phd thesis in bioinformatics

Medical Bioinformatics and Computational Modelling

PhD students at the Bioinformatics Laboratory

In Progress 

• Lashgari, D. Kinetic maturation in the Germinal Center . University of Amsterdam, Amsterdam. Supported by AMC. Van Kampen, A.H.C. (promotor), Van Gils, M. (co-promotor), Hoefsloot, H. C. (co-promotor).
• Mahamune, U. Single Cell RNAseq and computational modelling .   University of Amsterdam, Amsterdam. ARCAID . Marie Curie COFUND, Horizon 2020. Van Kampen, A.H.C. (promotor), Moerland, P.D. (co-promotor), E.G.M. van Baarsen (co-promotor).
• Valiente, R. G. Development of multiscale mathematical models of the germinal center (GC) to study its role in B-cell lymphoma (BCL) and/or rheumatoid arthritis (RA). (PhD thesis). University of Amsterdam, Amsterdam. COSMIC . Marie Curie ITN, Horizon 2020. Van Kampen, A.H.C. (promotor), De Vries, N. (promotor), Hoefsloot, H. C. (co-promotor), Guikema, J. E. (co-promotor).
• Stobbe, M. (2012). 18 October 2012. The road to knowledge: from biology to databases and back again. University of Amsterdam, Amsterdam. NBIC BioRange. Van Kampen,  A.H.C. (promotor),  Moerland, P. D. (co-promotor). [ UvA-DARE ]
• Shahand, S. (2015). 29 October 2015. Science gateways for biomedical big data analysis. University of Amsterdam, Amsterdam. COMMIT. Van Kampen,  A. (promotor), Olabarriaga, S. (co-promotor). [ UvA-DARE ]
• Reshetova, P. (2017). 2 March 2017. Use of Prior Knowledge in Biological Systems Modelling. University of Amsterdam, Amsterdam. NBIC Biorange. Van Kampen,  A.H.C (promotor), Smilde, A.  (promotor), Westerhuis, J.  (co-promotor). [ UvA-DARE ]
• Tejero Merino, E. (2022). 7 November 2022 Multiscale modelling of plasma cell differentiation in the Germinal Center. University of Amsterdam, Amsterdam. Supported by AMC. Van Kampen, A.H.C. (promotor), Guikema, J.E.J. (co-promotor), Hoefsloot, H. C. (co-promotor). [ PhD thesis] [ UvA-DARE ]
• Nandal, U. (2023). Computational approaches for biological data integration. University of Amsterdam, Amsterdam. NBIC BioRange. Van Kampen, A.H.C. (promotor), Moerland, P.D. (co-promotor). [ UvA-DARE ]
• Balashova, D. Repertoire sequencing . University of Amsterdam, Amsterdam. ARCAID . Marie Curie COFUND, Horizon 2020. Van Kampen, A.H.C. (promotor), De Vries N. (promotor), Greiff V. (co-promotor). – Terminated

Co-supervised PhD students from other research groups

In Progress

• Balzaretti, G. Repertoire Sequencing . University of Amsterdam, Amsterdam. De Vries, N. (promotor), Van Kampen, A.H.C. (promotor).
• Lermo Jimenez, M. Epigenetics and breast cancer drug resistance . University of Amsterdam, Amsterdam. Verschure P. J. (promotor), Moerland, P.D. (co-promotor).
• Olivieri, A. Repertoire Sequencing. University of Amsterdam, Amsterdam. ARCAID , Marie Curie COFUND, Horizon 2020. De Vries, N. (promotor), Van Kampen, A.H.C. (promotor).
• Stratigopoulou, M. Germinal Center and B-cell Lymphoma . University of Amsterdam, Amsterdam. COSMIC. Marie Curie ITN, Horizon 2020. Van Kampen, A.H.C. (promotor), Van Noesel, C. J. (promotor), De Vries, N. (co- promotor), Guikema, J. E. (co-promotor).
• Sontrop, H. (2015). 15 January 2015. A critical perspective on microarray breast cancer gene expression profiling. TU Delft, Delft. NBIC BioRange. Reinders, M. (promotor), Moerland, P. D. (co-promotor). [ Link ]
• Beckman, W. (2021). 17 August 2021. The Role of Epigenetics in Transcriptional Stochasticity and the Implications for Breast Cancer Drug Resistance . University of Amsterdam, Amsterdam. EpiPredict. Marie Curie ITN, Horizon 2016. Verschure P.J. (promotor), Van Kampen, A.H.C. (promotor). [ UvA-DARE ]
• Barros, R. S. (2022). 1 November 2022 High performance computing for clinical medical imaging . University of Amsterdam, Amsterdam. Henk Marquering (promotor), Van Kampen, A.H.C. (promotor), Olabarriaga, S. (co-promotor). [ UvA-DARE ]
• Anang, D. (2023) 6 November 2023. B and T Cell Immune Responses in Rheumatoid Arthritis and Myositis. In Search for the Immunological Drummers and Dancers . University of Amsterdam, Amsterdam. COSMIC . Marie Curie ITN, Horizon 2020. De Vries, N. (promotor), Van Kampen, A.H.C. (promotor), van Baarsen, E.G.M. (co-promotor). [ UvA-DARE ]
• Wegdam, W. (2024). In search of protein biomarkers in ovarian cancer and Gaucher disease. University of Amsterdam, Amsterdam. Aerts J.M.F.G. (promotor), Kenter, G.G.  (promotor), Moerland, P.D. (co-promotor). [ UvA-DARE ]
• Pollastro, S (2024) 17 May 2024. Understanding Response to Rituximab Treatment in Rheumatoid Arthritis Through Immune Fingerprinting of T and B Cells . University of Amsterdam, Amsterdam. De Vries, N. (promotor), Van Kampen, A.H.C. (co-promotor). [ UvA-DARE ].

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PhD in Bioinformatics Data Science

A Ph.D. in Bioinformatics Data Science will train the next-generation of researchers and professionals who will play a key role in multi- and interdisciplinary teams, bridging life sciences and computational sciences. Students will receive training in experimental, computational and mathematical disciplines through their coursework and research. Students who complete this degree will be able to generate and analyze experimental data for biomedical research as well as develop physical or computational models of the molecular components that drive the behavior of the biological system.

Students must complete a minimum of 15 hours of coursework, plus 3 credit hours of seminar, 6 credit hours of research and 9 credit hours of doctoral dissertation. The Ph.D. requires a minimum of 33 credits. Students who are admitted directly after a B.S. degree will be required to complete up to 9 additional credits in order to fulfill the core curriculum in the following areas: Database Systems, Statistics, and Introduction to Discipline. In addition, if students entering the program with an M.S. degree are lacking equivalent prerequisites, they also will be required to complete courses in these three areas; however, these courses may fulfill the elective requirement in the Ph.D. program, if approved in the program of study.

Academic Load

PhD students holding research assistantships (or teaching) are considered full-time with 6 credit hours . Students without RA or TA  are considered full-time if enrolled in at least 9 credit hours or in sustaining credit. Those enrolled for fewer than 9 credit hours are considered part-time students. Generally, a maximum load is 12 graduate credit hours; however, additional credit hours may be taken with the approval of the student’s adviser and the Graduate College. A maximum course load in either summer or winter session is 7 credit hours. Permission must be obtained from the Graduate College to carry an overload in any session. 

Bioinformatics Data Science Courses

Students must take one course in each of the following areas (9 credits):

Prerequisites

Students must fulfill core curriculum in each of the following areas (3-9 credits):

Elective Courses

Students must take two courses to compliment their bioinformatics data science dissertation project (6 credits): 

See Elective courses

Students must take six semesters of seminar (three 0 credit; three 1 credit) and give a presentation during three semesters.

Other Requirements:

• Formation of Graduate Dissertation Committee
• Successful completion of Graduate Preliminary Exam
• Research on a significant scientific problem
• Successful completion of Ph.D. Candidacy Exam
• Successful completion of Dissertation Defense

Formation of Graduate Committee

The student needs to establish a Dissertation Committee within the first year of study. The Committee should consist of at least four faculty members, including the primary faculty advisor (serving as the Committee Chair), a secondary faculty advisor (in a complementary field to the primary advisor), a second faculty from the home department, and one CBCB affiliate faculty outside the Departments of the primary and secondary advisors or from outside the University. Students must complete the Dissertation Committee Formation form and submit to the Associate Director.

Students should convene their dissertation committee at least once every six months.

Preliminary Examination

The preliminary examination should be taken before the end of the fourth semester and will consist of an oral exam in subjects based on the Bioinformatics Data Science core.* In recognition of the importance of the core curriculum in providing a good test of the student’s knowledge, students must achieve a minimum 3.0 GPA in the core curriculum before taking the preliminary exam. Students will not be permitted to take the preliminary examination if the core grade requirements and cumulative GPA of 3.0 has not been achieved. The exam will be administered by the Preliminary Exam Committee , which will consist of one instructor from each of the three core courses. Each member of the Committee will provide a single grade (pass, conditional pass or fail) and the final grades will be submitted via the Results of Preliminary Exam Form :

• Pass . The student may proceed to the next stage of his/her degree training.
• Conditional pass . In the event that the examination committee feels that the student did not have an adequate background or understanding in one or more specific areas, the Preliminary Exam Committee will communicate the conditional pass to the student and must provide the student with specific requirements and guidelines for completing the conditional pass. The student must inform the Preliminary Exam Committee, the Graduate Program Director and Program Committee when these conditions have been completed. The Preliminary Exam Committee will then meet with the student to ensure all recommendations have been completed and whether a re-examination is necessary. If required, the re-examination will be done using the same format and prior to the beginning of the next academic semester. If the student still does not perform satisfactorily on this re-examination, he/she will then be recommended to the Graduate Affairs Committee for dismissal from the graduate program.
• Failure . This outcome would indicate that the Examination Committee considers the student incapable of completing degree training. The student’s academic progress will be reviewed by the Graduate Affairs Committee, who will make recommendations to the Program Director regarding the student’s enrollment status. The Program Director may recommend to the Graduate College that the student be dismissed from the Program immediately.

