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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 a fundamental concern of our program.

This program offers training in the theory of statistics and biostatistics, computer implementation of analytic methods and opportunities to use this knowledge in areas of biological/medical research. The resources of Berkeley Public Health and the UC Berkeley Department of Statistics, together with those of other university departments, offer a broad set of opportunities to satisfy the needs of individual students. Furthermore, the involvement of UCSF faculty from the Department of Biostatistics and Epidemiology also enriches instructional and research activities.

Curriculum

A PhD degree in Biostatistics requires a program of courses selected from biostatistics, statistics, and at least one other subject area (such as environmental health, epidemiology, or genomics), an oral qualifying examination, and a dissertation. Courses cover traditional topics as well as recent advances in biostatistics and statistics. Those completing the PhD will have acquired a deep knowledge and understanding of the MA subject areas. Since graduates with doctorates often assume academic research and teaching careers, a high degree of mastery in research design, theory, methodology, and execution is expected, as well as the ability to communicate and present concepts in a clear, understandable manner.

The PhD degree program requires 4-6 semesters of coursework, the completion of  the qualifying examination and dissertation (in total, a minimum of four semesters of registration is required). Since there are no formal course requirements for the PhD, a program of courses appropriate to a student’s background and interests may be developed with a graduate adviser.

Qualifications

All students accepted into the PhD program must hold a master’s degree in biostatistics or a related field. Applicants to the PhD program who do not already hold an MA, if admitted, are admitted initially to the MA-PhD degree program, and then apply to continue in the PhD program. This practice does not prolong the time to conferral of the doctorate, since the first two years of both the MA and PhD programs for students coming from the baccalaureate are identical. Therefore, most students entering without a MA degree should be able to finish their PhD studies within a 5-year range. Students entering with a relevant master’s degree in biostatistics or statistics must have a faculty advisor (affiliated with the Division of Biostatistics) committing funding support.

Employment

Many doctoral graduates accept faculty positions in schools of public health, medicine, and statistics and/or math departments at colleges and universities, both in the United States and abroad. Some graduates take research positions, including with pharmaceutical companies, hospital research units, non-profits, and within the tech sector.

Funding and fee remission

Prospective students who are US citizens or permanent residents can find more information about applying for an application fee waiver for the Berkeley Graduate Application. Fees will be waived based on financial need or participation in selected programs described on the linked website. International applicants (non-US citizens or Permanent Residents) are not eligible for application fee waivers.

100% of biostatistics MA and PhD students who seek financial support through GSI and GSR positions have been successful in previous years. There are often more available positions than students, so our students have historically had most or all of their direct program costs funded. All PhD students are fully funded (including tuition and fees and a stipend or salary) with the exception of Non-Resident Supplemental Tuition (NRST) for the second year, if applicable. NRST is typically waived after the first year of study for PhD students when they advance to candidacy. Information on applying to GSI positions for biostatistics students can be found in the student handbook.

Fees not covered by funding provided by GSI/GSR appointments between 25% and 50%:

  • GSI and GSR appointments provide partial fee remission which covers the Graduate Tuition Fee, Student Services Fee, Health Insurance Fee, and $150 toward the Berkeley Campus Fee each semester you serve. For the 2020 – 2021 academic year, the total amount covered was $9276 which means students paid $687.75 out of pocket (not including NRST, see below).
  • Students who are not California residents are required to pay a Non-Resident Supplemental Tuition (NRST). NRST was $7551 per semester for the 2020 – 2021 academic year. This fee is covered for all PhD students in their first year and waived in future semesters once students advance to candidacy. This fee may also be covered for MA or MA/PhD students in their first year if indicated in the funding letter sent shortly after admission. Payment of NRST by the program, beyond any commitments in the funding letter, is not guaranteed and is based on funding availability.  US citizens and Permanent Residents can apply for California residency after their first year and, once approved for residency, NRST will no longer apply. For more information, including the requirements for applying for residency, please see the Office of the Registrar website.

Tuition and fees change each academic year. To view the current tuition and fees, see the fee schedule on the Office of the Registrar website (in the Graduate: Academic section).

Please contact biostat@berkeley.edu if you have any questions about funding opportunities for the biostatistics programs.

Diversity, Equity and Inclusion

The Division of Biostatistics is committed to challenging systemic inequities in the areas of health, medical, and biological sciences, and to advancing the goals of diversity, equity, and inclusivity in Biostatistics and related fields.

