About the Program
Under the auspices of the Center for Computational Biology, the Computational Biology Graduate Group offers the PhD in Computational Biology as well as the Designated Emphasis in Computational and Genomic Biology, a specialization for doctoral students in associated programs. The PhD is concerned with advancing knowledge at the interface of the computational and biological sciences and is therefore intended for students who are passionate about being high functioning in both fields. The designated emphasis augments disciplinary training with a solid foundation in the different facets of genomic research and provides students with the skills needed to collaborate across disciplinary boundaries to solve a wide range of computational biology and genomic problems.
Admission to the University
Minimum Requirements for Admission
The following minimum requirements apply to all graduate programs and will be verified by the Graduate Division:
- A bachelor’s degree or recognized equivalent from an accredited institution;
- A grade point average of B or better (3.0);
- If the applicant has completed a basic degree from a country or political entity (e.g., Quebec) where English is not the official language, adequate proficiency in English to do graduate work, as evidenced by a TOEFL score of at least 90 on the iBT test, 570 on the paper-and-pencil test, or an IELTS Band score of at least 7 on a 9-point scale (note that individual programs may set higher levels for any of these); and
- Sufficient undergraduate training to do graduate work in the given field.
Applicants Who Already Hold a Graduate Degree
The Graduate Council views academic degrees not as vocational training certificates, but as evidence of broad training in research methods, independent study, and articulation of learning. Therefore, applicants who already have academic graduate degrees should be able to pursue new subject matter at an advanced level without the need to enroll in a related or similar graduate program.
Programs may consider students for an additional academic master’s or professional master’s degree only if the additional degree is in a distinctly different field.
Applicants admitted to a doctoral program that requires a master’s degree to be earned at Berkeley as a prerequisite (even though the applicant already has a master’s degree from another institution in the same or a closely allied field of study) will be permitted to undertake the second master’s degree, despite the overlap in field.
The Graduate Division will admit students for a second doctoral degree only if they meet the following guidelines:
- Applicants with doctoral degrees may be admitted for an additional doctoral degree only if that degree program is in a general area of knowledge distinctly different from the field in which they earned their original degree. For example, a physics PhD could be admitted to a doctoral degree program in music or history; however, a student with a doctoral degree in mathematics would not be permitted to add a PhD in statistics.
- Applicants who hold the PhD degree may be admitted to a professional doctorate or professional master’s degree program if there is no duplication of training involved.
Applicants may apply only to one single degree program or one concurrent degree program per admission cycle.
Required Documents for Applications
- Transcripts: Applicants may upload unofficial transcripts with your application for the departmental initial review. Unofficial transcripts must contain specific information including the name of the applicant, name of the school, all courses, grades, units, & degree conferral (if applicable).
- Letters of recommendation: Applicants may request online letters of recommendation through the online application system. Hard copies of recommendation letters must be sent directly to the program, by the recommender, not the Graduate Admissions.
Evidence of English language proficiency: All applicants who have completed a basic degree from a country or political entity in which the official language is not English are required to submit official evidence of English language proficiency. This applies to institutions from Bangladesh, Burma, Nepal, India, Pakistan, Latin America, the Middle East, the People’s Republic of China, Taiwan, Japan, Korea, Southeast Asia, most European countries, and Quebec (Canada). However, applicants who, at the time of application, have already completed at least one year of full-time academic course work with grades of B or better at a US university may submit an official transcript from the US university to fulfill this requirement. The following courses will not fulfill this requirement:
courses in English as a Second Language,
courses conducted in a language other than English,
courses that will be completed after the application is submitted, and
courses of a non-academic nature.
Applicants who have previously applied to Berkeley must also submit new test scores that meet the current minimum requirement from one of the standardized tests. Official TOEFL score reports must be sent directly from Educational Test Services (ETS). The institution code for Berkeley is 4833 for Graduate Organizations. Official IELTS score reports must be sent electronically from the testing center to University of California, Berkeley, Graduate Division, Sproul Hall, Rm 318 MC 5900, Berkeley, CA 94720. TOEFL and IELTS score reports are only valid for two years prior to beginning the graduate program at UC Berkeley. Note: score reports can not expire before the month of June.
Where to Apply
Visit the Berkeley Graduate Division application page.
Admission to the Program
Applicants for the Computational Biology PhD are expected to have a strong foundation in relevant stem fields, achieved by coursework in at least two computational biology subfields (including, but not limited to, advanced topics in biology, computer science, mathematics, statistics). Typical students admitted to the program have demonstrated outstanding potential as a research scientist and have clear academic aptitude in multiple disciplines, as well as excellent communication skills. This is assessed based on research experience, coursework & grades, essays, personal background, and letters of recommendation. Three letters of recommendation are required, but up to five can be submitted. The GRE is no longer accepted or used as part of the review (this includes both the general and subject exams). The program does *not* offer a Masters degree in Computational Biology.
