for specialized backend. I look forward to pursuing research that can help people everywhere.

Nicolas: The Google PhD Fellowship gave me a lot of freedom to pursue my own research ideas, spend time developing the CleverHans library , and collaborate with researchers at different universities and in other research communities. The opportunities I've had to work on differential privacy and machine learning with leading researchers at Google Brain were transformative to my career, and fundamental to bootstrapping my academic career at the University of Toronto and Vector Institute. During the program, I was able to implement privacy-preserving algorithms that are now used by product teams with lots of users. This was a great opportunity to have an immediate impact on technology. More generally, my research is by design seeking to understand the limitations of machine learning so that society can better trust it.

What advice do you have for current and future Google PhD Fellows?

Flora: Take advantage of the opportunities it provides, apply to Google internships, go to more conferences, collaborate and meet PhD Fellows in other fields. By becoming a Google PhD Fellow, you are joining a community of incredibly talented researchers and gaining influential mentors. As for the PhD, you will still go through the ups and downs of doctoral research. But it will be much easier as a Fellow. Stay the course. If you are an undergraduate considering a PhD pathway, invest energy and time in figuring out if there is a problem or a field that you care enough about to dedicate many years of your life to it.

Minsuk: My advice is to look for opportunities to cross the boundaries between disciplines. My work was made possible by collaborating with people across multiple research areas, such as information visualization, machine learning, human-computer interaction and databases. While research from different fields might seem unrelated at first, combinations of ideas can create unique research opportunities. Before starting my PhD, I conducted research on making recommendation algorithms more accurate, but found myself being much more motivated by different flavors of research. This experience led me to find my research direction and vigorously pursue it in my PhD.

Nicolas: I recommend that you do not optimize for short term rewards (like publishing papers) but instead focus on solving the problems that you find the most interesting. Research is often a random process and it is hard to predict what work will have an impact, so optimizing for short term rewards can quickly remove the “fun” out of doing research. While an undergraduate student, you have many opportunities to learn about topics that are diverse and possibly far away from the topic you will eventually choose to work on if you start a PhD. This breadth of knowledge will not only make you a more interesting person but help you in your research, because the most interesting research questions are often the ones that require an interdisciplinary approach to find an answer.

Announcing the 2022 Google PhD Fellows

Since 2009, the Google PhD Fellows have represented some of the best and brightest computer science researchers from around the globe, and we’re honored to support them as they make their mark on the world. Congratulations to all of this year’s awardees! See the complete list of Google PhD Fellowship recipients for 2022 .

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Google PhD Fellowship recipients

Previous years:, algorithms, optimizations and markets.

Brice Huang, Massachusetts Institute of Technology

Debajyoti Kar, Indian Institute of Science

Jamie Tucker-Foltz, Harvard University

Joakim Blikstad, KTH Royal Institute of Technology

Mahdieh Labani, Macquarie University

Rehema Hamis Mwawado, University of Rwanda

Uddalok Sarkar, Indian Statistical Institute Kolkata

Computational Neural and Cognitive Sciences

Gizem Özdil, École polytechnique fédérale de Lausanne

Sreejan Kumar, Princeton University

Bridget Chak, University of Chicago

Li-Wen Chiu, National Yang Ming Chiao Tung University

Md. Saiful Islam, University of Rochester

Rutendo Jakachira, Brown University

Tsai-Min Chen, National Taiwan University

Wenhao Gao, Massachusetts Institute of Technology

Human Computer Interaction

Brianna Wimer, University of Notre Dame

Emily Kuang, Rochester Institute of Technology

Eunkyung Jo, University of California - Irvine

Georgianna Lin, University of Toronto

Gustavo Pacheco Santiago, Universidad Nacional Autónoma de México

Marcelo Marques da Rocha, Universidade Federal Fluminense

Yulia Goldenberg, Ben Gurion University

Zixiong Su, The University of Tokyo

Machine Learning

Berivan Isik, Stanford University

Blake Bordelon, Harvard University

Cristhian Delgado Fajardo, University of Otago

Denish Azamuke, Makerere University

Fuzhao Xue, National University of Singapore

Heinrich Pieter van Deventer, University of Pretoria

Imane Araf, Mohammed VI Polytechnic University

Itamar Franco Salazar Reque, Pontificia Universidad Católica del Perú

Jihoon Tack, Korea Advanced Institute of Science and Technology

Julliet Chepngeno Kirui, Strathmore University

Krystal Dacey, Charles Sturt University

Laura Smith, University of California - Berkeley

Marcos Paulo Silva Gôlo, ​​Universidade de São Paulo

Melisa Yael Vinograd, Universidad de Buenos Aires

Miriam Rateike, Saarland University

Mitchell Wortsman, University of Washington

Natalia Gil Canto, Universidade Estadual de Campinas

Nicolás Esteban Valenzuela Figueroa, Universidad de Chile

Omprakash Chakraborty, Indian Institute of Technology Kharagpur

S. Durga, Indian Institute of Technology Bombay

Strato Angsoteng Bayitaa, C.K. Tedam University of Technology and Applied Sciences

Yiding Jiang, Carnegie Mellon University

Yifan Zhang, National University of Singapore

Machine Perception, Speech Technology and Computer Vision

Antoine Yang, National Institute for Research in Digital Science and Technology (Inria)

Astitva Srivastava, International Institute of Information Technology Hyderabad

Chen Yu, National University of Singapore

Ethan Tseng, Princeton University

Matheus Viana da Silva, Universidade Federal de São Carlos

Sunghwan Hong, Korea University

Sungyeon Kim, Pohang University of Science and Technology

Vincent Milimo Masilokwa Punabantu, University of Cape Town

Yanxi Li, The University of Sydney

Yosef Gandelsman, University of California - Berkeley

Ziqi Huang, Nanyang Technological University

Mobile Computing

Ke Sun, University of California - San Diego

Kyungjin Lee, Seoul National University

Natural Language Processing

Allahsera Auguste Tapo, Rochester Institute of Technology

Cheng-Han Chiang, National Taiwan University

Liunian Li, University of California - Los Angeles

Sarah Masud, Indraprastha Institute of Information Technology Delhi

Sumanth Doddapaneni, Indian Institute of Technology Madras

Zhiqing Sun, Carnegie Mellon University

Privacy and Security

Jiayuan Ye, National University of Singapore

Miranda Wei, University of Washington

Neha Jawalkar, Indian Institute of Science

Yihui Zeng, Arizona State University

Programming Technology and Software Engineering

Aaditya Naik, University of Pennsylvania

Thanh Le-Cong, The University of Melbourne

Quantum Computing

Diego Hernando Useche Reyes, Universidad Nacional de Colombia

Elies Gil-Fuster, Free University of Berlin

Juan David Nieto García, Universidade Estadual de Campinas

Lia Yeh, University of Oxford

Structured Data and Database Management

Zezhou Huang, Columbia University

Systems and Networking

Jennifer Switzer, University of California - San Diego

Jiaxin Lin, University of Texas at Austin

Jinhyung Koo, Daegu Gyeongbuk Institute of Science & Technology

Maurine Chepkoech, University of Cape Town

Qinghao Hu, Nanyang Technological University

Anjali Gupta, Indian Institute of Technology Delhi

Shunhua Jiang, Columbia University

Shyam Sivasathya Narayanan, Massachusetts Institute of Technology

Venkata Naga Sreenivasulu Karnati, Indian Institute of Science

Yang P. Liu, Stanford University

Aditi Jha, Princeton University

Klavdia Zemlianova, New York University

Devon Jarvis, University of the Witwatersrand

Emily Schwenger, Albert Einstein College of Medicine

Reihaneh Torkzadehmahani, TU Munich

Xin Liu, University of Washington

Qian Niu, Kyoto University

Karthik Mahadevan, University of Toronto

Meena Muralikumar, University of Washington

Nika Nour, University of California - Irvine

Pang Suwanaposee, University of Canterbury

Ryan Louie, Northwestern University

Tiffany Li, University of Illinois - Urbana-Champaign

Zhongyi Zhou, The University of Tokyo

Eunji Kim, Seoul National University

Hayeon Lee, Korea Advanced Institute of Science and Technology

Julius von Kügelgen, Max Planck Institute for Intelligent Systems

Kaloma Usman Majikumna, Euromed University of Fes, Morocco

Lily Xu, Harvard University

Maksym Andriushchenko, EPFL

Pierre Marion, Fondation Sciences Mathématiques de Paris

Shashank Rajput, University of Wisconsin - Madison

Sheheryar Zaidi, University of Oxford

Sindy Löwe, University of Amsterdam

Tan Wang, Nanyang Technological University

Xiaobo Xia, University of Sydney

Yixin Liu, Monash University

Efthymios Tzinis, University of Illinois - Urbana-Champaign

Elizabeth Ndunge Mutua, Strathmore University

Haipeng Xiong, National University of Singapore

Jianyuan Guo, University of Sydney

Jiawei Ren, Nanyang Technological University

Juhong Min, Pohang University of Science and Technology

Liliane Momeni, University of Oxford

Qianqian Wang, Cornell University

Shuo Yang, University of Technology Sydney

Tahir Javed, Indian Institute of Technology Madras

Wei-Ting Chen, National Taiwan University

Yuming Jiang. Nanyang Technological University

Yu-Ying Yeh, University of California - San Diego

Binbin Xie, University of Massachusetts - Amherst

Clara Isabel Meister, ETH Zurich

Julia Mendelsohn, University of Michigan

Sachin Kumar, Carnegie Mellon University

Saley Vishal Vivek, Indian Institute of Technology Delhi

Swarnadeep Saha, University of North Carolina - Chapel Hill

Shuyi Wang, The University of Queensland

Thong Nguyen, National University of Singapore

Ussen Kimanuka, Pan African University Institute For Basic Sciences, Technology and Innovation