*Students who need to complete prerequisite courses may request a deadline extension for the preliminary and subsequently the candidacy examination. Requests must be submitted to the Graduate Program Committee prior to the start of the third semester.

Candidacy Exam

The candidacy examination must be completed by the end of the sixth semester of enrollment.* It requires a formal, detailed proposal be submitted to the Dissertation Committee and an oral defense of the student’s proposed research project. Upon the recommendation of the Dissertation Committee, the student may be admitted to candidacy for the Ph.D. degree. The stipulations for admission to doctoral candidacy are that the student has (i) completed one academic years of full-time graduate study in residence at the University of Delaware, (ii) completed all required courses with the exception of BINF865 and BINF969, (iii) passed the preliminary exams, (iv) demonstrated the ability to perform research, and (v) had a research project accepted by the Dissertation Committee. Within one week of the candidacy exam, complete and submit the Recommendation for Candidacy for Doctoral Degree form for details. A copy of the completed form should be given to the Associate Director.

*Students who need to complete prerequisite courses may request a deadline extension for the preliminary and subsequently the candidacy examination.  Requests must be submitted to the Graduate Program Committee prior to the start of the third semester.

Dissertation Exam

The dissertation examination of the Ph.D. program will involve the approval of the written dissertation and an oral defense of the candidate’s dissertation.  The written dissertation will be submitted to the Dissertation Committee and the CBCB office at least three weeks in advance of the oral defense date.  The oral defense date will be publicly announced at least two weeks prior to the scheduled date. The oral presentation will be open to the public and all members of the Bioinformatics Data Science program. The Dissertation Committee will approve the candidate’s dissertation. The student and the primary faculty advisor will be responsible for making all corrections to the dissertation document and for meeting all Graduate College deadlines.  Within one week of the dissertation defense, complete and submit the Certification of Doctoral Dissertation Defense Form. A copy of the completed form should be given to the Associate Director.

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• PhD in Bioinformatics

The PhD in Bioinformatics program offers unique interdisciplinary training for graduate students in the science, engineering, medicine, and ethics of twenty-first-century cell biology jointly through the Faculty of Computing & Data Sciences. The program aims to prepare top researchers for careers in both academia and industry in the areas of molecular life sciences. In order to be admitted, students need at least a bachelor’s degree in a field related to bioinformatics, preferably one with a strong component in mathematics and computer science.

Learning Outcomes

Students graduating with a PhD in Bioinformatics are expected to:

• Demonstrate mastery of the core concepts of Bioinformatics: These include (a) advanced methods in computational biology, (b) the chemical principles that underlie biochemistry, molecular biology, and genomics, (c) the design and implementation of relational databases, (d) fundamental methods in probability and statistics, and (e) the construction of predictive mathematical models of biological systems.
• Be capable of using critical thinking and research methods in Bioinformatics to understand computational and experimental data. In addition to formal coursework, this ability will be learned and demonstrated in (a) dissertation research and (b) presentations at scientific meetings, graduate seminars, student seminars, and qualifying examinations.
• Demonstrate the ability to produce and present original research in Bioinformatics. The most important manifestation of this outcome is publication of peer-reviewed research papers on dissertation research, and, in particular, papers with the trainee as first author. The Challenge Project, seminar presentations, and presentations at meetings also demonstrate this outcome.
• Conduct scholarly activities in a professional and ethical manner.
• Develop the ability to communicate clearly the meaning, potential impacts, and risks associated with one’s research activities to a nontechnical audience in ways that confer a sense for its value to society.

Course Requirements

The PhD requires a total of 64 course units, consisting of the 36 required units listed below, or their equivalents, and additional elective lecture, laboratory, and research units. The precise course of study will be determined in consultation with faculty advisors and will reflect the student’s background and interests. In order to be admitted to PhD candidacy, students must demonstrate mastery of the required subject matter (no lower than a B in each of the required courses). Course requirements are as follows:

• CAS MA 681 Accelerated Introduction to Statistical Methods for Quantitative Research (4 units)
• ENG BE 562 Computational Biology: Genomes, Networks, Evolution (4 units)
• CAS CS 542 Machine Learning (4 units)
• CAS MA 770 Mathematical and Statistical Methods of Bioinformatics (4 units)
• CDS BF 571 Dynamics and Evolution of Biological Networks (4 units)
• CDS BF 768 Biological Database Systems (4 units)
• CAS BI 565 Functional Genomics (4 units)
• CDS BF 751 Molecular Biology and Biochemistry: Molecules and Processes (4 units)
• CDS BF 690 Bioinformatics Challenge Project (2 units each; 4 total)
• CDS BF 752 Legal and Ethical Issues of Science and Technology (4 units)
• CDS BF 810 Laboratory Rotation System (1 unit each, 3 total)
• CDS BF 820 Research Opportunities in Bioinformatics (1 unit)
• CDS BF 821 Bioinformatics Graduate Seminar (2 units each)
• One nonresearch elective course (4 units)
• A minimum of 2 research units

Fulfillment of required course equivalents will be determined based on documented previous academic and/or work experience. The student and their advisors will petition the curriculum committee for such equivalencies. When either past work or an alternate course has been accepted as a required course equivalent, the student’s advisors will recommend another course to fulfill the 36 core unit hours. Advanced elective courses should be taken in place of any waived course requirements.

Qualifying Examination

Students must pass an oral qualifying exam in order to advance to the level of PhD candidacy. The goal of the exam is for the student to demonstrate their general proficiency in bioinformatics, as well as command of the area(s) in which they intend to conduct research. All parts of the qualifying examination must be passed before the dissertation or thesis prospectus will be accepted by the Faculty of Computing & Data Sciences.

Students must schedule their qualifying exam by March 31 of their second year, and must take the exam by June 30. Students who fail to pass the exam on their first try are allowed a second attempt, to be scheduled and completed by the end of the first term of their third year.

Language Requirement

There is no foreign language requirement for the bioinformatics degree. However, basic mastery of spoken and written English, as determined by oral presentations, written reports, and publishable manuscripts, is a requirement for the PhD.

Dissertation and Final Oral Examination

Candidates shall demonstrate their abilities for independent study in a dissertation representing original research or creative scholarship. A prospectus for the dissertation must be completed and approved by the readers, the Director of Graduate Studies, and the Department Chair/Program Director. Candidates must undergo a final oral examination in which they defend their dissertations as a valuable contribution to knowledge in their field and demonstrate a mastery of their field of specialization in relation to their dissertation. All portions of the dissertation and final oral examination must be completed as outlined in the GRS General Requirements for the Doctor of Philosophy Degree . CDS uses the same requirements as GRS.

Students who complete the required core courses but are unable to successfully complete all of the requirements for the PhD will be eligible to be awarded a master’s degree.

Related Bulletin Pages

• Faculty of Computing & Data Sciences Courses
• Abbreviations and Symbols

Beyond the Bulletin

• Bioinformatics Program
• Faculty of Computing & Data Sciences
• BS in Data Science
• BS/MS in Data Science
• MS in Data Science
• MS in Data Science (Online)
• PhD in Computing & Data Sciences
• Minor in Data Science
• BS in Data Science/MS in Bioinformatics
• MS in Bioinformatics

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Dissertation Archive

All UNC Charlotte dissertations and theses can be found in ProQuest University of North Carolina at Charlotte .

Dissertations of past Ph.D. students from the Bioinformatics program.

Adam Price: Ph.D., Bioinformatics and Computational Biology Understanding Bias in Next-Generation Sequencing Technologies and Analyses

Benika Hall: Ph.D., Bioinformatics and Computational Biology Constructing microRNA eQTL Networks Using Integrative Network Learning Approaches in Cancer

Rosario Ivetth Corona de la Fuente: Ph.D., Bioinformatics and Computational Biology Structural Analysis of Protein-DNA Binding Specificity and its Application to Protein-DNA Docking Assessment Library

Sajedeh Safari: Ph.D., Bioinformatics and Computational Biology Evolution of Flavonoid Pathway in Legumes Library

Adam Michael Whaley: Ph.D., Bioinformatics and Computational Biology Genetic Mechanisms of Ozone Tolerance in Soybean and Methods of Evolutionary Distance Library

Yan Ni: Ph.D., Bioinformatics and Computational Biology Data Analysis Workflow for Gas Chromatography Mass Spectrometry-Based Metabolomics Studies Library

Meng Niu: Ph.D., Bioinformatics and Computational Biology De Novo Prediction of Cis-Regulatory Modules in Eukaryotic Organisms Library

Jaime Lynn Sheridan: Ph.D., Bioinformatics and Computational Biology Elucidation of Mirnas in Avena Sativa Library

Saeed Khoshnevis: Ph.D., Bioinformatics and Computational Biology The Effect of Structure in Short Regions of DNA on Measurement on Short Oligonucleotide Microarry and Ion Torrent PGM Sequencing Platforms Library

Jonathan Ward McCafferty: Ph.D., Bioinformatics and Computational Biology Microbial Contributions to Disease Phenotypes Library

Shatavia Sharday Morrison: Ph.D., Bioinformatics and Computational Biology Vibrio Vulnifcus Virulence and Survival Mechanisms Revealed through Comparative Microbial Genomic Analysis Library

Cristina Baciu: Ph.D., Bioinformatics and Computational Biology Bioinformatics and Biomolecular Tools for biomarker discovery in peripheral blood lymphocytes from patients with sporadic amyotrophic lateral sclerosis Library

Charles David: Ph.D., Bioinformatics and Systems Biology Time Delayed Dynamical Systems and the Duffing Equation Library