Doctoral Degree Requirements
Normative Time Requirements
Normative Time to Advancement: Two years
Please refer to the PhD page on the CCB website for the most up-to-date requirements and information.
Students perform three laboratory rotations with the chief aim of identifying a research area and thesis laboratory. They also take courses to advance their knowledge in their area of expertise or fill in gaps in foundational knowledge. With guidance from the program, students are expected to complete six total graded courses by the end of the second year (not including the Doc Sem or Ethics course). Please see the program's website for more detailed course and curriculum requirements.
Students attend seminars, complete course requirements, and prepare a dissertation prospectus in preparation for their PhD oral qualifying examination. With the successful passing of the orals, students select their thesis committee and advance to candidacy for the PhD degree.
Normative Time in Candidacy: Three years
Years 3 to 5
Students undertake research for the PhD dissertation under a three or four-person committee in charge of their research and dissertation. Students conduct original laboratory research and then write the dissertation based on the results of this research. On completion of the research and approval of the dissertation by the committee, the students are awarded the doctorate.
Total Normative Time: 5-5.5 years
Time to Advancement
|CMPBIO 293||Doctoral Seminar in Computational Biology||2|
|CMPBIO 294A||Introduction to Research in Computational Biology (rotation units, Fall semester)||2-12|
|CMPBIO 294B||Introduction to Research in Computational Biology (rotation units, Spring semester)||2-12|
|STAT 200A||Introduction to Probability and Statistics at an Advanced Level||4|
|STAT 201A||Introduction to Probability at an Advanced Level (Stat 200A and 201A are the same content, but offered on different schedules. Students only take one of these.)||4|
|STAT 200B||Introduction to Probability and Statistics at an Advanced Level||4|
|STAT 201B||Introduction to Statistics at an Advanced Level (Stat 200B and 201B are the same content, but offered on different schedules. Students only take one of these.)||4|
|COMPSCI 61A||The Structure and Interpretation of Computer Programs (or demonstrate they have completed the equivalent in another course; a syllabus is required for approval. Note: Students will need to complete CS61B and CS70 or the equivalent in order to enroll in upper division CS courses. )||4|
|CS 61A is a minimum requirement and students who demonstrate they have completed the equivalent in another course (via syllabus), should take an advanced CS course of their choosing in it's place.|
|Three additional courses, drawn from existing campus offerings. These courses are intended to resolve deficiencies in training and ensure competency in the fundamental knowledge of each discipline. Students are expected to develop a course plan for remaining program requirements (such as biology coursework) and any additional electives, and to consult with the Head Graduate Advisor before the Spring semester of their first year for formal approval (signature required). The course plan will take into account the student's undergraduate training areas and goals for PhD research areas.||12|
|MCELLBI 293C||Responsible Conduct in Research||1|
|Complete an experimental training component in one of three ways: 1) complete a laboratory course at Berkeley (or equivalent) with a minimum grade of B, 2) complete a rotation in an experimental lab (w/ an experimental project), with a positive evaluation from the PI, 3) demonstrate proof of previous experience, such as: a biological sciences undergraduate major with at least two upper division laboratory-based courses, a semester or equivalent of supervised undergraduate experimental laboratory-based research at a university, or previous paid or volunteer/internship work in an industry-based experimental laboratory. Students will provide a brief summary of this experience to the Head Graduate Advisor for approval before taking the QE.|
Students conduct three 10-week laboratory rotations in the first year. The thesis lab, where dissertation research will take place, is chosen at the end of the third rotation in late April/early May.
The qualifying examination will evaluate a student’s depth of knowledge in his or her research area, breadth of knowledge in fundamentals of computational biology, ability to formulate a research plan, and critical thinking. The QE prospectus will include a description of the specific research problem that will serve as a framework for the QE committee members to probe the student’s foundational knowledge in the field and area of research. Proposals will be written in the manner of an NIH-style grant proposal. The prospectus must be completed and submitted to the chair no fewer than four weeks prior to the oral qualifying examination. Students are expected to pass the qualifying examination by the end of the fourth semester in the program.
Time in Candidacy
After passing the qualifying exam by the end of the second year, students have until the beginning of the fifth semester to select a thesis committee and submit the Advancement to Candidacy paperwork to the Graduate Division.
Primary dissertation research is conducted in years 3-5/5.5. Requirements for the dissertation are decided in consultation with the thesis advisor and thesis committee members. To this end, students are required to have yearly thesis committee meetings with the committee after advancing to candidacy.