Amy Elizabeth Gooden, University Kwazulu-Natal

Promise Ricardo Agbedanu, University of Rwanda

Alexander Bienstock, New York University

Daniel De Almeida Braga, Universite Rennes 1

Gaurang Bansal, National University of Singapore

Nicolas Huaman Groschopf, Leibniz University of Hanover

Simon Spies, Max Planck Institute for Software Systems

Ilkwon Byun, Seoul National University

Margaret Fortman, University of Wisconsin - Madison

Oscar Higgott, University College London

Sam Gunn, University of California - Berkeley

Recommender Systems

Jessie J. Smith, University of Colorado - Boulder

Wenjie Wang, National University of Singapore

Nikolaos Tziavelis, Northeastern University

Humphrey Owuor Otieno, University of Cape Town

Jiarong Xing, Rice University

Shweta Pandey, Indian Institute of Science

Sunil Kumar, Indraprastha Institute of Information Technology Delhi

Yang Zhou, Harvard University

Yujeong Choi, Korea Advanced Institute of Science and Technology

Daniel Mutembesa, Makerere University

Kevin Tian, Stanford University

Prerona Chatterjee, Tata Institute of Fundamental Research

Sampson Wong, The University of Sydney

Santhoshini Velusamy, Harvard University

Sruthi Gorantla, Indian Institute of Science

Wenshuo Guo, University of California, Berkeley

Malvern Madondo, Emory University

Steffen Schneider, University of Tübingen

Nalini Singh, Massachusetts Institute of Technology

Roman Koshkin, Okinawa Institute of Science and Technology

Vishwali Mhasawade, New York University

Anupriya Tuli, Indraprastha Institute of Information Technology - Delhi

Chia-Hsing Chiu, National Taiwan University of Science and Technology

Dennis Makafui Dogbey, University of Cape Town

George Hope Chidziwisano, Michigan State University

Harmanpreet Kaur, University of Michigan

Srishti Palani, University of California, San Diego

Amir-Hossein Karimi, Max Planck Institute for Intelligent Systems

Anastasia Koloskova, EPFL, Lausanne

Anirudh Goyal, University of Montreal

Daniel Kang, Stanford University

Elena Fillola, University of Bristol

Emmanuel Chinyere Echeonwu, Nnamdi Azikiwe University, Nigeria

Gal Yona, Weizmann Institute of Science

Hae Beom Lee, KAIST

Jaekyeom Kim, Seoul National University

Logan Engstrom, Massachusetts Institute of Technology

Piyushi Manupriya, Indian Institute of Technology - Hyderabad

Qinbin Li, National University of Singapore

Shen Li, National University of Singapore

Shubhada Agrawal, Tata Institute of Fundamental Research

Theekshana Dissanayake, Queensland University of Technology

Tianyuan Jin, National University of Singapore

Yun Li, The University of New South Wales

Andrea Burns, Boston University

Fangzhou Hong, Nanyang Technological University

Hai-Bin Wu, National Taiwan University

Jogendra Nath Kundu, Indian Institute of Science

Kelvin C.K. Chan, Nanyang Technological University

Sanghyun Woo, KAIST

Sara El-Ateif, National School For Computer Science (ENSIAS)