Verma Deeptak: Ph.D., Bioinformatics and Computational Biology Elucidating the Effects of Mutation and Evolutionary Divergence Upon Protein Structure Quantitative Stability/Flexibility Relationships Library

Christopher C Overall: Ph.D., Bioinformatics and Computational Biology Microarray Tools and Analysis Methods to Better Characterize Biological Networks Library

Luis Gonzalez: Ph.D., Bioinformatics and Systems Biology A Virtual Pebble Game to Ensemble Average Graph Rigidity Library

Nina Sanapareddy: Ph.D., Bioinformatics and Systems Biology Using Bioinformatics to Analyze the Role of Microbial Taxa in Complex Ecosystems Library

Melanie Spencer: Ph.D., Bioinformatics and Systems Biology Stability, Resistance and Change in Mammalian Microbiota and Their Associations With Host Health Library

Timothy Tickle: Ph.D., Bioinformatics and Systems Biology Data Mining the Serous Ovarian Tumor Transcriptome Library

Vladyslava Ratushna: Ph.D., Bioinformatics ans Systems Biology The Effect of Target Secondary Structure on Microarray Data Quality Library

Robert Reid: Ph.D., Bioinformatics and Systems Biology Improving Data Extraction Methods for Large Molecular Biology Datasets Library

Raad Gharaibeh: Ph.D., Bioinformatics and Systems Biology Studies on the Relationships Between Oligonucleotide Probe Properties and Hybridization Signal Intensities Library

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Graduate Theses and Dissertations - Biostatistics, Bioinformatics & Biomathematics

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Bioinformatics

Phd curriculum.

The requirements for each student in the PhD program in Bioinformatics include the successful completion of a set of core courses in Biology, Biochemistry, Mathematics, and Computer Science, while the main emphasis of the program is on the successful completion of an original and independent research project. Each student must also complete a minor program of study in accordance with Institute policies.

Admission to candidacy requires passing written and oral comprehensive examinations administered by the Bioinformatics PhD Graduate Committee (see the Qualifying Exams section below). The PhD dissertation written on results of the individual research project should provide evidence that the PhD candidate is ready to start an independent research career. The PhD thesis should be defended publicly and approved by the thesis committee.

Each student regardless of home unit is required to complete course work from the following categories.  Specific courses should be selected in consultation with the student’s faculty advisor, committee, and the Bioinformatics program director.  

A. 9 credit hours of Bioinformatics and Computational Bioscience (e.g. BIOL 6150, BIOL 7200, BIOL 7210)

B.  9 credit hours in Biology, Biochemistry or Biomedical Engineering (e.g. BIOL 7015, BMED 6517, BIOL 8803)

C. 9 credit hours of Mathematics and Computer Science (e.g. CS 7641, CSE 6242, MATH 6702)

D. 9 credit hours of courses in an approved minor

E. 24 research credit hours

Credit hours for courses in categories A, B, and C could be completed by previous graduate study (such as study in the Georgia Tech Master's Degree in Bioinformatics program). Approval of transfer of credits from courses taken elsewhere is done by the Bioinformatics graduate committee.  Typically, 2/3 of credit hours in each category A, B, C, D should be at 6000 or higher level. Students can use appropriate 4000 level courses from the list of recommended courses (see separate tab), if the student's thesis committee approves them and include them into a program of study.  A student must maintain a GPA of 3.2 in his/her course work.

Participating Schools may have additional requirements and policies for students registered for the Bioinformatics PhD program in that School as the home unit, such as a requirement that courses in sections B or C must be taken in the home department, and/or specifics on affiliation of thesis committee members. These further define the course of study, but do not constitute additional academic workload.

Please download the pdf. below to complete the PhD Program of Study form:

Program of Study Bioinformatics.pdf

QUALIFYING EXAMS

The student must successfully pass a qualifying exam, preferably within 24 months after entering the PhD program. The exam consists of written and oral parts. The written part is a written proposal of the planned PhD dissertation research, in the format of a research grant proposal. The oral examination is a presentation of this written thesis proposal. The written exam in Bioinformatics and the oral exam are administered by a faculty committee consisting of:

• Two Bioinformatics Program faculty
• One faculty member from the Home Unit
• Thesis advisor as an observer, not as a participant (as a rule).

The committee is suggested by the advisor and approved jointly by the Chair of the Bioinformatics Graduate Committee and the Chair of a Home Unit Graduate Committee.

The guidelines of the qualifying exam are given as an example and not as a strict guideline to follow by each home unit. Each home unit is allowed to modify the qualifying exam policy to make it most suitable to the unit profile.

Students who wish to transfer to the Bioinformatics program after passing their qualifying exam in another PhD major can be admitted by the Bioinformatics Graduate committee without the requirement of passing the Bioinformatics qualifying exam. In this case the advisor (with co-advisor) and thesis committee may have to specify additional courses to be taken to satisfy the requirements of the program of study.

Home Unit approval for degree petition, as well as approval by the Bioinformatics Graduate Committee, will be required.

A student should choose a thesis advisor (from the Bioinformatics Program Faculty) and co-advisors within the first year of being in the PhD program. In the second year a student along with advisor are expected to assemble the thesis committee. The thesis committee should consist of a minimum of five faculty members. At least three members of the committee should be from Bioinformatics Program Faculty and at least two members of thesis committee should be from the home unit. Not later than in the middle of the third year a student has to present and defend a written PhD proposal.

RESEARCH PROGRESS

A student should meet with his/her thesis committee at least once a year to review the research progress.

PhD DISSERTATION

Within 5 years after entering the PhD program, the student is expected to complete the thesis research, and, typically, the student should have the results of the research published or submitted in peer reviewed journals. Upon submitting a written thesis and public defense and approval by the committee, the student is awarded the PhD degree.

Welcome to the MIT Computational and Systems Biology PhD Program (CSB)

The Ph.D. program seeks to train a new breed of quantitative biologists who can take advantage of technologies at the leading edge of science and engineering to tackle fundamental and applied problems in biology. Our students acquire: (i) a background in modern molecular/cell biology; (ii) a foundation in quantitative/engineering disciplines to enable them to create new technologies as well as apply existing methods; and (iii) exposure to subjects emphasizing the application of quantitative approaches to biological problems.  Our program and courses emphasize the logic of scientific discovery rather than mastering a specific set of skills or facts.  The program includes teaching experience during one semester of the second year.  It prepares students with the tools needed to succeed in a variety of academic and non-academic careers.

The program is highly selective with typical class sizes 8 to 10 students. About half of our graduate students are women, about one-quarter are international students, and about 10% are under-represented minorities.

Students complete most coursework during the first year, while exploring research opportunities through 1- or 2-month research rotations.  A faculty academic advisor assigned in the first year provides guidance and advice. Students choose a research advisor in spring or early summer of year 1 and develop a Ph.D. research project in with their advisor and input from a thesis committee chosen by the student.

Average time to graduation is 5½ years. 

The Program in CSB is committed to increasing opportunities for under-represented minority graduate students and students who have experienced financial hardship or disability.

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Thesis or Dissertation

Each graduate student in the program will work on a dissertation project under dual mentorship, consisting of a primary advisor who is Program Training Faculty, and a co-advisor who may or may not be Program Training Faculty, but must be from a different disciplinary area.

It is expected that the student will meet at least annually with the committee to update the members on his or her progress. As a partial fulfillment for the PhD degree, the student will submit a complete dissertation to be evaluated by a doctoral committee chosen by his or her mentors in consultation with the bioinformatics steering committee. The doctoral dissertation will be submitted to each member of the doctoral committee at least four weeks before the final examination. The student will defend his or her final thesis after the committee's evaluation and will pass or fail depending on the committee's decision.

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Education & Training

Phd in bioinformatics and systems biology with emphasis in biomedical informatics.

The PhD curriculum for our trainees consists of formal instruction to provide the intellectual framework for conducting research.

Biomedical Informatics Core

• Informatics in Clinical Environments (MED 265): 1 Students are introduced to the basics of healthcare systems and clinical information needs through direct observation and classroom discussion. Students are introduced to medical language, disease processes, and health care practices to provide context prior to direct patient observation at primary, specialty, emergency, and inpatient sites in conjunction with clinical faculty affiliated with the training program. Students examine how clinicians use history-taking, physical examination and diagnostic testing to establish diagnoses and prognoses. Medical decision-making is introduced in the context of available informatics tools and clinical documentation and communication processes. Post-observation classroom discussions encourage students to think critically of the processes they observed and formulate hypotheses about how informatics solutions can modify the processes.
• Modeling Clinical Data and Knowledge for Computation (MED 267): This course describes existing methods for representing and communicating biomedical knowledge. The course describes existing health care standards and modeling principles required for implementing data standards, including biomedical ontologies, standardized terminologies, and knowledge resources.

1  Students with a clinical background will replace MED 265 with an additional course: Bioinformatics Applications to Human Disease (MED 263).

Bioinformatics Core

The core courses provide foundations in the biological basis of human health and disease and the statistical discovery of medical knowledge from biological experimentation. These classes are taken during the first year.

• Bioinformatics II (BENG 202) :  Introduction to methods for sequence analysis, applications to genome and proteome sequences, and protein structure and sequence-structure analysis.
• Principles of Biomedical Informatics (MED 264) : students are introduced to the fundamental principles of BMI and to the problems that define modern healthcare. The extent to which BMI can address healthcare problems is explored. Topics covered include structuring of data, computing with phenotypes, integration of molecular, image and other non-traditional data types into electronic medical records, clinical decision support systems, biomedical ontologies, data and communication standards, data aggregation, and knowledge discovery.
• Bioinformatics IV (MATH 283):  Analysis of modern genomic data, sequence analysis, gene expression/functional genomics analysis, and gene mapping/applied population genetics. The course focuses on statistical modeling and inference.

For the fourth core class, choose one of the following. In the event that a student completes two or more of these with suitable grades, one will count as core and the other(s) as electives.