Dissertation Presentation/Finishing Talk
There is no formal defense of the completed dissertation; however, students are expected to publicly present a talk about their dissertation research in their final year.
Required Professional Development
All computational biology students are expected to attend the annual retreat, and will regularly present research talks there. They are also encouraged to attend national and international conferences to present research.
Computational biology students are required to teach for one or two semesters (either one semester at 50% (20hrs/wk) or two semesters at 25% (10hrs/wk)) and may teach more. The requirement can be modified if the student has funding that does not allow teaching.
Designated Emphasis Requirements
Please refer to the DE page on the CCB website for the most up-to-date requirements and information.
The DE curriculum consists of one semester of the Doctoral Seminar in computational biology (CMPBIO 293, offered Fall & Spring) taken before the qualifying exam, plus three courses, one each from the three broad areas listed below, which may be independent from or an integral part of a student’s Associated Program. The three courses should be taken in different departments, only one of which may be the student’s home program. These requirements must be fulfilled with coursework taken with a grade of B or better while the student is enrolled as a graduate student at UC Berkeley. S/U graded courses do not count. See below for recommended coursework.
Students do not need to complete all of the course requirements prior to the application or the qualifying exam. The Doctoral Seminar does not need to be taken in order, ie either Fall or Spring are ok, but should be prior to or in the same semester as the Qualifying Exam. The DE will be rescinded if coursework has not been completed upon graduation (students should report their progress each year to the DE advisor, especially if they wish to change one of the courses they listed for the requirement).
- Computer Science and Engineering: A single course at the level of CS61A or higher will fulfill this requirement. Students can also take CS 88 (as an alternative to CS61A), though depending on their background, Data 8 may be necessary to complete this course. Students with a more advanced background are recommended to take a higher level CS course to fulfill the requirement.
- Biostatistics, Mathematics and Statistics: A single course at the level of Stat 131A, 133, 134, or 135 or higher will fulfill this requirement. Students with a more advanced background are recommended to take one of either Stat 201A & 201B or a higher level course to fulfill the requirement. Statistics or probability courses from other departments may be able to fulfill this requirement with prior approval of the program.
- Biology: please select an appropriate biology course from the list linked below (not up-to-date), or choose a course from current course listings.
- Computational Biology: CMPBIO C293, Doctoral Seminar, offered Fall & Spring.
More information, including a link to pre-approved courses, can be found on the CCB website.
Qualifying Examination and Dissertation
The qualifying examination and dissertation committees must include at least one (more is fine) Core faculty members from the Computational Biology Graduate Group. The faculty member(s) may serve any role on the committee from Chair to ASR. The Qualifying Examination must include examination of knowledge within the area of Computational and Genomic Biology. The Comp Bio Doctoral Seminar must be completed before the QE, as it will be important preparation for the exam.
Seminars & Retreat
Students must attend the annual Computational Biology Retreat (generally held in November) as well as regular CCB Seminar Series, or equivalent, as designated by the Curriculum Committee. Students are also strongly encouraged to attend or volunteer with program events during Orientation, Recruitment, Symposia, etc. Available travel funds will be dependent upon participation.
Faculty and Instructors
* Indicates this faculty member is the recipient of the Distinguished Teaching Award.
Murat Arcack, Professor. Control, Intelligent Systems, and Robotics (CIR), Biosystems & Computational Biology (BIO).
Adam Arkin, Professor. Systems modeling.
Doris Bachtrog, Professor. Evolution of sex and recombination, Y degeneration, dosage compensation, sexually antagonistic variation.
Lisa F. Barcellos, Associate Professor. Public health, genetic epidemiology, human genetics, autoimmune diseases, multiple schlerosis, lupus erythematosus, rheumatoid arthritis, epigenetics, genomics, computational biology.
Peter J. Bickel, Professor. Statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology.
Michael Boots, Professor. Ecology/epidemiology and evolution of infectious disease.
Steven Brenner, Professor. Molecular biology, computational biology, evolutionary biology, bioengineering, structural genomics, computational genomics, cellular activity, cellular functions, personal genomics.
Andres Cardenas, Assistant Professor. Children's environmental health, epigenetics, environmental epidemiology, molecular epidemiology, environmental health.
Thomas Courtade, Associate Professor. Information theory, data compression, communications, computer science.
Perry De Valpine, Associate Professor. Population ecology, mathematical modeling and statistics.
Sandrine Dudoit, Professor. Genomics, classification, statistical computing, biostatistics, cross-validation, density estimation, genetic mapping, high-throughput sequencing, loss-based estimation, microarray, model selection, multiple hypothesis testing, prediction, RNA-Seq.