Soo Ye Kim, KAIST

Tewodros Amberbir Habtegebrial, Technical University of Kaiserslautern

Xinlong Wang, The University of Adelaide

Xueting Li, University of California, Merced

Zhiqin Chen, Simon Fraser University

Byungjin Jun, Northwestern University

Soundarya Ramesh, National University of Singapore

Derguene Mbaye, Universite Cheikh Anta Diop

Eya Hammami, LARODEC

Haoyue Shi, Toyota Technological Institute at Chicago

Kalpesh Krishna, University of Massachusetts Amherst

Peter Hase, University of North Carolina at Chapel Hill

Rochelle Choenni, University of Amsterdam

Chandan Kumar, Indian Institute of Technology - Kharagpur

Kevin Loughlin, University of Michigan

Teodora Baluta, National University of Singapore

Yuqing Zhu, University of California, Santa Barbara

Aishwarya Sivaraman, University of California, Los Angeles

Jenna Wise, Carnegie Mellon University

Alicja Dutkiewicz, Leiden University

Hsin-Yuan Huang, California Institute of Technology

Mykyta Onizhuk, The University of Chicago

Sayantan Chakraborty, Tata Institute of Fundamental Research

Brian Kundinger, Duke University

Yiru Chen, Columbia University

Yu Meng, University of Illinois at Urbana-Champaign

Zheng Wang, Nanyang Technological University

Aishwariya Chakraborty, Indian Institute of Technology - Kharagpur

Alireza Farshin, KTH Royal Institute of Technology

Erika Hunhoff, University of Colorado Boulder

S. VenkataKeerthy, Indian Institute of Technology - Hyderabad

Soroush Ghodrati, University of California, San Diego

Yejin Lee, Seoul National University

Jan van den Brand, KTH Royal Institute of Technology

Mahsa Derakhshan, University of Maryland, College Park

Sidhanth Mohanty, University of California, Berkeley

Computational Neuroscience

Connor Brennan, University of Pennsylvania

Abdelkareem Bedri, Carnegie Mellon University

Brendan David-John, University of Florida

Hiromu Yakura, University of Tsukuba

Manaswi Saha, University of Washington

Muratcan Cicek, University of California, Santa Cruz

Prashan Madumal, University of Melbourne

Alon Brutzkus, Tel Aviv University

Chin-Wei Huang, Universite de Montreal

Eli Sherman, Johns Hopkins University

Esther Rolf, University of California, Berkeley

Imke Mayer, Fondation Sciences Mathématique de Paris

Jean Michel Sarr, Cheikh Anta Diop University

Lei Bai, University of New South Wales

Nontawat Charoenphakdee, The University of Tokyo

Preetum Nakkiran, Harvard University

Sravanti Addepalli, Indian Institute of Science

Taesik Gong, Korea Advanced Institute of Science and Technology

Vihari Piratla, Indian Institute of Technology - Bombay

Vishakha Patil, Indian Institute of Science

Wilson Tsakane Mongwe, University of Johannesburg

Xinshi Chen, Georgia Institute of Technology

Yadan Luo, University of Queensland

Benjamin van Niekerk, University of Stellenbosch

Eric Heiden, University of Southern California

Gyeongsik Moon, Seoul National University

Hou-Ning Hu, National Tsing Hua University

Nan Wu, New York University

Shaoshuai Shi, The Chinese University of Hong Kong

Yifan Liu, University of Adelaide

Yu Wu, University of Technology Sydney

Zhengqi Li, Cornell University

Xiaofan Zhang, University of Illinois at Urbana-Champaign

Anjalie Field, Carnegie Mellon University

Mingda Chen, Toyota Technological Institute at Chicago

Shang-Yu Su, National Taiwan University

Yanai Elazar, Bar-Ilan

Julien Gamba, Universidad Carlos III de Madrid

Shuwen Deng, Yale University

Yunusa Simpa Abdulsalm, Mohammed VI Polytechnic University

Adriana Sejfia, University of Southern California

John Cyphert, University of Wisconsin-Madison

Amira Abbas, University of KwaZulu-Natal

Mozafari Ghoraba Fereshte, EPFL

Yanqing Peng, University of Utah

Huynh Nguyen Van, University of Technology Sydney

Michael Sammler, Saarland University, MPI-SWS

Sihang Liu, University of Virginia

Yun-Zhan Cai, National Cheng Kung University

Aidasadat Mousavifar, EPFL Ecole Polytechnique Fédérale de Lausanne

Peilin Zhong, Columbia University

Siddharth Bhandari, Tata Institute of Fundamental Research

Soheil Behnezhad, University of Maryland at College Park

Zhe Feng, Harvard University

Caroline Haimerl, New York University

Mai Gamal, German University in Cairo

Catalin Voss, Stanford university

Hua Hua, Australian National University

Zhanna Sarsenbayeva, University of Melbourne

Abdulsalam Ometere Latifat, African University of Science and Technology Abuja

Adji Bousso Dieng, Columbia University

Anshul Mittal, IIT Delhi

Blake Woodworth, Toyota Technological Institute at Chicago

Diana Cai, Princeton University

Francesco Locatello, ETH Zurich

Ihsane Gryech, International University Of Rabat, Morocco

Jaemin Yoo, Seoul National University

Maruan Al-Shedivat, Carnegie Mellon University

Ousseynou Mbaye, Alioune Diop University of Bambey

Rendani Mbuvha, University of Johannesburg

Shibani Santurkar, Massachusetts Institute of Technology

Takashi Ishida, University of Tokyo

Chenxi Liu, Johns Hopkins University

Kayode Kolawole Olaleye, Stellenbosch University

Ruohan Gao, The University of Texas at Austin

Tiancheng Sun, University of California San Diego

Xuanyi Dong, University of Technology Sydney

Yu Liu, Chinese University of Hong Kong

Zhi Tian, University of Adelaide

Naoki Kimura, University of Tokyo

Abigail See, Stanford University

Ananya Sai B, IIT Madras

Byeongchang Kim, Seoul National University

Daniel Patrick Fried, UC Berkeley

Hao Peng, University of Washington

Reinald Kim Amplayo, University of Edinburgh

Sungjoon Park, Korea Advanced Institute of Science and Technology

Ajith Suresh, Indian Institute of Science

Itsaka Rakotonirina, Inria Nancy

Milad Nasr, University of Massachusetts Amherst

Sarah Ann Scheffler, Boston University

Caroline Lemieux, UC Berkeley

Conrad Watt, University of Cambridge

Umang Mathur, University of Illinois at Urbana-Champaign

Amy Greene, Massachusetts Institute of Technology

Leonard Wossnig, University College London

Yuan Su, University of Maryland at College Park

Amir Gilad, Tel Aviv University

Nofar Carmeli, Technion

Zhuoyue Zhao, University of Utah

Chinmay Kulkarni, University of Utah

Nicolai Oswald, University of Edinburgh

Saksham Agarwal, Cornell University

Emmanouil Zampetakis, Massachusetts Institute of Technology

Manuela Fischer, ETH Zurich

Pranjal Dutta, Chennai Mathematical Institute

Thodoris Lykouris, Cornell University

Yuan Deng, Duke University

Ella Batty, Columbia University

Neha Spenta Wadia, University of California - Berkeley

Reuben Feinman, New York University

Human-Computer Interaction

Gierad Laput, Carnegie Mellon University

Mike Schaekermann, University of Waterloo

Minsuk (Brian) Kahng, Georgia Institute of Technology

Niels van Berkel, The University of Melbourne

Siqi Wu, Australian National University

Xiang Zhang, The University of New South Wales

Abhijeet Awasthi, Indian Institute of Technology - Bombay

Aditi Raghunathan, Stanford University

Futoshi Futami, University of Tokyo

Lin Chen, Yale University

Qian Yu, University of Southern California

Ravid Shwartz-Ziv, Hebrew University

Shuai Li, Chinese University of Hong Kong

Shuang Liu, University of California - San Diego

Stephen Tu, University of California - Berkeley

Steven James, University of the Witwatersrand

Xinchen Yan, University of Michigan

Zelda Mariet, Massachusetts Institute of Technology

Machine Perception, Speech Technology, and Computer Vision

Antoine Miech, INRIA

Arsha Nagrani, University of Oxford

Arulkumar S, Indian Institute of Technology - Madras

Joseph Redmon, University of Washington

Raymond Yeh, University of Illinois - Urbana-Champaign

Shanmukha Ramakrishna Vedantam, Georgia Institute of Technology

Lili Wei, Hong Kong University of Science & Technology

Rizanne Elbakly, Egypt-Japan University of Science and Technology

Shilin Zhu, University of California - San Diego

Anne Cocos, University of Pennsylvania

Hongwei Wang, Shanghai Jiao Tong University

Jonathan Herzig, Tel Aviv University

Rotem Dror, Technion - Israel Institute of Technology

Shikhar Vashishth, Indian Institute of Science - Bangalore

Yang Liu, University of Edinburgh

Yoon Kim, Harvard University

Zhehuai Chen, Shanghai Jiao Tong University

Imane khaouja, Université Internationale de Rabat

Aayush Jain, University of California - Los Angeles

Gowtham Kaki, Purdue University

Joseph Benedict Nyansiro, University of Dar es Salaam

Reyhaneh Jabbarvand, University of California - Irvine

Victor Lanvin, Fondation Sciences Mathématiques de Paris

Erika Ye, California Institute of Technology

Lingjiao Chen, University of Wisconsin - Madison

Andrea Lattuada, ETH Zurich

Chen Sun, Tsinghua University

Lana Josipovic, EPFL

Michael Schaarschmidt, University of Cambridge

Rachee Singh, University of Massachusetts - Amherst

Stephen Mallon, The University of Sydney

Chiu Wai Sam Wong, University of California, Berkeley

Eric Balkanski, Harvard University

Haifeng Xu, University of Southern California

Motahhare Eslami, University of Illinois, Urbana-Champaign

Sarah D'Angelo, Northwestern University

Sarah Mcroberts, University of Minnesota - Twin Cities

Sarah Webber, The University of Melbourne

Aude Genevay, Fondation Sciences Mathématiques de Paris

Dustin Tran, Columbia University

Jamie Hayes, University College London

Jin-Hwa Kim, Seoul National University

Ling Luo, The University of Sydney

Martin Arjovsky, New York University

Sayak Ray Chowdhury, Indian Institute of Science

Song Zuo, Tsinghua University

Taco Cohen, University of Amsterdam

Yuhuai Wu, University of Toronto

Yunhe Wang, Peking University

Yunye Gong, Cornell University

Avijit Dasgupta, International Institute of Information Technology - Hyderabad

Franziska Müller, Saarland University - Saarbrücken GSCS and Max Planck Institute for Informatics

George Trigeorgis, Imperial College London

Iro Armeni, Stanford University

Saining Xie, University of California, San Diego

Yu-Chuan Su, University of Texas, Austin

Sangeun Oh, Korea Advanced Institute of Science and Technology

Shuo Yang, Shanghai Jiao Tong University

Bidisha Samanta, Indian Institute of Technology Kharagpur

Ekaterina Vylomova, The University of Melbourne

Jianpeng Cheng, The University of Edinburgh

Kevin Clark, Stanford University

Meng Zhang, Tsinghua University

Preksha Nama, Indian Institute of Technology Madras

Tim Rocktaschel, University College London

Romain Gay, ENS - École Normale Supérieure

Xi He, Duke University

Yupeng Zhang, University of Maryland, College Park

Programming Languages, Algorithms and Software Engineering

Christoffer Quist Adamsen, Aarhus University

Muhammad Ali Gulzar, University of California, Los Angeles

Oded Padon, Tel-Aviv University

Amir Shaikhha, EPFL CS

Jingbo Shang, University of Illinois, Urbana-Champaign

Ahmed M. Said Mohamed Tawfik Issa, Georgia Institute of Technology

Khanh Nguyen, University of California, Irvine

Radhika Mittal, University of California, Berkeley

Ryan Beckett, Princeton University

Samaneh Movassaghi, Australian National University

Google Australia PhD Fellowships

Chitra Javali, Security, The University of New South Wales

Dana McKay, Human Computer Interaction, The University of Melbourne

Kwan Hui Lim, Machine Learning, The University of Melbourne

Weitao Xu, Machine Perception, The University of Queensland

Google East Asia PhD Fellowships

Chungkuk YOO, Mobile Computing, Korea Advanced Institute of Science and Technology

Hong ZHANG, Systems and Networking, The Hong Kong University of Science and Technology

Quanming YAO, Machine Learning, The Hong Kong University of Science and Technology

Tian TAN, Speech Technology, Shanghai Jiao Tong University

Woosang LIM, Machine Learning, Korea Advanced Institute of Science and Technology