• Algorithms in Computational Biology (CSE 280A): (Formerly CSE 206B) The course focuses on algorithmic aspects of modern bioinformatics and covers the following topics: computational gene hunting, sequencing, DNA arrays, sequence comparison, pattern discovery in DNA, genome rearrangements, molecular evolution, computational proteomics, and others. Prerequisites: CSE202 preferred or consent of instructor. 
• Algorithms for Biological Data Analysis (ECE 208): This course introduces a series of general algorithmic techniques but uses computational evolutionary biology as the context. The course motivates each algorithmic concept using a specific biological application related to evolution and focuses the discussion on specific types of (big) data available in modern biological studies. Note: The instructor and the BISB program are in the process of getting approval from the Graduate Council to introduce this as a course and to allow it as a core option. While we await approval, the course is offered under a temporary course number, ECE 286, by Prof. Siavash Mirarab, with the title "Algorithms for Biological Data Analysis." The course code ECE 286 may be used by other special topics courses as well, so be sure to enroll in the correct one.
• Genomics, Proteomics, and Network Biology (Bioinformatics III, BENG 203/CSE283): This is core in the BISB track. In the BMI track, it may be taken as the 4th core class or as an elective. Anotating genomes, characterizing functional genes, profiling, reconstructioning pathways.  Prerequisites: Pharm 201, BENG 202/CSE282, or consent of instructor. 

All students in years 1 and 2 must take both seminars in fall, winter, and spring quarters.

• Current Trends in Biomedical Informatics (MED 262): Weekly talks by researchers introduce students to current research topics within BMI. Speakers are drawn from academia, health care organizations, industry, and government.
• Bioinformatics Student Research Talks (BNFO 283) : Weekly presentations by Bioinformatics and Systems Biology students about Research Projects that are proposed or completed. Faculty mentors are present to contribute critiques and suggestions.

All students must take one of the two ethics courses by the end of second year. However, funding sources may require that it be taken first year, so we recommend taking it the first year.

• Scientific Ethics (SOMI 226): see below description
• Ethics in Scientific Research (BIOM 219): Overview of ethical issues in scientific research, conflicts of interest; national, statewide and campus issues and requirement; ethical issues in publications; authorship; retention of research records; tracing of research records; attribution; plagiarism; copyright considerations; primary, archival and meeting summary publications; ethical procedures and policies; NIH, NSF, California and UC San Diego; case studies and precedents in ethics.

Research and Teaching

During the academic year, all students must be enrolled in the appropriate research course for their level. Students typically do three rotations in year 1 (BNFO 298) and then do research units (BNFO 299) with their thesis advisor in years 2 and later. BNFO 299 units may be varied to meet the full-time enrollment requirement of 12 units per quarter in fall, winter, and spring.

• Teaching Assistantship (TA) (BNFO 500) :  Students will be a TA for two quarters during second or third year. To prepare for this teaching, students will receive training through the Center for Teaching Development at UCSD.
• Research Rotation (BNFO 298) : Taken each quarter during first year to help determine the thesis adviser.
• Graduate Research (BNFO 299): Independent work by graduate students engaged in research and writing theses. S/U grades only. May be taken for credit fifteen times.

Students must take 16 units of elective courses, including 8 units from the BMI series and 4 units from the CS series. The final 4 units can be taken from any series. The two BMI core courses MED 265 (or MED 263 for students with a clinical background) and MED 267 count as electives. Please check this  BISB curriculum page  for the list of all approved electives and elective series. 

Formal Progress to Degree

There are three formal evaluations that students must complete prior to being awarded a PhD degree: 

• Qualifying Examination:  This examination must be passed prior to the end of the student’s second year of study. The written portion of the exam consists of the student preparing an NIH or NSF-style research proposal. This proposal is then defended in an oral examination. Once the student passes the oral portion of the exam, the student is deemed to be qualified for advancing into PhD thesis research.
• Advancement to PhD Candidacy:  Upon completion of formal course requirements, each student is required to take a written and oral qualifying examination that admits the student to the candidacy of the PhD Program. The exam is administered by the dissertation committee, which consists of five faculty members.
• Final Examination:  All students defend their thesis in a final oral examination.

How to Apply

Application for admission to graduate studies is made directly through the Bioinformatics and Systems Biology website.

To be considered for the NLM fellowship, in addition to submitting your application and documentation to the degree program of your choice, please send the following to dbmi fellowship at ucsd dot edu:

• Personal Statement- explaining why you are a good candidate for the fellowship and what you hope to accomplish as an NLM trainee, the specific kind of research and topics you are interested in studying and what your goals are after completing the fellowship.
• A current and up to date CV; and
• In the body of your email please indicate which degree program you are applying to.

Computational and Systems Biology PhD Program

Computational and systems biology.

The field of computational and systems biology represents a synthesis of ideas and approaches from the life sciences, physical sciences, computer science, and engineering. Recent advances in biology, including the human genome project and massively parallel approaches to probing biological samples, have created new opportunities to understand biological problems from a systems perspective. Systems modeling and design are well established in engineering disciplines but are newer in biology. Advances in computational and systems biology require multidisciplinary teams with skill in applying principles and tools from engineering and computer science to solve problems in biology and medicine. To provide education in this emerging field, the Computational and Systems Biology (CSB) program integrates MIT's world-renowned disciplines in biology, engineering, mathematics, and computer science. Graduates of the program are uniquely prepared to make novel discoveries, develop new methods, and establish new paradigms. They are also well-positioned to assume critical leadership roles in both academia and industry, where this field is becoming increasingly important.

Computational and systems biology, as practiced at MIT, is organized around "the 3 Ds" of description, distillation, and design. In many research programs, systematic data collection is used to create detailed molecular- or cellular-level descriptions of a system in one or more defined states. Given the complexity of biological systems and the number of interacting components and parameters, system modeling is often conducted with the aim of distilling the essential or most important subsystems, components, and parameters, and of obtaining simplified models that retain the ability to accurately predict system behavior under a wide range of conditions. Distillation of the system can increase the interpretability of the models in relation to evolutionary and engineering principles such as robustness, modularity, and evolvability. The resulting models may also serve to facilitate rational design of perturbations to test understanding of the system or to change system behavior (e.g., for therapeutic intervention), as well as efforts to design related systems or systems composed of similar biological components.

CSB Faculty and Research

More than 70 faculty members at the Institute participate in MIT's Computational and Systems Biology Initiative (CSBi). These investigators span nearly all departments in the School of Science and the School of Engineering, providing CSB students the opportunity to pursue thesis research in a wide variety of different laboratories. It is also possible for students to arrange collaborative thesis projects with joint supervision by faculty members with different areas of expertise. Areas of active research include computational biology and bioinformatics, gene and protein networks, regulatory genomics, molecular biophysics, instrumentation engineering, cell and tissue engineering, predictive toxicology and metabolic engineering, imaging and image informatics, nanobiology and microsystems, biological design and synthetic biology, neurosystems biology, and cancer biology.

The CSB PhD Program

The CSB PhD program is an Institute-wide program that has been jointly developed by the Departments of Biology, Biological Engineering, and Electrical Engineering and Computer Science. The program integrates biology, engineering, and computation to address complex problems in biological systems, and CSB PhD students have the opportunity to work with CSBi faculty from across the Institute. The curriculum has a strong emphasis on foundational material to encourage students to become creators of future tools and technologies, rather than merely practitioners of current approaches. Applicants must have an undergraduate degree in biology (or a related field), bioinformatics, chemistry, computer science, mathematics, statistics, physics, or an engineering discipline, with dual-emphasis degrees encouraged.

CSB Graduate Education

All students pursue a core curriculum that includes classes in biology and computational biology, along with a class in computational and systems biology based on the scientific literature. Advanced electives in science and engineering enhance both the breadth and depth of each student's education. During their first year, in addition to coursework, students carry out rotations in multiple research groups to gain a broader exposure to work at the frontier of this field, and to identify a suitable laboratory in which to conduct thesis research. CSB students also serve as teaching assistants during one semester in the second year to further develop their teaching and communication skills and facilitate their interactions across disciplines. Students also participate in training in the responsible conduct of research to prepare them for the complexities and demands of modern scientific research. The total length of the program, including classwork, qualifying examinations, thesis research, and preparation of the thesis is roughly five years.

The CSB curriculum has two components. The first is a core that provides foundational knowledge of both biology and computational biology. The second is a customized program of electives that is selected by each student in consultation with members of the CSB graduate committee. The goal is to allow students broad latitude in defining their individual area of interest, while at the same time providing oversight and guidance to ensure that training is rigorous and thorough.

Core Curriculum

The core curriculum consists of three classroom subjects plus a set of three research rotations in different research groups. The classroom subjects fall into three areas described below.

Modern Biology (One Subject): A term of modern biology at MIT strengthens the biology base of all students in the program. Subjects in biochemistry, genetics, cell biology, molecular biology, or neurobiology fulfill this requirement. The particular course taken by each student will depend on their background and will be determined in consultation with graduate committee members.

Computational Biology (One Subject): A term of computational biology provides students with a background in the application of computation to biology, including analysis and modeling of sequence, structural, and systems data. This requirement can be fulfilled by 7.91[J] / 20.490[J] Foundations of Computational and Systems Biology.

Topics in Computational and Systems Biology (One Subject): All first-year students in the program participate in / 7.89[J] Topics in Computational and Systems Biology, an exploration of problems and approaches in the field of computational and systems biology through in-depth discussion and critical analysis of selected primary research papers. This subject is restricted to first-year PhD students in CSB or related fields in order to build a strong community among the class. It is the only subject in the program with such a limitation.

Research Group Rotations (Three Rotations): To assist students with lab selection and provide a range of research activities in computational and systems biology, students participate in three research rotations of one to two months' duration during their first year. Students are encouraged to gain experience in experimental and computational approaches taken across different disciplines at MIT.