Diversity, Equity and Inclusion in Biostatistics

Admissions Statistics

8.6% Admissions Ratio (8/93)
3.9 Average GPA of admitted applicants
81% Average Verbal GRE percentile
92% Average Quantitative GRE percentile
25 Average age upon admission
4 Average years of relevant professional/research experience

Faculty

Clinical Faculty

Emeritus

Faculty Associated in Biostatistics Graduate Group

  • Peter Bickel PhD
    Statistics
  • David R. Brillinger PhD
    Statistics
  • Perry de Valpine PhD
    Environmental Science, Policy, and Management
  • Haiyan Huang PhD
    Statistics
  • Michael J. Klass PhD
    Statistics
  • Priya Moorjani PhD
    Molecular & Cell Biology
  • Rasmus Nielsen PhD
    Integrative Biology and Statistics
  • Elizabeth Purdom PhD
    Statistics
  • Sophia Rabe-Hesketh PhD
    Education
  • John Rice PhD
    Statistics
  • Yun S. Song PhD
    Statistics; Electrical Engineering and Computer Sciences
  • Bin Yu PhD
    Statistics

Student Directory

Kevin Benac

benac@berkeley.edu

Advisor: Peng Ding

LinkedIn

Philippe Boileau

philippe_boileau@berkeley.edu

Advisor: Sandrine Dudoit

Website

Github

I’m a PhD student from Montreal under the supervision of Professor Sandrine Dudoit. My research revolves around the development of statistical learning methods and their application to high-dimensional datasets. I also collaborate with epidemiologists and biologists, guiding experimental design and analyzing data generated via next-generation sequencing experiments.

David Chen

Mary Combs

Maryac330@gmail.com

Google Scholar

Advisor: Mark van der Laan

Mary Combs, MA, is a PhD student in Biostatistics at the University of California, Berkeley under Mark van der Laan. Mary’s research interests include developing targeted estimators for parameters of interest in semi- and non-parametric models within the framework of causal inference. Her doctorate thesis proposes, with minimal assumptions, a novel approach to risk estimation in complex longitudinal studies in the presence of competing risks.

Lauren Eyler Dang

lauren.eyler@berkeley.edu

Advisors: Alan Hubbard, Mark van der Laan

Github

Lauren Eyler Dang, MD, MPH is currently a PhD student in Biostatistics at the University of California, Berkeley. Her research focuses on targeted maximum likelihood-based methods and constrained optimization. Her applied work addresses measurement of health disparities, cancer risk prediction for populations with limited data, and healthcare systems development in low-resource settings.

James Duncan

jpduncan@berkeley.edu

Advisor: Bin Yu

Zhiyue Hu

Partow Imani

William Krinsman

krinsman@berkeley.edu

Github

Has worked for Computer Sciences Area at LBNL (CRD and NERSC) helping to develop open source projects, currently works for Environmental Genomics and Systems Biology (EGSB) division of the Biosciences Area. Lived in Germany for two (non-consecutive) years, speaks some languages besides English. Currently interested in statistical problems arising from microbial ecology and applications of statistics thereto.

Xiang Lyu

xianglyu@berkeley.edu

Advisor: Lexin Li

Ivana Malenica

imalenica@berkeley.edu

Advisor: Mark van der Laan

LinkedIn

My research interest span non/semi-parametric theory, causal inference and machine learning. Most of my current work involves complex dependent settings (dependence through time and network), reinforcement learning, and adaptive sequential designs. I am also interested in model selection criteria, optimal individualized treatment, online learning and software development.

Jarrod Millman

Sara Moore

Maxwell Murphy

murphy2122@berkeley.edu

Advisors: Rasmus Nielsen / Bryan Greenhouse (UCSF), Mark van der Laan

Github

Rachael Phillips

rachaelvphillips@berkeley.edu

Advisor: Mark van der Laan

Twitter

Github

Rachael has an MA in Biostatistics, BS in Biology, and BA in Mathematics. A student of targeted learning and causal inference; her research integrates personalized medicine, human-computer interaction, experimental design, and regulatory policy.

George Shan

Nicholas Sim

Yutong Wang

ytwang@berkeley.edu

Advisor: Yun S. Song

Website

I am broadly interested in statistical machine learning, probabilistic modeling, and causal inference, with applications in high-dimensional genomics and metagenomics data.

Waverly (Linqing) Wei

linqing_wei@berkeley.edu

Advisors: Jingshen Wang, Alan Hubbard

Website

My research interests lie in causal inference and adaptive design.

Yulun (Rayn) Wu

yulun_wu@berkeley.edu

Advisor: James Bentley “Ben” Brown

Github

My research interests include bayesian network, reinforcement learning, causal inference, semiparametric estimation and statistical computing. Outside of work, I love basketball, billiards, skating, biking, gaming and various kinds of water sports.

Mingrui Zhang

mingrui_zhang@berkeley.edu

Advisor: Lexin Li

Wenxin Zhang

wenxin_zhang@berkeley.edu

Advisor: Mark van der Laan

Yun Zhou