Ying CHEN, Systems and Networking, Tsinghua University

Google India PhD Fellowships

Arpita Biswas, Algorithms, Indian Institute of Science

Aniruddha Singh Kushwaha, Networking, Indian Institute of Technology Bombay

Anirban Santara, Machine Learning, Indian Institute of Technology Kharagpur

Gurunath Reddy, Speech Technology, Indian Institute of Technology Kharagpur

Google North America, Europe and the Middle East PhD Fellowships

Cameron, Po-Hsuan Chen, Computational Neuroscience, Princeton University

Grace Lindsay, Computational Neuroscience, Columbia University

Martino Sorbaro Sindaci, Computational Neuroscience, The University of Edinburgh

Koki Nagano, Human-Computer Interaction, University of Southern California

Arvind Satyanarayan, Human-Computer Interaction, Stanford University

Amy Xian Zhang, Human-Computer Interaction, Massachusetts Institute of Technology

Olivier Bachem, Machine Learning, Swiss Federal Institute of Technology Zurich

Tianqi Chen, Machine Learning, University of Washington

Emily Denton, Machine Learning, New York University

Yves-Laurent Kom Samo, Machine Learning, University of Oxford

Daniel Jaymin Mankowitz, Machine Learning, Technion - Israel Institute of Technology

Lucas Maystre , Machine Learning, École Polytechnique Fédérale de Lausanne

Arvind Neelakantan, Machine Learning, University of Massachusetts, Amherst

Ludwig Schmidt, Machine Learning, Massachusetts Institute of Technology

Shandian Zhe, Machine Learning, Purdue University, West Lafayette

Eugen Beck, Machine Perception, RWTH Aachen University

Yu-Wei Chao, Machine Perception, University of Michigan, Ann Arbor

Wei Liu, Machine Perception, University of North Carolina at Chapel Hill

Aron Monszpart, Machine Perception, University College London

Thomas Schoeps, Machine Perception, Swiss Federal Institute of Technology Zurich

Chia-Yin Tsai, Machine Perception, Carnegie Mellon University

Hossein Esfandiari, Market Algorithms, University of Maryland, College Park

Sandy Heydrich, Market Algorithms, Saarland University - Saarbrucken GSCS

Rad Niazadeh, Market Algorithms, Cornell University

Sadra Yazdanbod, Market Algorithms, Georgia Institute of Technology

Lei Kang, Mobile Computing, University of Wisconsin

Tauhidur Rahman, Mobile Computing, Cornell University

Yuhao Zhu, Mobile Computing, University of Texas, Austin

Tamer Alkhouli, Natural Language Processing, RWTH Aachen University

Jose Camacho Collados, Natural Language Processing, Sapienza - Università di Roma

Kartik Nayak, Privacy and Security, University of Maryland, College Park

Nicolas Papernot, Privacy and Security, Pennsylvania State University

Damian Vizar, Privacy and Security, École Polytechnique Fédérale de Lausanne

Xi Wu, Privacy and Security, University of Wisconsin

Marcelo Sousa, Programming Languages and Software Engineering, University of Oxford

Xiang Ren, Structured Data and Database Management, University of Illinois, Urbana-Champaign

Andrew Crotty, Systems and Networking, Brown University

Ilias Marinos, Systems and Networking, University of Cambridge

Kay Ousterhout, Systems and Networking, University of California, Berkeley

Bahar Salehi, Natural Language Processing, University of Melbourne

Siqi Liu, Computational Neuroscience, University of Sydney

Qian Ge, Systems, University of New South Wales

Bo Xin, Artificial Intelligence, Peking University

Xingyu Zeng, Computer Vision, The Chinese University of Hong Kong

Suining He, Mobile Computing, The Hong Kong University of Science and Technology

Zhenzhe Zheng, Mobile Networking, Shanghai Jiao Tong University

Jinpeng Wang, Natural Language Processing, Peking University

Zijia Lin, Search and Information Retrieval, Tsinghua University

Shinae Woo, Networking and Distributed Systems, Korea Advanced Institute of Science and Technology

Jungdam Won, Robotics, Seoul National University

Palash Dey, Algorithms, Indian Institute of Science

Avisek Lahiri, Machine Perception, Indian Institute of Technology Kharagpur

Malavika Samak, Programming Languages and Software Engineering, Indian Institute of Science

Google Europe and the Middle East PhD Fellowships

Heike Adel, Natural Language Processing, University of Munich

Thang Bui, Speech Technology, University of Cambridge

Victoria Caparrós Cabezas, Distributed Systems, Swiss Federal Institute of Technology Zurich

Nadav Cohen, Machine Learning, The Hebrew University of Jerusalem

Josip Djolonga, Probabilistic Inference, Swiss Federal Institute of Technology Zurich

Jakob Julian Engel, Computer Vision, Technische Universität München

Nikola Gvozdiev, Computer Networking, University College London

Felix Hill, Language Understanding, University of Cambridge

Durk Kingma, Deep Learning, University of Amsterdam

Massimo Nicosia, Statistical Natural Language Processing, University of Trento

George Prekas, Operating Systems, École Polytechnique Fédérale de Lausanne

Roman Prutkin, Graph Algorithms, Karlsruhe Institute of Technology

Siva Reddy, Multilingual Semantic Parsing, The University of Edinburgh

Immanuel Trummer, Structured Data Analysis, École Polytechnique Fédérale de Lausanne

Margarita Vald, Security, Tel Aviv University

Google United States/Canada PhD Fellowships

Waleed Ammar, Natural Language Processing, Carnegie Mellon University

Justin Meza, Systems Reliability, Carnegie Mellon University

Nick Arnosti, Market Algorithms, Stanford University

Osbert Bastani, Programming Languages, Stanford University

Saurabh Gupta, Computer Vision, University of California, Berkeley

Masoud Moshref Javadi, Computer Networking, University of Southern California

Muhammad Naveed, Security, University of Illinois at Urbana-Champaign

Aaron Parks, Mobile Networking, University of Washington

Kyle Rector, Human Computer Interaction, University of Washington

Riley Spahn, Privacy, Columbia University

Yun Teng, Computer Graphics, University of California, Santa Barbara

Carl Vondrick, Machine Perception,, Massachusetts Institute of Technology

Xiaolan Wang, Structured Data, University of Massachusetts Amherst

Tan Zhang, Mobile Systems, University of Wisconsin-Madison

Wojciech Zaremba, Machine Learning, New York University

Guosheng Lin, Machine Perception, University of Adelaide

Kellie Webster, Natural Language Processing, University of Sydney

Internships

Internships in business, engineering and technology, and more

You can explore all open internships on the Google Careers site.

Our interns

#GoogleInterns work across Google, including being part of various teams like software engineering, business, user experience, and more. With internships across the globe, we offer many opportunities to grow with us and help create products and services used by billions. Come help us build for everyone.

Browse our internships

The internships below are not exhaustive, and may or may not be currently available, but provide a taste of the various internships Google offers.

Showing 9 results

Business Internships

Business internships include multiple teams and roles within the business world at Google. Available outside of the united States, the internship is for undergraduate and graduate students with qualifications and application dates varying by location.

STEP Internship

STEP (Student Training in Engineering Program) is a development project that is focused on students that have a passion for technology. Requirements and application dates vary location.

Software Engineering Internship

Software engineering internships are available throughout the globe to undergraduate and graduate/PhD students, with rolling application dates (depending on location). Our interns have a broad set of technical skills, enable them to tackle some of technology's greatest challenges.

Associate Product Manager Internship

Our interns bridge technical and business worlds, designing technology with engineers and then zooming out of lead matrix teams such as Sales, Marketing, and Finance, to name a few. The internship is available globally, with varying requirements and application dates.

Legal Internship

Offered in certain countries outside of North America, the Legal internship is open to students majoring or specializing in legal studies. Applications generally open in October.

BOLD Internship

BOLD interns join teams across Sales, Marketing, and People Operations to identify challenges, collaborate on building solutions, and drive meaningful change for clients and users - all while developing skills and building careers. Applications open in October for rising undergraduate seniors.

MBA Internship

Our MBA internships are offered throughout the globe, and interns are able to put their education to use on day one. Available to students currently enrolled in a MBA program (with specific rquirements tied to the internship location, and applications open in September and October).

Korean Veteran Business Internship

Veteran Business Internship is designed for students who are direct descendants of Korean veterans. It is a 6-month upskilling program and includes multiple teams and roles in the business world at Google.

Hardware Engineering Internship

As a Hardware Engineering Intern, you will work on our core Consumer Hardware products. The teams you work with design, develop, and deploy next generation consumer hardware while ensuring that this equipment is reliable.

