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.

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Admissions

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:

1. A bachelor’s degree or recognized equivalent from an accredited institution;
2. A grade point average of B or better (3.0);
3. If the applicant comes 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
4. 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:

1. 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.
2. 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

1. Transcripts: Applicants may upload unofficial transcripts with your application for the departmental initial review. If the applicant is admitted, then official transcripts of all college-level work will be required. Official transcripts must be in sealed envelopes as issued by the school(s) attended. If you have attended Berkeley, upload your unofficial transcript with your application for the departmental initial review. If you are admitted, an official transcript with evidence of degree conferral will not be required.
2. 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, not the Graduate Division.
3. Evidence of English language proficiency: All applicants from countries or political entities in which the official language is not English are required to submit official evidence of English language proficiency. This applies to applicants 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.

If applicants have previously been denied admission to Berkeley on the basis of their English language proficiency, they must submit new test scores that meet the current minimum 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. Official IELTS score reports must be mailed directly to our office from the British Council. TOEFL and IELTS score reports are only valid for two years.

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 the basic sciences (e.g., biology, mathematics, chemistry), in addition to training in computing/informatics/probability, significant programming experience, and substantial research experience. 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, grades, standardized exams, course selection, essays, personal background, and letters of recommendation. Three letters of recommendation are required, as is the general GRE. A GRE subject examination in an appropriate area is recommended, but not required. The program does not accept applicants interested in a Master of Science in Computational Biology.

Doctoral Degree Requirements

Normative Time Requirements

Normative Time to Advancement: Two years

Year 1
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 first or second year (not including the Doc Sem or Ethics course).

Year 2
Students attend seminars, prepare a dissertation prospectus, complete course requirements, and prepare 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 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

Curriculum

Lab Rotations

Students conduct three 9-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.

Qualifying Examination

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

Advancement

After passing the qualifying exam by the end of the second year, students have until the fifth semester to select a thesis committee and submit the Advancement to Candidacy paperwork to the Graduate Division.

Dissertation

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

Presentations

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.

Teaching

Computational biology students are required to teach for two semesters and may teach more. The requirement can be modified if the student has funding that does not allow teaching.

Designated Emphasis Requirements

Curriculum/Coursework

The DE curriculum consists of a Doctoral Seminar in computational biology composed of separate Fall and Spring components, plus three courses, one each from the three broad areas listed below, which may be independent of 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. The Doctoral Seminar may be taken in different academic years (and does not need to be taken in order, ie either Spring/Fall or Fall/Spring are ok), with at least one of the two semesters being prior to 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).

1. Computer Science and Engineering: A single course at the level of CS61A or higher will fulfill this requirement. Students with a more advanced background are recommended to take a higher-level CS course to fulfill the requirement.
2. Biostatistics, Mathematics and Statistics: A single course at the level of Stat 133, 134, or 135 or higher will fulfill this requirement. Students with a more advanced background are recommended to take one of either Stat 200A & 200B 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.
3. Biology: Please select an appropriate biology course from the list on the website or petition program for an alternative course.
4. Computational Biology: CMPBIO 293, Doctoral Seminar, Fall & Spring.

More information can be found on the CCB website.

Qualifying Examination and Dissertation

The qualifying examination and dissertation committees must include at least one, but preferably two, Core faculty members from the Computational Biology Graduate Group. The faculty member(s) may either represent the home department or serve as an outside member (Academic Senate Representative). The Qualifying Examination must include an 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 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.

Courses

Computational Biology

Faculty and Instructors

+ Indicates this faculty member is the recipient of the Distinguished Teaching Award.

Faculty

Adam Arkin, Professor. Systems modeling.
Research Profile

Doris Bachtrog, Associate Professor. Evolution of sex and recombination, Y degeneration, dosage compensation, sexually antagonistic variation.
Research Profile

Lisa F. Barcellos, Associate Professor. Public health, genetic epidemiology, human genetics, autoimmune diseases, multiple schlerosis, lupus erythematosus, rheumatoid arthritis, epigenetics, genomics, computational biology.
Research Profile

Carl Boettiger, Assistant Professor. Theoretical ecology, ecoinformatics, modeling, data science, resilience, early warning signals, decision theory.
Research Profile

Steven Brenner, Professor. Molecular biology, computational biology, evolutionary biology, bioengineering, structural genomics, computational genomics, cellular activity, cellular functions, personal genomics.
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Perry De Valpine, Associate Professor. Population ecology, mathematical modeling and statistics.
Research Profile

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.
Research Profile

Michael B. Eisen, Professor. Genomics, genome sequencing, bioinformatics, animal development.
Research Profile

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.
Research Profile

+ Phillip Lewis Geissler, Professor. Statistical mechanics, theoretical chemistry, microscopic behavior of complex biological and material systems, biomolecular structure and dynamics, nonlinear vibrational spectroscopy.
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Wayne Marcus Getz, Professor. Africa, disease ecology, wildlife conservation, resource management.
Research Profile

Oskar Hallatschek, Assistant Professor.