Michael B. Eisen, Professor. Genomics, genome sequencing, bioinformatics, animal development.
Steven N. Evans, Professor. Genetics, random matrices, superprocesses and other measure-valued processes, probability on algebraic structures -particularly local fields, applications of stochastic processes to biodemography, mathematical finance, phylogenetics and historical linguistics.
Daniel Fletcher, Professor. Bioengineering, optical and force microscopy, microfabrication, biophysics, mechanical properties of cells.
Wayne Marcus Getz, Professor. Africa, disease ecology, wildlife conservation, resource management.
Oskar Hallatschek, Assistant Professor. Biophysics, random mutational events, genetic diversity, genome architecture, statistical physics, stochoastic reaction-diffusion systems, .
Teresa Head-Gordon, Professor. Computational chemistry, biophysics, bioengineering, biomolecules, materials, computational science.
Ian Holmes, Assistant Professor. Computational biology.
Haiyan Huang, Associate Professor. Applied statistics, functional genomics, translational bioinformatics, high dimensional and integrative genomic/genetic data analysis, network modeling, hierarchical multi-lable classification.
Alan Hubbard, Associate Professor. Causal inference, Statistical issues in infectious disease, Bioinformatics .
John P. Huelsenbeck, Professor. Computational biology, evolutionary biology, phylogenetics.
Nicholas Ingolia, Assistant Professor. Ribosome Profiling, translation, genomics.
Nilah Ioannidis, Assistant Professor. Computational biology, computational genomics, personalized medicine, genetic epidemiology, biostatistics, biophysics.
Michael I. Jordan, Professor. Computer science, artificial intelligence, bioinformatics, statistics, machine learning, electrical engineering, applied statistics, optimization.
Anthony Joseph, Professor. Internet security, mobile/distributed computing, and wireless communications, networking and telephony.
* Richard Karp, Professor. Computational molecular biology, genomics, DNA molecules, structure of genetic regulatory networks, combinatorial and statsitical methods.
Sung-Hou Kim, Professor. Computational genomics, Structural Biology, drug discovery, disease genomics.
Liana Lareau, Assistant Professor. Computational biology, molecular biology.
Joe Lewnard, Assistant Professor. Infectious diseases, antimicrobial resistance, public health surveillance, mathematical modeling, bayesian inference.
Lexin Li, Associate Professor. Neuroimaging data analysis, networks data analysis, personalized recommendation, statistical genetics, computational biology, dimension reduction, variable selection, high dimensional regressions, statistical machine learning, data mining, computational statistics.
Jennifer Listgarten, Professor. Artificial intelligence, biosystems and computational biology.
John Marshall, Assistant Professor. Mathematical models of infectious diseases, malaria epidemiology, dengue epidemiology, mosquito control, gene drive, population genetics.
Priya Moorjani, Assistant Professor. Human evolutionary genetics.
Michael Nachman, Professor. Population genetics, evolution, genomics, mammalian evolution.
Rasmus Nielsen, Professor. Statistical and computational aspects of evolutionary theory and genetics.
Elizabeth Purdom, Assistant Professor. Computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics.
Daniel S. Rokhsar, Professor. Biology, collective phenomena and ordering in condensed matter and biological systems, theoretical modeling, computational modeling, behavior of quantum fluids, cold atomic gases, high temperature superconductors, Fermi and Bose systems.
Karthik Shekhar, Assistant Professor. Cellular and systems biology, statistical inference, single-cell genomics.
Montgomery Slatkin, Professor. Evolutionary theory, genetic evolution, natural populations of plants and animals populations, human populations, natural selection structure genomes.
Friedrich Sommer, Adjunct Professor. Circuit dynamics of the brain, algebraic structure in the dynamics of recurrent neural networks.
Yun Song, Associate Professor. Computational biology, population genomics, applied probability and statistics.
Aaron Streets, Assistant Professor. Biological systems, microfluidics, microscopy, genomics.
Peter Sudmant, Assistant Professor. Genomics, genetics, computational biology, structural variation, RNA, diversity, aging, population genetics.
Denis Titov, Assistant Professor.
Mark J. Van Der Laan, Professor. Statistics, computational biology and genomics, censored data and survival analysis, medical research, inference in longitudinal studies.
Noah Whiteman, Associate Professor. Adaptation, evolutionary biology, genomics, genetics, toxicology, insect biology, plant biology, microbiology, CRISPR-Cas9 genome editing.
Nir Yosef, Assistant Professor. Computational biology.
Computational Biology Graduate Group
574 Stanley Hall
Graduate Program Coordinator
574 Stanley Hall, MC #3220
Executive Director, CCB
CCB DE email