Google Internship FAQs

Want to learn more about internships at Google? This collection shares some of the most common questions we get from across the globe (for the best info on particular roles, search our jobs page and check each role’s job description). Want more help to prepare? Head to our Google Students YouTube page and find our Virtual Career Fair, tips, info, and more.

You might also like

SCHOLARSHIP

Generation Google Scholarship (APAC)

Designed to help students pursuing computer science degrees excel in technology and become leaders in the field. We strongly encourage women to apply.

APPRENTICESHIP

Apprenticeships

Apprenticeships join different teams to gain practical skills while at Google, and student towards an externally-recognized qualification.

We've curated good stuff like playlists, technical development resources, and other material to help you be your best

Phd In Data Science Salaries: What You Can Expect To Earn

A PhD in data science qualifies you for some of the most prestigious and high-paying jobs in tech. But exact salaries can vary significantly based on your specialization, industry, location and experience. Understanding the earning potential can help you evaluate if a PhD is worthwhile.

If you’re short on time, here’s the key takeaway: Data scientists with a PhD earn median salaries between $130,000 to $165,000, with top earners making $200,000 or more annually .

Average Salary Ranges for Data Science PhDs

When it comes to pursuing a PhD in Data Science, one of the key factors that potential students consider is the earning potential. Data Science is a rapidly growing field that offers lucrative career opportunities, and the salaries for professionals with a PhD in Data Science can be quite impressive.

National median salaries

The national median salary for individuals with a PhD in Data Science is around $120,000 per year. This figure represents the midpoint of the salary range, with some professionals earning significantly higher salaries based on their level of experience, expertise, and the industry they work in.

According to recent studies, data scientists with a PhD can earn anywhere between $90,000 to $180,000 per year, depending on various factors such as location, industry, and company size.

Salaries by major cities and regions

Salaries for data science PhDs can vary significantly based on the city or region in which you work. For example, major tech hubs like San Francisco, New York City, and Seattle tend to offer higher salaries due to the high demand for data scientists and the cost of living in these areas.

On the other hand, cities with a lower cost of living may offer slightly lower salaries.

According to a survey conducted by Glassdoor , the average salary for data scientists with a PhD in San Francisco is around $145,000 per year, while in New York City, it’s approximately $135,000 per year.

In contrast, in cities like Atlanta or Austin, the average salary might be around $120,000 per year.

Variance across industries and companies

The industry and company you work for also play a significant role in determining your salary as a data science PhD. Industries such as finance, healthcare, and technology tend to offer higher salaries due to the high demand for data-driven insights and analytics.

Big tech companies like Google, Facebook, and Amazon are known for offering competitive salaries to data science PhDs. On the other hand, start-ups and smaller companies may offer slightly lower salaries but provide other perks like equity or flexible work arrangements.

According to a study published by PayScale , the average salary for data science PhDs in the finance industry is around $150,000 per year, while in the healthcare industry, it’s approximately $140,000 per year.

However, it’s important to note that these figures can vary based on factors such as location, experience, and the specific role within the industry.

Factors That Influence Salary

Academic focus or specialization.

One of the key factors that influence the salary of a PhD in Data Science is their academic focus or specialization. Data Science is a vast field with various sub-disciplines, such as machine learning, artificial intelligence, big data analytics, and more.

Specializing in a high-demand area can significantly increase earning potential. For example, professionals with expertise in machine learning algorithms and predictive modeling are in high demand and can command higher salaries.

Industry and type of employer

The industry and type of employer also play a crucial role in determining the salary of a PhD in Data Science. Different industries have different demands for data scientists, and some industries, such as finance, healthcare, and technology, tend to offer higher salaries due to the complexity and volume of data they deal with.

Additionally, working for large companies or research institutions may lead to higher salaries compared to smaller organizations.

Cost of living and location

The cost of living and location can have a significant impact on the salary of a PhD in Data Science. Salaries can vary greatly depending on the region and city where the professional is employed. For instance, data scientists working in cities with a high cost of living, such as San Francisco or New York, may earn higher salaries to offset the higher expenses.

On the other hand, data scientists working in smaller cities or regions with a lower cost of living may have a lower salary.

Years of experience

Experience is another crucial factor that influences the salary of a PhD in Data Science. As professionals gain more experience and expertise in the field, their earning potential tends to increase. Data scientists with several years of experience may have a higher salary compared to those who are just starting their careers.

Additionally, professionals who have a track record of successful projects and achievements may be able to negotiate higher salaries.

Roles and Titles for PhDs

Obtaining a PhD in Data Science opens up a wide range of career opportunities. Let’s explore some of the roles and titles that PhD holders in this field can expect to pursue.

Data Scientist

The role of a data scientist is highly sought after in today’s data-driven world. Data scientists are responsible for collecting, analyzing, and interpreting large sets of complex data to uncover valuable insights and drive decision-making.

They use various statistical and machine learning techniques to develop models and algorithms that can predict future trends and patterns. According to Glassdoor , the average salary for a data scientist with a PhD is around $120,000 per year.

Research Scientist

A research scientist with a PhD in Data Science focuses on conducting advanced research and development in the field. They work on cutting-edge projects, exploring new algorithms, and developing innovative solutions to complex data problems.

Research scientists often publish their findings in academic journals and contribute to the advancement of the field. The average salary for a research scientist can range from $90,000 to $150,000 per year, depending on experience and industry.

Machine Learning Engineer

Machine learning engineers are responsible for designing, implementing, and maintaining the infrastructure and systems that power machine learning models. They work closely with data scientists and software engineers to deploy and scale machine learning algorithms in real-world applications.

Machine learning engineers also optimize and fine-tune models to improve their performance. According to Payscale , the average salary for a machine learning engineer with a PhD can range from $100,000 to $150,000 per year.

Analytics Lead/Director

As an analytics lead or director, PhD holders in Data Science take on managerial roles where they oversee data analytics teams and projects. They are responsible for defining the strategic direction of analytics initiatives, setting goals, and ensuring that projects are executed successfully.

Analytics leads/directors collaborate with cross-functional teams to identify business opportunities and develop data-driven strategies. The average salary for an analytics lead or director can range from $120,000 to $200,000 per year, depending on the size and industry of the organization.

These are just a few examples of the roles and titles that PhD holders in Data Science can pursue. The field is rapidly growing, and there is a high demand for skilled professionals who can leverage data to drive innovation and make informed decisions.

With the right skills and qualifications, a PhD in Data Science can open up exciting and lucrative career opportunities.

Career Advancement and Growth

Obtaining a PhD in Data Science can open up a world of career advancement and growth opportunities. With the increasing demand for data professionals in various industries, individuals with advanced degrees in data science are highly sought after.

Here are some ways in which a PhD in Data Science can propel your career:

Move into leadership/executive positions

One of the major benefits of having a PhD in Data Science is the opportunity to move into leadership or executive positions within an organization. With your advanced knowledge and expertise in data analysis and interpretation, you can take on roles such as Chief Data Officer or Data Science Director.

These positions often come with higher salaries and greater responsibilities, allowing you to have a significant impact on the strategic direction of the company.

Manage teams of data professionals

As a PhD holder in Data Science, you will have the ability to lead and manage teams of data professionals. This can involve overseeing the work of data analysts, data engineers, and data scientists, ensuring that projects are executed efficiently and effectively.

By managing a team, you can not only contribute to the success of the organization but also develop your leadership and managerial skills, which are highly valued in the industry.

Consulting and freelance opportunities

With a PhD in Data Science, you can also explore consulting and freelance opportunities. Many companies and organizations are in need of data experts to help them make sense of their data and provide insights for decision-making.

As a consultant or freelancer, you have the flexibility to work on a variety of projects with different clients, allowing you to continuously sharpen your skills and expand your network. This can lead to exciting and lucrative opportunities in the field.

According to a study conducted by Bureau of Labor Statistics , the demand for data scientists is projected to grow by 16% from 2020 to 2030, much faster than the average for all occupations. This indicates a promising outlook for those pursuing a career in the field of data science, especially for those with advanced degrees such as a PhD.

Other Benefits Beyond Salary

Expert status in a top field.

One of the major benefits of earning a PhD in Data Science is the expert status it confers upon you in a rapidly growing and highly sought-after field. With a PhD, you become a recognized authority in the world of data science, which can open doors to exciting career opportunities and prestigious positions.

Employers value the depth of knowledge and expertise that comes with a PhD, and this can lead to rewarding projects, collaborations, and leadership roles within organizations.

Opportunities for publications

Another advantage of pursuing a PhD in Data Science is the opportunity to contribute to the advancement of knowledge through research publications. As a PhD student, you will have the chance to conduct original research and publish your findings in reputable journals and conferences.