Advanced Electives

The requirement of four advanced electives is designed to develop both breadth and depth. The electives add to the base of the diversified core and contribute strength in areas related to student interest and research direction. To develop depth, two of the four advanced electives must be in the same research area or department. To develop breadth, at least one of the electives must be in engineering and at least one in science. Each student designs a program of advanced electives that satisfies the distribution and area requirements in close consultation with members of the graduate committee.

Additional Subjects: As is typical for students in other doctoral programs at MIT, CSB PhD students may take classes beyond the required diversified core and advanced electives described above. These additional subjects can be used to add breadth or depth to the proposed curriculum, and might be useful to explore advanced topics relevant to the student's thesis research in later years. The CSB Graduate Committee works with each graduate student to develop a path through the curriculum appropriate for his or her background and research interests.

Training in the Responsible Conduct of Research: Throughout the program, students will be expected to attend workshops and other activities that provide training in the ethical conduct of research. This is particularly important in interdisciplinary fields such as computational and systems biology, where different disciplines often have very different philosophies and conventions. By the end of the fourth year, students will have had about 16 hours of training in the responsible conduct of research.

Qualifying Exams: In addition to coursework and a research thesis, each student must pass a written and an oral qualifying examination at the end of the second year or the beginning of the third year. The written examination involves preparing a research proposal based on the student's thesis research, and presenting the proposal to the examination committee. This process provides a strong foundation for the thesis research, incorporating new research ideas and refinement of the scope of the research project. The oral examination is based on the coursework taken and on related published literature. The qualifying exams are designed to develop and demonstrate depth in a selected area (the area of the thesis research) as well as breadth of knowledge across the field of computational and systems biology.

Thesis Research: Research will be performed under the supervision of a CSBi faculty member, culminating in the submission of a written thesis and its oral defense before the community and thesis defense committee. By the second year, a student will have formed a thesis advisory committee that they will meet with on an annual basis.

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An all-encompassing, highly interdisciplinary field

Program Overview

The University of Michigan  Bioinformatics Graduate Program builds a strong foundation for Master’s and PhD students through comprehensive course offering, research training and mentoring.

Students in our program take courses in advanced math, modeling, statistics, computer programming, machine learning, informatics, comprehensive courses in introductory biology, genomics, proteomics, clinical informatics, environmental health, and much more. They are encouraged to take advantage of the enormous research and teaching resources across U-M.

Our students have an abundance of research opportunities in many subject areas under the mentorship of our 130 affiliated faculty members of the Center for Computational Medicine and Bioinformatics ( CCMB ). These faculty are from U-M Medical School, College of Engineering, College of Literature, Arts and Sciences, School of Public Health, School of Nursing, and School of Information. 

In 2023, the Bioinformatics Graduate Program maintains a student body of 87 PhD students, and over 120 Master's students. They are mentored by the 44 DCMB faculty and the 130 CCMB faculty. Faculty members with biological and more quantitative expertise are both well represented.

The Bioinformatics Graduate Program was created in 1999 and is housed in the Department of Computational Medicine and Bioinformatics ( DCMB ). 

Apply through our PIBS application

Bioinformatics offers an extensive range of research opportunities, from applications for clinical medical problems and specific diseases to computational work on synthetic biological systems. There are very active groups in:

• Artificial Intelligence (AI) and machine learning
• Genomics, regulatory genomics and epigenomics
• Protein structure, proteomics, and alternative splicing
• Multi-“omics” integrative bioinformatics
• Systems biology and networks analysis
• Biomedical data science, translational bioinformatics and pharmacogenomics
• Methodological development in computational biology
• Applications to complex genetic diseases
• 4D Nucleome
• Single Cell Analysis
• Signal/Image Processing and Machine Learning

Bioinformatics has had NIH supported training grants since 2005. Our students are eligible for a wide range of other training grant support related to more specific areas of research, such as genomics or cancer proteomics.

Students are required to take courses in each of the following areas:

• Introductory Bioinformatics
• Computing & Informatics
• Probability & Statistics
• Molecular Biology
• Bioinformatics 602 (Journal Club) taken once in the first year.
• Bioinformatics 603 (Journal Club) taken once; students present papers for discussion
• Research Responsibility and Ethics course (PIBS 503)
• One Advanced Bioinformatics course offered or cross-listed by the Bioinformatics Graduate Program
• One additional Advanced Bioinformatics course in any program

Details about courses available in each of these areas can be found on the department website . Courses may be offered by Bioinformatics or other units.

Attendance at weekly seminars is also expected. Offered seminars include a weekly series of invited guest speakers, “Tools & Tech” presentations that highlight a tool or technology, either under development or in current use, and BISTRO, a lively seminar where students present their ongoing research.

Preliminary Examination

Students take a preliminary exam in their second year, usually at the end of the 3rd or 4th term. The preliminary exam should show both creativity and skill, and should not be identical to the student’s thesis work. The aims of the examination are two-fold. The first aim is to demonstrate that students have developed the ability to analyze a scientific problem and develop appropriate strategies to carry out a research plan. The second aim is to demonstrate that students have enough Bioinformatics knowledge needed to carry out their thesis research. Students sometimes develop their prelim proposals into a paper and/or a thesis chapter later.

Teaching Requirement

Teaching, in Bioinformatics or in other departments, is encouraged and expected for at least one term from most Bioinformatics students. Individual circumstances such as English language ability, interest, and funding situation of the mentor are considered.

Expected Length of Program

The expected time to PhD graduation is 5 to 6 years.

Approximately 8-15 new students join the PhD program each year. Each term, contact between faculty and students is encouraged through research events & social gatherings. Given the interdisciplinary nature of the program, students are encouraged to develop and pursue their own research interests. In an effort to support students’ academic growth, the department and other units (such as Rackham Graduate School) offer funding to assist students with conference participation or workshop attendance.

Approximately 50% of program alumni choose academia, while others with go into industry with many working at biotechnology companies. Aware of this, current students are provided opportunities to meet with guest seminar speakers or visitors from industry. In addition, outside internships are encouraged if related to a student’s research as they have proven to be valuable experiences.

The program supports student-led initiatives that are focused on building community such as student organized social activities, a pre-Thanksgiving dinner, and group run in the local marathon. Separately, Bioinformatics coordinates an annual off-site weekend retreat and an annual picnic.

DCMB welcomes and supports several  student organizations :

• The Bioinformatics Graduate Student Association (BGSA)
• The Bioinformatics Black Student Union (BBSU)
• The Data Analysis Networking Group (DANG!)
• DCMB Girls Who Code Club

Alumni from the Bioinformatics Graduate Program pursue successful careers in academia, biotechnology and biomedical research in industry and government. Most of them are employed immediately after graduation.

Learn more about the Department of Computational Medicine and Bioinformatics.

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Every master’s degree thesis plan requires the completion of an approved thesis that demonstrates the student’s ability to perform original, independent research.

Students must choose a permanent faculty adviser and submit a thesis proposal by the end of the third quarter of study. The proposal must be approved by the permanent adviser who served as the thesis adviser. The thesis is evaluated by a three-person committee that is nominated by the program and appointed by the Division of Graduate Education. Students must present the thesis in a public seminar.

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Finished PhD theses

List of finished PhD theses under (co-)supervision of A. Goesmann.

The following PhD theses were conducted with support and/or supervision of A. Goesmann:

• Selection of a thesis laboratory
• Completion of the preliminary exam and thesis defense

Completion of course requirements

Laboratory Rotations for Selection of Thesis Laboratory

Admitted students will participate in three rotations in different laboratories, chosen by the student, during the fall semester of the first year of the program. These rotations will last approximately 4-5 weeks and will allow the student to work on a small project in the chosen laboratories to help the student and faculty decide which laboratory is the best fit for the student and their research interests. At the end of the fall semester in the first year, the student will choose a permanent laboratory to complete the remainder of their thesis project.

Fall 2021 Rotation Schedule

Important Dates

Aug 11–Aug 17 Graduate Orientation Week Aug 20  Student's first rotation requests due to Dr. Hollenhorst (no later than 12 pm) Aug 23–Sept 24 First rotation period (five weeks) Sept 22 Student's second rotation requests due to Dr. Hollenhorst (no later than 5 pm) Sept 27–Oct 29 Second rotation period (five weeks) Oct 27 Student's third rotation requests due to Dr. Hollenhorst (no later than 5 pm) Nov 1–Dec 8 Third rotation period (five weeks one day, not including R/F of Thanksgiving week) Dec 13 Student thesis lab request due to Dr. Hollenhorst (no later than 5 pm) Dec 15 Students are informed of thesis lab assignment and begin thesis research

Completion of Exams

All students will be required to write a thesis project proposal and orally defend this proposal to their thesis committee during the fall semester of the third year of the program to reach candidacy. After completion of the thesis project, students will be required to provide a written thesis and give an oral defense to their thesis committee.