Teresa Head-Gordon, Professor. Computational chemistry, biophysics, bioengineering, biomolecules, materials, computational science.
Research Profile

Ian Holmes, Assistant Professor. Computational biology.
Research Profile

Haiyan Huang, Associate Professor. Applied statistics, functional genomics, translational bioinformatics, high dimensional and integrative genomic/genetic data analysis, network modeling, hierarchical multi-lable classification.
Research Profile

Alan Hubbard, Associate Professor. Causal inference, Statistical issues in infectious disease, Bioinformatics .

John P. Huelsenbeck, Professor. Computational biology, evolutionary biology, phylogenetics.
Research Profile

Nicholas Ingolia, Assistant Professor. Ribosome Profiling, translation, genomics.
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Nicholas P. Jewell, Professor. AIDS, statistics, epidemiology, infectious diseases, Ebola Virus Disease, SARS, H1N1 influenza, adverse cardiovascular effects of pharmaceuticals, counting civilian casualties during conflicts.
Research Profile

Michael I. Jordan, Professor. Computer science, artificial intelligence, bioinformatics, statistics, machine learning, electrical engineering, applied statistics, optimization.
Research Profile

Sona Kang, Assistant Professor. Epigenetics, chromatin remodeling, gene expression, diabetes, metabolic diseases .
Research Profile

+ Richard Karp, Professor. Computational molecular biology, genomics, DNA molecules, structure of genetic regulatory networks, combinatorial and statsitical methods.
Research Profile

Sung-Hou Kim, Professor. Computational genomics, Structural Biology, drug discovery, disease genomics.
Research Profile

Lexin Li, Associate Professor.

Susan Marqusee, Professor. Amino acids, determinants of protein structure and folding, biophysical, structural and computational techniques, translocation, protein synthesis.
Research Profile

John Marshall, Assistant Professor. Utilize mathematical models to predict the utility of genetic control strategies for a variety of mosquito-borne diseases.

Elchanan Mossel, Professor. Applied probability, statistics, mathematics, finite markov chains, markov random fields, phlylogeny.
Research Profile

Michael Nachman, Professor. Population genetics, evolution, genomics, mammalian evolution.
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Rasmus Nielsen, Professor. Statistical and computational aspects of evolutionary theory and genetics.
Research Profile

Lior Pachter, Professor. Mathematics, applications of statistics, combinatorics to problems in biology.
Research Profile

Elizabeth Purdom, Assistant Professor. Computational biology, bioinformatics, statistics, data analysis, sequencing, cancer genomics.
Research Profile

+ Jasper D. Rine, Professor. Biology, cell biology, DNA replication, gene regulation, saccharomyces cerevisiae, genetic analysis, genome, cholesterol biosynthetic pathway, modification of proteins, prenylated proteins.
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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.
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Kimmen Sjolander, Professor. Computational biology, algorithms, phylogenetic tree reconstruction, protein structure prediction, multiple sequence alignment, evolution, bioinformatics, hidden Markov models, metagenomics, statistical modeling, phylogenomics, emerging and neglected diseases, machine-learning, genome annotation, metagenome annotation, systems biology, functional site prediction, ortholog identification.
Research Profile

Yun Song, Associate Professor. Computational biology, population genomics, applied probability and statistics.
Research Profile

John Taylor, Professor. Evolution, fungi, phylogenomics, mycology, population genomics.
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Mark J. Van Der Laan, Professor. Statistics, computational biology and genomics, censored data and survival analysis, medical research, inference in longitudinal studies.
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Nir Yosef, Assistant Professor. Computational biology.
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Emeritus Faculty

Peter J. Bickel, Professor Emeritus. Statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology.
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David A. Patterson, Professor Emeritus. Computer Architecture & Engineering (ARC), Computer Architecture and Systems: Parallel Computing performance, correctness, productivity,Biosystems & Computational Biology (BIO), Cancer tumor genomics, Operating Systems & Networking (OSNT).
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Montgomery Slatkin, Professor Emeritus. Evolutionary theory, genetic evolution, natural populations of plants and animals populations, human populations, natural selection structure genomes.
Research Profile

Terence P. Speed, Professor Emeritus. Genomics, statistics, genetics and molecular biology, protein sequences.
Research Profile