Publishing your work not only adds to the body of knowledge in the field, but it also enhances your professional reputation and can increase your chances of securing high-profile positions or academic research roles.

Additionally, having publications to your name can be a valuable asset when applying for grants and funding for future projects.

Job security and flexibility

Having a PhD in Data Science can provide a sense of job security and flexibility in the ever-evolving job market. Data scientists with advanced degrees are in high demand across industries, as organizations increasingly rely on data-driven insights to make strategic decisions.

The specialized skills and knowledge gained during a PhD program make you a valuable asset in industries such as technology, healthcare, finance, and consulting. Furthermore, the versatility of a PhD in Data Science allows you to explore various career paths, including academia, industry research, consulting, and entrepreneurship.

According to a survey conducted by Burtch Works , data scientists with PhDs tend to earn higher salaries compared to those with master’s degrees. However, it is important to note that the benefits of a PhD in Data Science extend beyond monetary compensation.

The expert status, opportunities for publications, and job security and flexibility that come with a PhD make it a worthwhile investment for those passionate about the field of data science.

A PhD in data science can unlock high-paying, advanced career opportunities in data and analytics. While salaries vary based on specialization, location, and employer, data scientists with doctoral degrees can expect to earn well into six figures.

Beyond competitive pay, a data science PhD also brings prestige, flexibility, and the chance to be a leader in an increasingly critical domain. Weighing salaries alongside other benefits can help determine if pursuing this advanced degree is the right move to maximize your career potential.

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• Data Scientist

Google Data Scientist Salaries

Data Scientist compensation in United States at Google ranges from $193K per year for L3 to $881K per year for L8. The median compensation in United States package totals $260K. View the base salary, stock, and bonus breakdowns for Google's total compensation packages. Last updated: 4/18/2024

Average Compensation By Level .css-s9ggwg{z-index:1500;pointer-events:none;}.css-s9ggwg[data-popper-placement*="bottom"] .MuiTooltip-arrow{top:0;margin-top:-0.71em;}.css-s9ggwg[data-popper-placement*="bottom"] .MuiTooltip-arrow::before{transform-origin:0 100%;}.css-s9ggwg[data-popper-placement*="top"] .MuiTooltip-arrow{bottom:0;margin-bottom:-0.71em;}.css-s9ggwg[data-popper-placement*="top"] .MuiTooltip-arrow::before{transform-origin:100% 0;}.css-s9ggwg[data-popper-placement*="right"] .MuiTooltip-arrow{left:0;margin-left:-0.71em;height:1em;width:0.71em;}.css-s9ggwg[data-popper-placement*="right"] .MuiTooltip-arrow::before{transform-origin:100% 100%;}.css-s9ggwg[data-popper-placement*="left"] .MuiTooltip-arrow{right:0;margin-right:-0.71em;height:1em;width:0.71em;}.css-s9ggwg[data-popper-placement*="left"] .MuiTooltip-arrow::before{transform-origin:0 0;}

Given Google sometimes issues offers with an irregular vesting schedule (33%, 33%, 22%, 12%), the average total compensation is calculated by dividing the total stock grant evenly by 4

Latest Salary Submissions

Vesting schedule.

At Google, Main RSUs are subject to a 4-year vesting schedule:

38 % vests in the 1st -year ( 3.17 % monthly )

32 % vests in the 2nd -year ( 2.67 % monthly )

20 % vests in the 3rd -year ( 1.67 % monthly )

10 % vests in the 4th -year ( 0.83 % monthly )

33 % vests in the 1st -year ( 2.75 % monthly )

33 % vests in the 2nd -year ( 2.75 % monthly )

22 % vests in the 3rd -year ( 1.83 % monthly )

12 % vests in the 4th -year ( 1.00 % monthly )

Google commonly refers to RSU as GSU (Google Stock Unit). Although the name is different, it is the same as RSU's. Google's Vesting Schedule may vary between monthly and quarterly vesting depending on the number of shares you recieve: less than 32 GSUs (Annually), 32 - 63 GSUs (Semi-annually), 64 - 159 GSUs (Quarterly) and 160+ GSUs (Monthly).

50 % vests in the 1st -year ( 4.17 % monthly )

28 % vests in the 2nd -year ( 2.33 % monthly )

12 % vests in the 3rd -year ( 1.00 % monthly )

36 % vests in the 1st -year ( 3.00 % monthly )

16 % vests in the 4th -year ( 1.33 % monthly )

25 % vests in the 1st -year ( 2.08 % monthly )

25 % vests in the 2nd -year ( 2.08 % monthly )

25 % vests in the 3rd -year ( 2.08 % monthly )

25 % vests in the 4th -year ( 2.08 % monthly )

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Google | Bangalore, Karnataka, India

Minimum qualifications

• Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field, or equivalent practical experience.
• 10 years of experience with analysis applications (e.g., extracting insights, performing statistical analysis, or solving business problems), and coding (e.g., Python, R, SQL).
• Experience in articulating product questions, pulling data from datasets (e.g., SQL), and using statistics.
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Technical Delivery Executive, State and Local Government

Google | Chicago, IL, USA

• Bachelor's degree in a technical field, or equivalent practical experience.
• 8 years of experience in program management.
• Successful candidates will be required to possess or obtain US Government Top Secret/Sensitive Compartmentalized Information (TS/SCI) security clearance, as this is an essential requirement for this role.

Materials Engineer, Physical Infrastructure

Google | Sunnyvale, CA, USA

• Bachelor's degree in Mechanical Engineering, Product Design, or related field, or equivalent practical experience.
• 8 years of experience designing mechanical components such as plastic or metal parts, mechanical assemblies, printed circuit boards, or flexes.
• 8 years of experience using computer-aided design tools such as 3D MCAD, NX, Creo, or Solidworks.
• Experience in materials science and engineering applied to research and product development.

Data center E-technicus - Onderhoud & operations

Google | Eemshaven, Netherlands

• MBO4/HBO of vergelijkbaar niveau en/of praktijkervaring
• Ervaring met onderhoud van laag en middenspanningsinstallaties

Program Manager II, Supply Chain Spares Analytics, Data Center Operations

Google | Atlanta, GA, USA

• Bachelor's degree or equivalent practical experience.
• 2 years of experience in program or project management.
• Experience with materials planning, forecasting, demand planning, inventory, and supply chain management.

Senior Account Manager, Telecom

Google | New York, NY, USA

• 8 years of experience in digital advertising, consultative sales, digital media sales, business development, online media environment, or digital marketing roles.

Product Manager, Google Classroom

• 4 years of experience in software product management.
• Experience developing Internet products and technologies.
• Experience creating product roadmaps from conception to launch, driving the product vision, defining go-to-market strategy, and leading design discussions.

Software Engineer III, Google Cloud Data Management

Google | Sunnyvale, CA, USA ; Kirkland, WA, USA

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree in an industry setting.
• 2 years of experience with data structures or algorithms in either an academic or industry setting.

Cloud Technical Resident, Cloud Academy, Early Career (Korean, English)

Google | Seoul, South Korea

• Bachelor's degree in relevant STEM field (e.g., Computer Science, Information Systems, Management Information Systems)or equivalent practical experience.
• Experience in Databases, Web Technologies, Machine Learning/AI or Cloud Foundations (e.g., cloud computing, GCP, or similar) through certifications, internships, coursework, or relevant practical experience.
• Experience working in customer service, presenting to clients, or in a leadership role within a school or volunteer organization.
• Ability to communicate in Korean and English fluently to support client relationship management in this region.

CPU CAD Methodology Engineer II

Google | New Taipei, Banqiao District, New Taipei City, Taiwan

• Bachelor's degree in Computer Science, Electrical Engineering, Computer Engineering, a related technical field, or equivalent practical experience.
• 4 years of experience in developing high-performance, low-power design methodology or CAD flows in synthesis and implementation areas.
• Experience in writing production scripts for implementation and sign-off tools.

Design Verification Engineer

• Bachelor's degree in Electrical Engineering, Computer Science, or equivalent practical experience
• 2 years of experience working with digital logic at RTL level using System Verilog or C/C++

Data Center Technician, Engineering Field Services

Google | Reston, VA, USA

• Experience in operating systems and networking protocols.
• Experience in a data center, network operation center, help desk, or similar environments.
• Experience in diagnosing and troubleshooting computer and server hardware.

Security Sales Specialist, VirusTotal (German)

Google | Munich, Germany ; Berlin, Germany ; +2 more ; +1 more

• Candidates will typically have 7 years of experience in a sales role in the enterprise software or cloud space.
• Candidates will typically have 3 years of experience in the enterprise cybersecurity space.
• Ability to communicate in German fluently to support client relationship management in this region.