Course Requirements

A total of 90 credit hours are required for partial fulfillment of the Cell, Molecular and Cancer Biology PhD degree. Of these, 24.5 credits are earned by completing the course requirements for the Cell, Molecular and Cancer Biology major (see below). An additional 6-12 credits are earned by completing the requirements for a minor (not listed below). All remaining credits are earned through enrollment in M800, independent thesis research. Required courses for the major include:

• BIOT-T 540: Structure, Function and Regulation of Biomolecules
• BIOL-L585: Genetics and Bioinformatics
• MSCI-M 510: Research Methods
• BIOL-L 523: Critical Analysis of Scientific Literature
• MSCI-M 580 Molecular Biology of Cancer
• MSCI-M 509: Basics of Scientific Communication
• MSCI-M 512: Grant Writing
• MSCI-M 508: Precision Medicine of Cancer
• MSCI-M 550: Seminar in Cancer Biology
• MSCI-M 800: Research

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Doctoral Thesis: Approximation and system identification techniques for stochastic biomolecular systems

By: Theodore W. Grunberg

Thesis Supervisor Domitilla Del Vecchio

• Date: Tuesday, August 6
• Time: 12:00 pm - 1:30 pm
• Category: Thesis Defense
• Location: Room 3-133

Additional Location Details:

Abstract: Many biomolecular systems can be modeled as chemical reaction networks with a set of relevant species interacting via chemical reactions. When the molecular counts of the species are small, the inherent stochasticity in the occurrence of the reactions plays an important role in the behavior of the system. This stochasticity presents opportunities for system identification, since when a large population of cells is measured, one has many samples from the underlying distribution of the stochastic model. On the other hand, using the stochastic models of chemical reaction networks, given by continuous time Markov chains with countably infinite state spaces, creates computational and analytical difficulties when performing analysis or system identification. Therefore, approximate models that exploit timescale separation between different sets of chemical reactions to create reduced order models, or deterministic or diffusion approximations that approximate the continuous time Markov chain with an ordinary differential equation or stochastic differential equation respectively must be exploited. This thesis makes contributions in both directions, rigorously justifying such approximations as well as developing the theory to perform system identification on the approximate models.

• Email: [email protected]

M.Sc. in Bioinformatics

Technological advances have led to an explosion in the amount of biological information available to scientific communities, governments, and industry.

The challenge now is how to organize, visualize, and interpret this vast amount of information. Bioinformatics seeks to make sense of biological processes on all scales, from the molecular level to full ecosystems, using powerful and efficient computational techniques. An effective bioinformatician can apply computational and statistical tools to answer diverse biological questions.

Students in the Master of Science in Bioinformatics program develop a wide range of skills that prepare them for a future career in academia, industry, or government.

Degree Details

The Master of Science in Bioinformatics program is a traditional thesis-based program and thus places more emphasis on cutting-edge bioinformatics research. Areas of focus have included agricultural science, ecology, evolution, genetics, medicine, and veterinary science.

M.Sc. students will typically take six semesters to work on a traditional thesis-based research project and write a thesis. This program will provide students with the opportunity to develop research and communication skills in bioinformatics at an advanced level. Students will take four courses and can enter the program during any semester. M.Sc. applicants must indicate an agreed advisor at the time of application.

Collaborative Specialization

The M.Sc. in Bioinformatics program is also affiliated with the M.Sc./M.A.Sc. Collaborative Specialization in Artificial Intelligence (CSAI) at the University of Guelph. Applicants wishing to enter the CSAI program through Bioinformatics will be required to meet the same criteria as the M.Sc. in Bioinformatics criteria, as described in the Program Outline page listed above, in addition to the CSAI criteria. 

Interdisciplinary Program

Our interdisciplinary program aims to provide students with broad research and experiential opportunities to help meet their career goals. Over 50 researchers in funded laboratories in departments across campus are actively engaged in the bioinformatics graduate programs, thus providing students with opportunities to conduct cutting-edge and impactful research in a wide range of fields.

All students have graduate advisory committees comprised of faculty in both life sciences and computational sciences to ensure that students have integrative and multidisciplinary research experiences.

The Master of Science in Bioinformatics program is run by four colleges at the University of Guelph: the College of Biological Science, College of Engineering and Physical Sciences, Ontario Agricultural College, and Ontario Veterinary College.

Meet an M.Sc. in Bioinformatics Graduate

Excited about the bioinformatics I had learned in my undergrad, I decided to pursue the MSc in Bioinformatics program at the University of Guelph. During my master’s degree at Guelph, I did research in microbial ecology. I enjoyed programming in R for my thesis and having the opportunity to teach undergraduate biostatistics through my TA position.Now that I’ve completed my MSc, I am pursuing a PhD in biology at Carleton University. My doctoral research is laboratory-based, working in plant biochemistry. While I will not be primarily doing bioinformatics for my PhD, the programming skills I learned my MSc will help me in the data analysis stages of my PhD.

Alicia Halhed , Alumni M.Sc.BINF. ‘21

Meet Some of Our Faculty

Admission Details

Admission requirements.

Students entering the M.Sc. program will have completed an Honours Bachelor’s degree with a minimum admission average of B (75% and higher) in the last two years of full-time equivalent study in any of the following or related fields:

• Life sciences
• Physical sciences
• Mathematics
• Computational sciences

Application Process

Students must apply for the M.Sc. in Bioinformatics program through the Office of Graduate and Postdoctoral Studies at the University of Guelph. Interested candidates are encouraged to apply at least four months prior to the intended start date (six to eight months prior for international applicants).

All applicants should include a statement of research intent with their application.

Please note that once an application is submitted for a specific program,  it cannot be switched to one of the other programs . The applicant will have to re-apply to the other program and, therefore, pay the application fee again. Thus, it is important to ensure that the correct program was selected.

M.Sc. applicants  must  indicate an agreed advisor at the time of application. Prospective students interested in the M.Sc. program should commence discussions with faculty well in advance of applying. Offers of admission will only be issued in cases where a member of Bioinformatics Graduate Faculty has agreed to be the advisor.

If your first language is not English, you will be required to submit the results of a standardized language test. For applicants who speak English as a second or additional language, you may request consideration for a waiver of the English language requirement once you have submitted your application if you have successfully completed a Bachelor's or Master's university degree in English from Canada, Australia, New Zealand, the United States, and/or the United Kingdom. Please email [email protected] for more information. In all other situations, an English proficiency test score is required with your application.

Degree Requirements

A total of 2.0 credits are required, which must include:

• BINF*6110 [0.50] Genomic Methods for Bioinformatics
• BINF*6210 [0.50] Software Tools for Biological Data Analysis and Organization

View a full list of courses in the Academic Calendar .

The advisory committee and/or the graduate program committee may require additional courses.

When the course work is satisfactorily completed, the submission and successful defence of an appropriate thesis on an approved topic completes the requirements for the M.Sc. in Bioinformatics.

Finding an M.Sc. Supervisor

Having an established supervisor is required for admission into the M.Sc. Bioinformatics program. Visit Before You Apply for helpful advice on identifying and reaching out to faculty members. Before contacting members of our graduate faculty, please review some of our suggested tips below to optimize the success of your communication:

• Be informed:  Ensure you review the research areas of our graduate faculty before contacting them. Take some time to review their information, publications, and the specifics of the faculty member’s research by browsing their departmental webpage and research group website, if available.
• Use concise, targeted communication:  Graduate faculty supervisors receive numerous emails from prospective graduate students on a daily basis. Therefore, as most faculty members have very limited time, you must communicate your information as clearly and concisely as possible. Use short paragraphs, keep the length of your email to a minimum, use a descriptive email title and be professional.
• Stand out from the crowd:  Highlight specific and clear reasons why you would be a good candidate for working with the chosen faculty member. Include information that will set you apart from other candidates such as notable achievements/scholarships, publications, similar research interests and/or related experience.
• Communicate early:  Start contacting faculty members at least 9-12 months in advance of the application deadlines. This is especially important for international applicants to ensure you have sufficient time to apply for a study permit/visa. Finding a supervisor can sometimes take months to establish.
• Be patient:  Our graduate faculty members are very busy, especially during the start and end of the semester. Therefore, it may take some faculty members days or even weeks to respond to your email. Follow up if it has been a couple of weeks with no response.

Current Opportunities for M.Sc. Students

M.sc. position to study erythrocyte fatty acid signatures associated with dairy intakes and cardiometabolic disease risk factors in a canadian population.

Dr. David M Mutch (Human Health and Nutritional Sciences)

Project description : Dairy foods and beverages provide various nutrients that are important for health and development, including vitamins, minerals, protein, carbohydrates, and fatty acids. Despite this, many Canadians are eating less dairy due to contradictory messaging about its effect on health. The relationship between dairy and health is complex, and may depend on the amount of dairy consumed, the type of dairy consumed, and the overall fat content of the dairy consumed. These differences may be due to the varying fatty acid compositions of different dairy products that can then modify blood fatty acid profiles in distinct ways. This is particularly important because blood fatty acid profiles are now considered markers of disease risk. The overall goal of this research project is to apply supervised and unsupervised clustering methods to investigate the relationships between dairy intakes, blood fatty acids, and risk factors for common diseases in a representative Canadian population using data collected in the Canadian Health Measures Survey (CHMS) study by Stats Canada.

The successful candidate will use supervised and unsupervised clustering techniques, as well as multiple linear regression, to explore the relationship between blood fatty acid profiles in different dairy intake groups and their associations with quantitative cardiometabolic risk markers in ~4,000 Canadians. The data used was collected as part of the Canadian Health Measures Survey (CHMS) study by Statistics Canada and will be accessed through a secure Stats Canada facility at the University of Guelph main campus.

How to apply : Interested candidates should send a CV, a copy of undergraduate transcripts, names of three references, and a cover letter stating interest in the above-mentioned research topic to Dr. David M Mutch at [email protected] .

Selection of the successful candidate is based on a combination of academic criteria, relevant experience, and referees’ evaluations. Previous experience in programming (e.g., R ) and knowledge of statistical methods such as clustering analysis and regression models will be considered an asset. Review of applications will begin immediately and continue until the position is filled. A start date as early as September 2024 is possible.

M.Sc. position to study the relationship between host genetics and pig gut microbiome 

Dr. Brandon Lillie (Pathobiology), Dr. Khurram Nadeem (Mathematics and Statistics), Dr. Vahab Farzan (Pathobiology and Population Medicine) 

Project description : The microbial colonization of the neonatal and adult gut plays a key role in function of immunological and metabolic pathways that influence disease resistance, health, and performance of pigs. Indeed, the mechanism-of-action of many commercially available intervention strategies and products designed to promote health and performance in pigs and replace antibiotics, is associated with their demonstrated ability to modify the composition of the gut microbiome. The application of molecular and bioinformatics methods has demonstrated that the gut microbiome is abundant, complex, and highly dynamic, partly influenced by host genetics. The overall goal of this M.Sc. project is to investigate the relationship between host genetics and the swine gut microbiome. The project is specifically aiming to identify the single nucleotide variants with known roles in innate immunity and host resistance to microbes, as well as SNPs spaced across the entire genome, using a genome wide association study (GWAS) approach.   

