About the Program

Many issues in the health, medical, and biological sciences are addressed by collecting and exploring relevant data. The development and application of techniques to better understand such data is the fundamental concern of the Group in Biostatistics. The program offers training in theory of statistics and biostatistics, the computer implementation of analytic methods, and opportunities to use this knowledge in areas of biological/medical research. The curriculum is taught principally by members of the Department of Statistics (College of Letters and Science) and the Division of Biostatistics (School of Public Health) and provides a wide range of ideas and approaches to the analysis of data.

The Group in Biostatistics offers two graduate programs: MA and PhD. These programs are appropriate for students who have either a strong mathematical and statistical background with a focus in the biomedical sciences, or degrees in the biological sciences with a focus in mathematics and statistics. (The MA degree can be obtained under Plan I or Plan II. The PhD dissertation is administered according to Plan B.)

Master of Arts (MA)

Students pursuing an MA degree in Biostatistics will be expected, upon completion of the program, to be well-versed in the following areas:

• Fundamentals of estimation and hypothesis testing, including regression and analysis of variance
• Multivariate data analysis—categorical and continuous
• Survival data analysis
• Longitudinal data analysis
• Causal inference
• Computational statistics
• Statistical computing, with emphasis on the R and SAS languages
• Statistical methods in clinical trials, epidemiology, and genomics

The Group in Biostatistics has produced 170 MA graduates. Some students pursuing the MA intend to go on for a PhD, but many others take research, statistical and analytical positions in federal agencies, state and local health departments, health care delivery organizations and private industry. After a period of employment, some former MA students elect to resume study toward a PhD. Although readmission is not automatic, the application procedure to reenter the program for the PhD is a simple one. Reentry applicants are considered for readmission along with the pool of new applicants to the program.

Doctor of Philosophy (PhD)

Courses cover traditional topics as well as recent advances in biostatistics and in statistics. Those completing the PhD will have acquired a deep knowledge and understanding of these subject areas. Since graduates with doctorates often assume academic careers in research and teaching, a high degree of mastery in research design, theory, methodology and execution is expected as well as the ability to communicate and present research findings and area of expertise in a clear, understandable manner.

Since the inception of the program in 1955, the Group in Biostatistics has produced more than 100 doctoral graduates. A number of these people have gone on to teaching careers at colleges and universities, both here in the US and internationally. Others have pursued careers as Biostatisticians in the pharmaceutical/biotech industries, health care delivery organizations, medical schools and schools of public health across the US and abroad. The demand for biostatisticians with advanced training is high, particularly for those seeking teaching and research careers.

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Admissions

Admission to the University

Uniform minimum requirements for admission

The following minimum requirements apply to all programs and will be verified by the Graduate Division:

1. A bachelor’s degree or recognized equivalent from an accredited institution;
2. A minimum 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 570 on the paper-and-pencil test, 230 on the computer-based test, 90 on the iBT test, or an IELTS Band score of at least 7 (note that individual programs may set higher levels for any of these); and
4. Enough undergraduate training to do graduate work in the given field.

Applicants who already hold a graduate degree

The Graduate Council views academic degrees as evidence of broad research training, not as vocational training certificates; therefore, applicants who already have academic graduate degrees should be able to take up new subject matter on a serious level without undertaking a graduate program, unless the fields are completely dissimilar.

Programs may consider students for an additional academic master’s or professional master’s degree 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 only apply to one single degree program or one concurrent degree program per admission cycle.

Any applicant who was previously registered at Berkeley as a graduate student, no matter how briefly, must apply for readmission, not admission, even if the new application is to a different program.

Required documents for admissions applications

1. Transcripts:  Upload unofficial transcripts with the application for the departmental initial review. Official transcripts of all college-level work will be required if admitted. Official transcripts must be in sealed envelopes as issued by the school(s) you have attended. Request a current transcript from every post-secondary school that you have attended, including community colleges, summer sessions, and extension programs.
   If you have attended Berkeley, upload unofficial transcript with the application for the departmental initial review. Official transcript with evidence of degree conferral will not be required if admitted.
2. Letters of recommendation: Applicants can 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 in which the official language is not English are required to submit official evidence of English language proficiency. This requirement 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, and most European countries. 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 U.S. university may submit an official transcript from the U.S. university to fulfill this requirement. The following courses will not fulfill this requirement: 1) courses in English as a Second Language, 2) courses conducted in a language other than English, 3) courses that will be completed after the application is submitted, and 4) 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.

Doctoral Degree Requirements

Normative Time Requirements

Since there are no unit or course requirements for the PhD, a program of courses appropriate to a student's background and interests may be developed.

All students in the Biostatistics PhD program hold a master's degree in Biostatistics or a related field and those applying for PhD study who do not already hold a masters degree are considered for admission to the Biostatistics MA. This practice does not prolong the time to the doctorate since the first two years of both the MA and PhD programs for students coming from the baccalaureate are identical.