CPU Architecture and Performance Engineer

Google | Mountain View, CA, USA

• Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or equivalent practical experience.
• Experience in performance modeling, performance analysis, and workload characterization.
• Experience working with general-purpose programming languages (i.e., C/C++, Python).
• Experience in modern, high-performance CPU/ML architecture and micro-architecture.

Scaled Abuse Lead, Trust and Safety, YouTube

YouTube | Hyderabad, Telangana, India

• Bachelor's degree in Computer Science, Engineering, Statistics, Mathematics, a related field, or equivalent practical experience.
• 7 years of experience managing partnerships with Engineering, Product, and Operations teams.
• Experience with data analytics.

Customer Engineer, Machine Learning, Google Cloud

Google | San Francisco, CA, USA ; Sunnyvale, CA, USA

• 5 years of experience as a sales engineer or technical consultant in a cloud computing environment or in a customer-facing role.
• Experience in virtualization or cloud native architectures in a customer-facing or support role.
• Experience with big data, machine learning, and numerical programming frameworks (e.g., TensorFlow, Python, MATLAB).

AI/ML Carbon Reduction and Net Zero Lead

• Bachelor's degree in Computer Science, Math, related technical field, or equivalent practical experience.
• 8 years of experience in program or project management.
• 5 years of experience in a leadership role.
• Experience in compute infrastructure and platforms, including AI/ML specific products.

Silicon Design Engineering Lead, Raxium

Google | Fremont, CA, USA

• Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, or related field, or equivalent practical experience.
• 10 years of experience in ASIC/SoC design, with multiple completed tapeouts.
• 10 years of experience in mixed signal, such as memory integration, Video Input (e.g., MIPI, Quad SPI, I2c), Analog Block Design, Mixed-Signal Design Flow, or Custom Module Modeling (e.g. LEF/DEF/Liberty).

Group Futures and Portfolio Insights Manager, Brand Studio

Google | London, UK

• Bachelor's degree in a research or quantitative field, or equivalent practical experience.
• Candidates will typically have 10 years of experience designing, scoping, and executing research and analysis projects, as well as managing research and measurement agencies.
• Typically 10 years of experience translating business problems into research questions and translating research findings and insights into marketing recommendations.
• Experience in future scenario planning methodologies and translating macro trends into actionable portfolio plans to a senior/board-level audience.
• Experience building and scaling new foresight and portfolio strategy capabilities, leveraging an internal and external partner network.
• Experience as a people leader.

UX Researcher, Core Data UX

• Bachelor's degree in Human-Computer Interaction, Cognitive Science, Statistics, Psychology, Anthropology, related field, or equivalent practical experience.
• 4 years of experience in an applied research setting, or similar.
• Experience conducting semi-structured interviews, contextual field visits, or usability studies either live or remote.
• Experience collaborating with Engineers, Content Strategists, and Product Managers throughout the UX process.

• GradPost Blog

Google PhD Fellowship nominations now open

The UCSB Graduate Division is accepting applications for the Google PhD Fellowship. Google created this Fellowship Program to recognize outstanding graduate students doing exceptional and innovative research in areas relevant to computer science and related fields. The Fellowship, which can last up to three years, provides full tuition and fees plus a stipend. UCSB students should submit applications for internal review by April 26 at 5pm.

The UCSB Graduate Division is now accepting applications for the Google PhD Fellowship . Google created this Fellowship Program to recognize outstanding graduate students doing exceptional and innovative research in areas relevant to computer science and related fields.

Award The Fellowship, which can last up to three years, provides full tuition and fees plus a stipend to be used for living expenses, travel and personal equipment. Also, recipients will be matched with a Google Research Mentor.

Deadline UCSB students should submit their full application packet by April 26 at 5pm Pacific.

Eligibility Universities may only nominate students that meet the following requirements:

• Full-time graduate students pursuing a PhD and enrolled in an institution in one of the regions listed above.
• Completed graduate coursework by the academic award year when the Fellowship begins.
• Students must remain enrolled full-time in the PhD program for the duration of the Fellowship or forfeit the award.
• Google employees, and their spouses, children, and members of their household are not eligible.
• Students that are already supported by a comparable industry award are not eligible. Government or non-profit organization funding is exempt.

Nomination and Application PhD students must me nominated by their university. Universities are able to nominate up to four eligible students. If UCSB nominates more than two students, Google strongly encourages us to select nominees who self-identify as a woman, Black / African descent, Hispanic / Latino / Latinx, Indigenous, and/or a person with a disability.

Visit the Fellowship website's FAQ for information about eligibility, application requirements.

You can submit your application to be a UCSB nominee by filling out this form . Applications must be uploaded as a single PDF document.

Soumik Purkayastha, MS ’21, PhD ’24

• Biostatistics

April 15, 2024

A native of India, Soumik Purkayastha, MS ’21, PhD ’24, first made a connection with the University of Michigan School of Public Health in 2005.

Purkayastha was born in Chandannagar, a town about the same size and population as Ann Arbor, in the state of West Bengal. He spent most of his childhood with his parents in Kolkata or with his grandparents in Chandannagar. 

A visit to Ann Arbor when he was in fourth grade would leave an indelible impression on Purkayastha and set him on a path toward a career in public health.

“My father visited the Department of Biostatistics during his sabbatical year,” he said. “My mother and I were able to join him for a few months here in Ann Arbor. Later, when I was applying to graduate programs in the United States, the program here was very attractive because not only was it a great program, but I felt a personal connection to the school and the town at large.

It’s encouraging to see how the integration of data science in public health contributes to improved health outcomes. In many ways, I feel our work is meaningful and well-received not just in clinical circles but also by policymakers and the media.”

“Back in 2005, I had no way of knowing I’d be spending a good chunk of my 20s in Ann Arbor, but now I can look back and connect some dots.”

For Purkayastha, one of the most interesting things about public health is how he keeps learning about exciting ways in which public health and data science intersect.

“It’s encouraging to see how the integration of data science in public health contributes to improved health outcomes,” he said. “In many ways, I feel our work is meaningful and well-received not just in clinical circles but also by policymakers and the media.”

During the COVID-19 pandemic, Purkayastha had an opportunity to work on spatio-temporal forecasting of infectious diseases. His areas of focus included infection rates in India, comparing and understanding how different transmission models work, as well as developing a transmission data-driven framework to help inform public health policy.

“What I enjoyed the most was getting to learn from epidemiologists, biostatistics, economists and policy experts at a time when we were all confined to our apartments,” Purkayastha said. “It was a time when all of us struggled, yet our collective efforts at understanding what was going on in the world around us gave me a sense of purpose—and I cherish that.

“I think working in public health gives me a sense of direction on what to do with my skill set while also finding meaning in the work I do.”

Purkayastha will graduate in May with a PhD in Biostatistics from the Department of Biostatistics at Michigan Public Health. He earned a Master of Science in Biostatistics from Michigan Public Health after receiving bachelor’s and master’s degrees in Statistics from St. Xavier’s College and the Indian Statistical Institute respectively, both of Kolkata, India.

Driving change with biostatistics

He found meaning in working with the student organization STATCOM: Statistics in the Community .

“I’m proud to say that my department is home to STATCOM, a community outreach program provided by graduate students at the University of Michigan,” Purkayastha said.

The program offers the expertise of graduate students—free of charge—to nonprofit governmental and community organizations in the areas of data organization, analysis, and interpretation.

“I have been very fortunate to find meaningful consulting work with a host of community partners,” he said. “From 2022-23, I had the honor of serving as co-president of STATCOM. It has been both challenging as well as rewarding. I expect to continue with STATCOM until I graduate.”

A few of STATCOM’s recent projects include partnerships with:

• The Michigan Center for Youth Justice (MCYJ): STATCOM’s partnership with the MCYJ revolves around understanding the patterns of special investigations and violations occurring in juvenile justice facilities throughout the state.
• The Detroit Housing Commission (DHC) and Poverty Solutions (PS): STATCOM has been working with the DHC and PS to investigate evictions among families with children in Detroit to reduce accompanied and unaccompanied youth homelessness rates.
• Stand with Trans (SWT): STATCOM is collaborating with SWT to identify gaps in—and further improve—online resources that are designed to empower and support transgender youth.

Purkayastha also worked as a research assistant at the University of Michigan, studying diabetes.

“I worked with doctors and clinical experts to study diabetic foot ulcers,” he said. “They’re a major cause of amputation in US adults. With diabetes prevalence rising, understanding how these ulcers behave is crucial not only from a surgical perspective but also from a preventive viewpoint. It was a wonderful experience getting to apply my technical skills to real diabetes data to derive insights through statistics and visualization tools. I learned a lot about the disease from medical professionals as well.