Job description : The successful candidate will develop and utilize bioinformatics pipelines to model various outcomes related to microbiome (Ex. alpha and beta diversity, microbiome composition) against single nucleotide variants through statistical modeling and genome wide association study.  

Selection of the successful candidate is based on a combination of academic criteria, relevant experience, and referees’ evaluations. Previous experience in programming (e.g., R or Python) and knowledge of statistical methods such as regression analysis will be considered an asset. Review of applications will begin immediately and continue until the position is filled. The interested candidates should send a CV, a copy of undergraduate transcripts, a code example, a writing sample, names of three references, and a cover letter stating interest in the above-mentioned research topic to Dr. Vahab Farzan at  [email protected] .   

The University of Guelph is committed to an Employment Equity Program that includes special measures to achieve diversity among its faculty and staff. We therefore particularly encourage applications from members of underrepresented groups.  

A funding package is offered to thesis-based graduate students with their offer letter, which may vary by home department (i.e., the department of the primary supervisor). The package may consist of Graduate Teaching Assistantships (GTA) and Graduate Research Assistantships (GRA). You are required to contact prospective advisors to discuss the availability of projects prior to applying to thesis-based programs, and we suggest you also discuss the availability of GRA funding or prospects for applying for suitable scholarships. Please note that offers of admission will only be issued in cases where a member of the Bioinformatics Graduate Faculty has agreed to be the supervisor.

Scholarships and bursaries are available from the University, which can be  searched for here . For some scholarships and bursaries, you are automatically considered and do not need to apply. Other scholarships and bursaries require a separate application. Please read over the description for each one you are interested in.

The Ontario Graduate Scholarship (OGS)  is provided by the Government of Ontario, and the Government of Canada's Natural Sciences and Engineering Research Council (NSERC) offers a number of scholarships. These scholarships require a separate application, typically due in the fall for the following academic year. You are encouraged to speak to potential supervisors regarding these government-sponsored scholarships.

Master of Science in Bioinformatics collaborative specialization in AI M.Sc.BINF+AI

In addition to funding available to M.Sc. in Bioinformatics graduate students described above, domestic and international applicants applying for the Master of Science in Bioinformatics Collaborative Specialization in Artificial Intelligence (CSAI) program, M.Sc.BINF+AI can apply to the Vector Institute's Vector Scholarship In Artificial Intelligence . This scholarship requires a separate application, typically due in the winter for the following academic year. You are encouraged to speak to potential supervisors regarding this scholarship during the application process.

Program Fees

For information about tuition and fees for the M.Sc. in Bioinformatics program, please see the Cost of Tuition/Living webpage .

A detailed breakdown of semester fees can be found on the Student Financial Services website .

Is there an application deadline for the M.Sc. program?

There is no application deadline for the program; however, applicants are required to have secured a faculty advisor prior to submitting an application to the University. Interested candidates are encouraged to apply at least four months prior to the intended start date (6-8 months prior for international applicants).

What documents do I need to submit for my M.Sc. application?

The program requires your OUAC application, unofficial transcripts* and any relevant supporting documents, CV, statement of research intent, and two referee assessment forms with your application.

*Please do not upload Exam Marksheets; transcripts are required.

When can I start the M.Sc. program?

Students can begin the program January 1, May 1, and September 1 each year. Please note that offers of admission will only be issued in cases where a member of Bioinformatics Graduate Faculty has agreed to be the advisor.

Will I be funded during my M.Sc. degree?

Yes, students can expect to receive a stipend when enrolled in either the M.Sc. or PhD degree. The funding scheme for the student will depend on the department/college to which your faculty advisor belongs. For example, if the student’s faculty advisor is in the College of Biological Sciences, then the student’s funding scheme will abide by the rules of this college. 

Will I have the opportunity to apply for graduate teaching assistantships (GTA) during my M.Sc. degree?

Similar to the funding scheme, GTAs are handled according to the rules and regulations established in the department and college of your faculty advisor. Therefore, your ability to apply for GTAs will depend on the department and college to which your faculty advisor belongs.

Am I eligible for scholarships and awards?

There are a number of graduate awards available at the University of Guelph. For a complete list of these awards, please go to: Graduate Award Search . Please note that the application process will depend on the department and college to which your faculty advisor belongs. All students can apply for external scholarships managed by provincial and federal funding agencies (e.g., NSERC, CIHR, OGS, etc).

Do I have to take courses during my M.Sc. program?

Yes, you will be required to take courses.

For the M.Sc. program, students are required to take a minimum of 2.0 course credits, which must include BINF*6110 and BINF*6210. The student’s advisory committee may require additional courses be taken.

Can I fast track from the M.Sc. program to the PhD program?

Yes, students can switch to the PhD program during their M.Sc. program. This can only happen if the student, the faculty advisor, and members of the graduate advisory committee are all in agreement. If everyone is in agreement, then this switch must happen during the student’s 4th semester of the M.Sc. program.

Dr. Emily Berzitis, Bioinformatics Program Manager [email protected] 519-824-4120 x 56474

Dr. Steffen Graether, Graduate Program Coordinator [email protected] 519-824-4120 x 56457/54590

Dr. Jennifer Geddes-McAlister, Director [email protected]

Doctor of Public Health Program

The Doctor of Public Health (DrPH) is a fully online program designed for professionals who seek to expand their knowledge of evidence-based public health and aspire to excel in leadership roles that impact the complex public health challenges we face, both today and in the future.

Who is the DrPH Program For?

The DrPH program is designed for professionals who are ready for an advanced degree and higher levels of public health leadership.

Applicants typically have:

• An MPH/MSPH or equivalent master’s degree, although it is not required. (Students who have not earned an MPH or MSPH from an accredited institution will be required to complete specific coursework in addition to the regular DrPH curriculum).
• Three years of professional work experience at the time of matriculation. (Students may begin the application process once they have acquired two years of full-time work experience

Is a DrPH or PhD Right for Me?

While both a DrPH and PhD provide rigorous, doctoral-level training in public health, there are important differences to keep in mind as you decide which degree to pursue. Learn more about the differences between a DrPH and PhD below.

DrPH applications are completed through SOPHAS. Admissions requirements can be found here.

What Can I Do with a DrPH?

Our strong, global network of Rollins alumni work in a variety of sectors, from government, to industry, and everything in between.

The DrPH program uniquely positions participants to:

• Shape public health policies.
• Spearhead health promotion initiatives.
• Conduct impactful applied research.
• Build collaborative partnerships.
• Apply sophisticated communication strategies.

Professionals with a DrPH degree are in positions such as:

• Public Health Program Director
• Health Services Director
• Health Policy Advisor
• Global Health Director
• Non-Profit Executive Director

DrPH graduates will obtain a refined skill set and an intricate understanding of methodologies crucial for becoming public health leaders. Their preparation extends beyond academic knowledge, equipping them with the experience and tools to lead and influence with distinction, ultimately contributing significantly to the advancement of public health on both local and global scales.  

Program Overview

The DrPH curriculum cultivates foundational competencies essential for effective leadership in public health, including:  

• Mastery of data and analysis
• Adeptness in leadership
• Understanding of policy, programs, and educating the public health workforce

This fully online program offers two concentrations:

Public Health Preparedness and Response

Implementation and evaluation science.

Public health preparedness and response is an interdisciplinary field that focuses on leading, coordinating, and implementing systems in response to local, regional, national, or global public health threats.

Doctoral training in public health preparedness and response encompasses foundational methods coursework to:

• Assess community risk through appropriate sampling strategies.
• Facilitate rigorous data collection, analysis, and data linkage to emerging health problems.
• Engage in timely and science-driven communication to the public through data dashboards and other visualization methods.

This concentration prepares graduates for leadership roles, providing them with the knowledge and skills to lead teams in times of crisis and to communicate and collaborate effectively with external partners at the local, regional, national, and global level across various industries.

Graduates are well-prepared for careers as directors of public health preparedness at public health agencies such as the Centers for Disease Control and Prevention, state and local health departments, the Administration for Strategic Preparedness and Response, and the World Health Organization, as well as other leadership roles within hospital systems, private industry, and the military.

Implementation and evaluation science is a multidisciplinary field focused on the systematic study of strategies and processes for effectively adapting, adopting, and scaling evidence-based interventions and assessing outcomes for diverse populations and contexts.

This training encompasses foundational methods coursework to enable the design, implementation, and evaluation of evidence-based interventions, including policies, practices, and programs. The primary goal is to bridge gaps between what we know (research) and what we do (practice and policy) to improve population health.

Students will learn to:

• Identify contextual factors that influence successful design and implementation of evidence-based interventions.
• Employ implementation strategies to increase effective service delivery.
• Apply implementation science and evaluation theories, models, and frameworks.
• Measure implementation outcomes and impacts.
• Inform iterative improvements.

This concentration prepares graduates for leadership roles, providing them with the knowledge and skills to work collaboratively and to scale evidence-based strategies that ultimately amplify effectiveness and improve outcomes in health promotion and disease prevention, public health policies, and health care systems.

Program Structure 

The DrPH is a fully online, 60 credit-hour program that can be completed full- or part-time in three to seven years, consisting of:

• Foundational courses – 25 credits
• Implementation and Evaluation Sciences
• Applied Practice Experience – 3 credits
• Integrative Learning Experience (dissertation research/project) – 11 credits

Comprehensive Exam

The online, asynchronous format of the DrPH program offers convenience and flexibility for professionals to complete their studies while working full time.