Normative Time to Advancement: 2-3 Years

Normative Time in Candidacy: 1-2 Years

Total Normative Time: 3-5 Years

Time to Advancement

Curriculum

The Qualifying Examination

The oral qualifying examination is scheduled for three hours. The primary purpose of the exam is to test both a candidate's general competence in the field of Biostatistics and the ability to apply biostatistical methods to a broad research area. The exam is designed to measure the candidate's breadth of knowledge as well as provide a determination of the candidate's readiness to enter the research phase of study.

To assure the examining committee that the candidate has a firm grasp of both basic areas and a familiarity with current problems in the field, the exam is conducted as follows:

1. The candidate is expected to begin with a 30 minute presentation of a proposed dissertation topic that includes a sound research strategy that the candidate can defend.
2. Following this presentation, the candidate will be asked to demonstrate an ability to synthesize the methods and techniques learned through course work and to apply this knowledge to areas and problems suggested by the committee members. To achieve this goal, committee members are likely to ask questions that delve into subjects that go beyond the chosen area of dissertation research.

Students are encouraged to take the qualifying exam after they have identified a dissertation advisor and a research topic. In preparation for the exam, the candidate should meet with the Chair of the qualifying examination committee to discuss the details of the structure of the exam and any other pertinent issues.

Time in Candidacy

Dissertation

After completion of course work and the oral comprehensive examination, a doctoral student advances to candidacy for the PhD. Before this is possible, a student must have identified:

1. A dissertation topic
2. A dissertation adviser
3. A committee

Advisers and committee members are particularly interested in working with candidates who have demonstrated an ability to perform original research, and doctoral students are encouraged to explore dissertation research topics early in the program. Topics for research are selected from biostatistics and statistics, public health, biology, computing and other areas (see dissertation topics of some recent graduates).

Master's Degree Requirements

Unit Requirements

Candidates for this degree are expected to complete 48 units, in four semesters.

Curriculum

MA Comprehensive Examination

The examination for the MA degree is designed to test a candidate's breadth and depth of understanding and knowledge and the ability to articulate and explain the basic concepts gained from the curriculum. The examination committee consists of two faculty members representing both Biostatistics and Statistics. Candidates are asked to select three topics and write a description of each. Topics are presented during the exam period of 90 minutes. Examiners are free to ask for clarification or elaboration through requests for more background, detail or examples.

MA Thesis

Note that the decision to submit a thesis rather than take the oral comprehensive examination must be made early in the final semester of the program.

Faculty

Professors

Peter L Bartlett, Professor. Statistics, machine learning, statistical learning theory, adaptive control.
Research Profile

David R. Brillinger, PhD, Professor. Risk analysis, statistical methods, data analysis, animal and fish motion trajectories, statistical applications in engineering and science, sports statistics.
Research Profile

Steven N. Evans, Professor. Genetics, random matrices, superprocesses & other measure-valued processes, probability on algebraic structures -particularly local fields, applications of stochastic processes to biodemography, mathematical finance, phylogenetics & historical linguistics.
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Michael J. Klass, Professor. Statistics, mathematics, probability theory, combinatorics independent random variables, iterated logarithm, tail probabilities, functions of sums.
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Elchanan Mossel, Professor. Applied probability, statistics, mathematics, finite markov chains, markov random fields, phlylogeny.
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James W Pitman, Professor. Fragmentation, statistics, mathematics, Brownian motion, distribution theory, path transformations, stochastic processes, local time, excursions, random trees, random partitions, processes of coalescence.
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Philip B. Stark, Professor. Astrophysics, law, statistics, litigation, causal inference, inverse problems, geophysics, elections, uncertainty quantification, educational technology.
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Bin Yu, Professor. Neuroscience, remote sensing, networks, statistical machine learning, high-dimensional inference, massive data problems, document summarization.
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Associate Professors

Noureddine El Karoui, Associate Professor. Applied statistics, theory and applications of random matrices, large dimensional covariance estimation and properties of covariance matrices, connections with mathematical finance.
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Haiyan Huang, Associate Professor. Applied statistics, functional genomics, translational bioinformatics, high dimensional and integrative genomic/genetic data analysis, network modeling, hierarchical multi-lable classification.
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Assistant Professors

Adityanand Guntuboyina, Assistant Professor.

Elizabeth Purdom, PhD, Assistant Professor. Computational biology, bioinformatics, statistics, data analysis.
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Benjamin Recht, Assistant Professor.

Allan Sly, Assistant Professor.

Adjunct Faculty

Lisa Robin Goldberg, PhD, Adjunct Faculty.

Jon Mcauliffe, Adjunct Faculty. Bioinformatics, machine learning, nonparametrics, convex optimization, statistical computing, prediction, supervised learning.
Research Profile

Lecturers

Ani Adhikari, Lecturer.

Fletcher H Ibser, Lecturer.