“I think the work biostatisticians do is helpful for practitioners and policymakers as we learn more about disease prediction and prevention. Most importantly, the work we do in public health must be accessible and informative to the public. I think it’s incredibly important that we use all our skills to advocate for public health initiatives, policies and practices.”

In addition, Purkayastha had an internship with Apple in California, where he learned about language models.

“The future of language models in medicine, and public health is incredibly exciting,” he said. “I was also fortunate to receive the Rackham Predoctoral Fellowship for my final dissertation year. It allowed me to focus on developing new research projects and spend a lot of time thinking about cool, new ideas.”

Working in public health is ‘incredibly rewarding’

After graduation, Purkayastha will join the Department of Biostatistics at the University of Pittsburgh as an assistant professor in the fall. He will be engaged in methodological and applied public health research in addition to teaching and mentoring younger students in public health education and research.

“Soumik is a terrific example of the best of Michigan graduates; His dedication to translational biostatistics is inspiring,” said Bhramar Mukherjee , the John D. Kalbfleisch Distinguished University Professor of Biostatistics and Siobán D. Harlow Collegiate Professor of Public Health. “His work is grounded in solid theory and computation but has profound public health impact. He has been an amazing student leader through his work in departmental committees and through our flagship student organization STATCOM. I look forward to his scholarly career after graduation. I know he will make a difference.” 

I think the work biostatisticians do is helpful for practitioners and policymakers as we learn more about disease prediction and prevention. Most importantly, the work we do in public health must be accessible and informative to the public. I think it’s incredibly important that we use all our skills to advocate for public health initiatives, policies and practices.”

Purkayastha said he thrives on the satisfaction of knowing that his efforts in public health are making a positive impact on the health and well-being of individuals and communities.

“I feel it can be incredibly rewarding on a personal level for many others as well,” he said. “I have seen firsthand how the work we do translates to changes in governance and policy making. Further, there is an increasing need for health data scientists, who use their data skills for the public good. And it’s not just biostatistics, public health offers a wide range of career paths and specialties, including epidemiology, health education, policy development, environmental health and global health. This diversity allows individuals to find roles that align with someone’s interests, skills and passions, creating opportunities for personal and professional growth.

“I’m satisfied with my career choices so far. Working in public health allows me to focus on a lot of things I’m interested in while building something useful for communities at large. It’s a good feeling, and I wish that for everyone.”

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• Learn more about the Biostatistics program .
• Read more stories about students, alumni, faculty, and staff.
• Support research and engaged learning at the School of Public Health.

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Computer Science > Computation and Language

Title: leave no context behind: efficient infinite context transformers with infini-attention.

Abstract: This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infini-attention. The Infini-attention incorporates a compressive memory into the vanilla attention mechanism and builds in both masked local attention and long-term linear attention mechanisms in a single Transformer block. We demonstrate the effectiveness of our approach on long-context language modeling benchmarks, 1M sequence length passkey context block retrieval and 500K length book summarization tasks with 1B and 8B LLMs. Our approach introduces minimal bounded memory parameters and enables fast streaming inference for LLMs.

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Fall 2024 CSCI Special Topics Courses

Cloud computing.

Meeting Time: 09:45 AM‑11:00 AM TTh  Instructor: Ali Anwar Course Description: Cloud computing serves many large-scale applications ranging from search engines like Google to social networking websites like Facebook to online stores like Amazon. More recently, cloud computing has emerged as an essential technology to enable emerging fields such as Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning. The exponential growth of data availability and demands for security and speed has made the cloud computing paradigm necessary for reliable, financially economical, and scalable computation. The dynamicity and flexibility of Cloud computing have opened up many new forms of deploying applications on infrastructure that cloud service providers offer, such as renting of computation resources and serverless computing.    This course will cover the fundamentals of cloud services management and cloud software development, including but not limited to design patterns, application programming interfaces, and underlying middleware technologies. More specifically, we will cover the topics of cloud computing service models, data centers resource management, task scheduling, resource virtualization, SLAs, cloud security, software defined networks and storage, cloud storage, and programming models. We will also discuss data center design and management strategies, which enable the economic and technological benefits of cloud computing. Lastly, we will study cloud storage concepts like data distribution, durability, consistency, and redundancy. Registration Prerequisites: CS upper div, CompE upper div., EE upper div., EE grad, ITI upper div., Univ. honors student, or dept. permission; no cr for grads in CSci. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/6BvbUwEkBK41tPJ17 ).

CSCI 5980/8980 

Machine learning for healthcare: concepts and applications.

Meeting Time: 11:15 AM‑12:30 PM TTh  Instructor: Yogatheesan Varatharajah Course Description: Machine Learning is transforming healthcare. This course will introduce students to a range of healthcare problems that can be tackled using machine learning, different health data modalities, relevant machine learning paradigms, and the unique challenges presented by healthcare applications. Applications we will cover include risk stratification, disease progression modeling, precision medicine, diagnosis, prognosis, subtype discovery, and improving clinical workflows. We will also cover research topics such as explainability, causality, trust, robustness, and fairness.

Registration Prerequisites: CSCI 5521 or equivalent. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/z8X9pVZfCWMpQQ6o6  ).

Visualization with AI

Meeting Time: 04:00 PM‑05:15 PM TTh  Instructor: Qianwen Wang Course Description: This course aims to investigate how visualization techniques and AI technologies work together to enhance understanding, insights, or outcomes.

This is a seminar style course consisting of lectures, paper presentation, and interactive discussion of the selected papers. Students will also work on a group project where they propose a research idea, survey related studies, and present initial results.

This course will cover the application of visualization to better understand AI models and data, and the use of AI to improve visualization processes. Readings for the course cover papers from the top venues of AI, Visualization, and HCI, topics including AI explainability, reliability, and Human-AI collaboration.    This course is designed for PhD students, Masters students, and advanced undergraduates who want to dig into research.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/YTF5EZFUbQRJhHBYA  ). Although the class is primarily intended for PhD students, motivated juniors/seniors and MS students who are interested in this topic are welcome to apply, ensuring they detail their qualifications for the course.

Visualizations for Intelligent AR Systems

Meeting Time: 04:00 PM‑05:15 PM MW  Instructor: Zhu-Tian Chen Course Description: This course aims to explore the role of Data Visualization as a pivotal interface for enhancing human-data and human-AI interactions within Augmented Reality (AR) systems, thereby transforming a broad spectrum of activities in both professional and daily contexts. Structured as a seminar, the course consists of two main components: the theoretical and conceptual foundations delivered through lectures, paper readings, and discussions; and the hands-on experience gained through small assignments and group projects. This class is designed to be highly interactive, and AR devices will be provided to facilitate hands-on learning.    Participants will have the opportunity to experience AR systems, develop cutting-edge AR interfaces, explore AI integration, and apply human-centric design principles. The course is designed to advance students' technical skills in AR and AI, as well as their understanding of how these technologies can be leveraged to enrich human experiences across various domains. Students will be encouraged to create innovative projects with the potential for submission to research conferences.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/Y81FGaJivoqMQYtq5 ). Students are expected to have a solid foundation in either data visualization, computer graphics, computer vision, or HCI. Having expertise in all would be perfect! However, a robust interest and eagerness to delve into these subjects can be equally valuable, even though it means you need to learn some basic concepts independently.

Sustainable Computing: A Systems View

Meeting Time: 09:45 AM‑11:00 AM  Instructor: Abhishek Chandra Course Description: In recent years, there has been a dramatic increase in the pervasiveness, scale, and distribution of computing infrastructure: ranging from cloud, HPC systems, and data centers to edge computing and pervasive computing in the form of micro-data centers, mobile phones, sensors, and IoT devices embedded in the environment around us. The growing amount of computing, storage, and networking demand leads to increased energy usage, carbon emissions, and natural resource consumption. To reduce their environmental impact, there is a growing need to make computing systems sustainable. In this course, we will examine sustainable computing from a systems perspective. We will examine a number of questions:   • How can we design and build sustainable computing systems?   • How can we manage resources efficiently?   • What system software and algorithms can reduce computational needs?    Topics of interest would include:   • Sustainable system design and architectures   • Sustainability-aware systems software and management   • Sustainability in large-scale distributed computing (clouds, data centers, HPC)   • Sustainability in dispersed computing (edge, mobile computing, sensors/IoT)

Registration Prerequisites: This course is targeted towards students with a strong interest in computer systems (Operating Systems, Distributed Systems, Networking, Databases, etc.). Background in Operating Systems (Equivalent of CSCI 5103) and basic understanding of Computer Networking (Equivalent of CSCI 4211) is required.

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