Foundational Curriculum

The foundational curriculum consists of 25 credit hours of coursework which all students, regardless of their selected concentration, must complete.

* Specific course titles and associated credits are subject to change. 

Concentration Curriculum 

In addition to the foundational curriculum, students will complete 11-12 credit hours of coursework within their selected concentration, in addition to 9-10 credit hours of elective courses.

*Specific course titles and associated credits are subject to change. 

Elective Courses

Students will choose elective courses from the options below to align further training with their unique interests (i.e., application of implementation and evaluation science or preparedness and response in the context of global health, environmental health, behavioral/social science, etc.). A minimum of 10 credit hours are required for Implementation and Evaluation Science students and 9 credit hours for Preparedness and Response students. List of elective courses is subject to change.

Applied Practice Experience

The Applied Practice Experience (APE) is a unique opportunity that enables students to apply their knowledge learned through coursework and leadership skills to a professional setting that complements the student’s interests and career goals. The APE can be completed in the student’s workplace when appropriate; however, the project and associated deliverable(s) must be distinct from the student’s daily work responsibilities.  

All DrPH students must complete a 200-hour APE, regardless of their number of years in the workforce or other relevant experience.

Integrative Learning Experience

The DrPH dissertation research or project is the student’s final comprehensive written product that demonstrates mastery of DrPH foundational and concentration competencies.

Students will register for a total of 11 Integrative Learning Experience credits, including:

• Dissertation Research/Project Seminar (first year)
• Dissertation Proposal Planning Seminar (fall of second year)
• Dissertation Proposal Development (spring of second year)
• Dissertation (final year)

The dissertation topic will be selected by the student with guidance from their faculty advisor. Consistent with the nature of the DrPH as an applied degree, we expect that dissertations will apply doctoral-level research and practice skills to address higher-level leadership, policy, and critical public health problems in an evidence-based, methodologically rigorous manner. 

The dissertation can be in one of three formats:

• Five-chapter format: This is the traditional dissertation format consisting of a) the problem statement and specific aims; b) comprehensive literature review; c) data collection and research methods; d) data analyses and results; e) discussion of findings and implications/recommendations for policy and public health practice.
• Manuscript format: A minimum of three manuscripts that must be submitted for publication in a refereed public health related journal following the formatting guidelines of the journal. The dissertation submission must include introductory and concluding chapters synthesizing the work across manuscripts.  
• Public health project portfolio: this format requires selecting one or more practice-based projects in an organization (e.g., organizational assessment, program evaluation, program plan/implementation, economic evaluation). The number of required projects and target deliverables depends on the scope of the projects and will be determined in collaboration with the dissertation advisor.  

Students must complete key foundational methods, leadership courses, and the four required concentration courses before scheduling their Comprehensive Exam (CE). Students will be required to maintain a professional portfolio that will serve as the basis for their CE to evaluate their readiness to advance to candidacy. The CE will consist of a reflective written document and an oral defense.

Recommended Course Sequence

The recommended course sequence for each concentration is based on a full-time student enrolled in a minimum of 9 credit hours per semester. Part-time students will take a reduced course load each semester.

Cost of Attendance

The cost of DrPH tuition is $1,311 per credit hour*.

* Tuition cost is pending approval in February 2025 .

Visit our DrPH admissions page for information about requirements and how to apply.

View All Events

Thesis/Dissertation Seminars: Planning your Journey

Thursday, 06 Jun 2024 from 1:00 pm to 2:00 pm

One Summer Session for 2024!

The Graduate College and the Center for Communication Excellence invite you to a seminar to  navigate the Graduate College requirements and deadlines, submission and review procedures, and writing support programs.  Attend a seminar to get an overview of the different aspects of the thesis/dissertation journey so that you can be better prepared. 

Watch the informational videos  in this playlist,  Thesis/Dissertation Seminar Series ,  BEFORE  attending the seminar.

• Graduate College Requirements (1 video)
• Submission and Review Process (2 videos)
• Writing Support (1 video)

During the informational seminar sessions, Graduate College staff members will answer questions about deadlines, formatting guidelines, and provide other helpful hints for working on your thesis/dissertation. Register for one or more informational seminar sessions today! 

Click here to register  for the following session(s).

• Thesis/Dissertation Informational Session I | June 6,  1pm - 2pm | ( *Virtual session,  WebEx)

*Links for connecting to virtual sessions via WebEx will be sent prior to the day of the session.

If you register late and need access to the session, please contact  [email protected]  to get connected. 

Office of Medical Communications

Uvm cancer center trainees successfully defend phd dissertations.

Alyssa Richman, PhD, and Cong Gao, PhD

The University of Vermont (UVM) Cancer Center is proud to facilitate the training of the next generation of cancer scientists. Recently,  Alyssa Richman, PhD , and  Cong Gao, PhD , successfully defended their PhD dissertations, joining previous trainees in the cancer workforce. Richman and Gao, both graduate students in the lab of  Seth Frietze, PhD , contributed substantial work to our understanding of epigenetic changes and their potential as therapeutic avenues. Epigenetic changes are reversible changes to DNA that result in alterations in gene expression. The dissertation work completed by Richman and Gao provides important insight into how these reversible changes could serve as novel therapeutic targets. 

Drawn to the supportive environment at UVM,  Richman  joined the Frietze lab to pursue her passion for cancer research. Recognizing the need to develop treatments that can effectively target cancer cells while preserving the integrity of healthy cells, Richman saw the opportunity to use innovative DNA sequencing-based approaches to identify cancer-specific therapeutic targets. Her research focused on acute lymphoblastic leukemia, the most common childhood malignancy. Richman studied how a tumor suppressor, Ikaros, which is mutated in high-risk acute lymphoblastic leukemia, impacted leukemia cell growth. She conducted a comprehensive analysis using multiple epigenetic datasets to evaluate the effect of Ikaros on gene regulation and epigenetics. The characteristic epigenetic alterations caused by Ikaros in leukemia cells provide insight into disease pathobiology, opportunities for biomarkers, and candidate targets for personalized therapies. Her work was supported in part by a Cancer Center Summer Student Fellowship, which provided valuable funding and resources for her research. In addition to her individual studies, she co-facilitated a journal club initiated last fall, bringing together the Cancer Cell and Redox Biology and Pathology groups to discuss current advances in cancer biology research. Richman is in the process of preparing multiple manuscripts to share this work and recently moved to Boston to continue her work in the biomedical research field. 

With a desire to conduct interdisciplinary research in a collaborative environment,  Gao  joined the Frietze lab to study epigenetic modifications in breast cancer progression. Gao focused on histone modifications which she compares to bookmarks in a book. The bookmarks or histone modifications are important for knowing where you left off. If a bookmark location is altered it will change the readers understanding of the story. In the case of histone modifications, these changes alter gene expression, causing genes to become hidden or overactive. Gao’s dissertation work provides a detailed map of histone modifications that occur in breast cancer progression, identifying critical regulatory elements and potential therapeutic targets. Her work highlights how the reversibility of epigenetic changes could allow for “turning off” cancer promoting genes without altering the underlying genetic code. To explore this therapeutic potential further, future work will validate the findings of Gao’s study in clinical samples. Gao credits the UVM Cancer Center for providing critical access to advanced sequencing technologies and bioinformatic analysis tools that supported her work. Since completing her PhD, Gao has continued her research in epigenetic mechanisms of cancer and is seeking bioinformatic scientist opportunities. In Gao’s words, “a journey in cancer research is a testament to the power of curiosity, collaboration, and perseverance”. 

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SPHIS Home » News » New Faculty, Tenure and Promotions

New faculty, tenure and promotions.

August 2024

Promotions and Tenure

The School of Public Health & Information Sciences is pleased to announce the following faculty actions approved by the Board of Trustees on June 27, 2024.

• Stephanie Boone , PhD, MPH, Assistant Professor of Epidemiology and Population Health was promoted to Associate Professor and awarded tenure.
• Aishia Brown , PhD, Assistant Professor of Health Promotion and Behavioral Sciences was promoted to Associate Professor and awarded tenure.
• David Johnson , PhD, MPH, Assistant Professor of Health Management and Systems Sciences was promoted to Associate Professor.
• Doug Lorenz , PhD, Associate Professor (Tenured) of Bioinformatics and Biostatistics was promoted to Professor (Tenured).

Dr. Shih-Ting Huang will join the Dept. of Bioinformatics and Biostatistics in August. He has a master’s degree in mathematics from Temple University and a PhD in Mathematics with specialization in statistics from Ruhr-University at Bochum in Germany. He is currently a postdoctoral research associate in the Division of Public Health Sciences at the Washington University School of Medicine in St. Louis.

Dr. Huang’s research interests include targeted deep learning, machine learning, personalized medicine, and sample-specific cooperative learning to integrate heterogeneous radiomics and pathomics data. His expertise in these areas in addition to a solid methodological background will be valuable assets to our department, the school and the university. Dr. Shih-Ting Huang’s office will be Room 128.

The Dept. of Health Management & Systems Sciences welcomes Melissa Eggen , PhD, MPH, as an Assistant Professor (Tenure Track) beginning August 1, 2024. Her areas of specialty include maternal-child health, modes of delivery, and breastfeeding. Dr. Eggen exhibits exceptional abilities in policy formulation. Her proficiency in analysis and writing will contribute to her career advancement and the mentorship of graduate students.

Dr. Eggen has been an instructor in the department since Sept. 2023, teaching courses and leading research grants. Prior to that, she worked as program manager and senior policy analyst for various projects within the Center for Health Organization Transformation and the Commonwealth of Institute of Kentucky.

Dr. Eggen earned her PhD in Public Health Sciences with a specialization in Health Management and Policy in May 2024 and was awarded the John M. Houchens Prize for Outstanding Dissertation. She also has an MPH in Maternal and Child Health from the University of Illinois Chicago.