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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.


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.


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.


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.

Admissions Statistics

12% Admissions Ratio (8/67)
3.72 Average GPA of admitted applicants
95% Average Verbal GRE scores of admitted applicants
90% Average Quantitative GRE scores of admitted applicants


Clinical Faculty


Faculty Associated in Biostatistics Graduate Group

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

Student Directory

Kevin Benac

Advisor: Peng Ding


Aurelien Bibaut

Philippe Boileau

Advisor: Sandrine Dudoit



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

Lauren Eyler Dang

Advisors: Alan Hubbard, Mark van der Laan


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

Advisor: Bin Yu

Boying Gong

Advisor: Elizabeth Purdom


Nima Hejazi

Advisor: Mark van der Laan


I’m generally interested in problems at the interface of causal inference and machine learning, using techniques drawing on loss-based estimation and targeted maximum likelihood estimation (TMLE). I’ve applied my work in applied problems in settings ranging from vaccine efficacy trials to computational biology. I’m also interested in writing software to empower statisticians and data scientists.

Zhiyue Hu

Partow Imani

Chris Kennedy

William Krinsman


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

Advisor: Lexin Li

Ivana Malenica

Advisor: Mark van der Laan


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

Lina Montoya

Sara Moore

Maxwell Murphy

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


Rachael Phillips

Advisor: Mark van der Laan



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.

Hector Roux de Bezieux

George Shan

Nicholas Sim

Yutong Wang

Advisor: Yun S. Song


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

Advisors: Jingshen Wang, Alan Hubbard


My research interests lie in causal inference and adaptive design.

Yulun (Rayn) Wu

Advisor: James Bentley “Ben” Brown


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.

Yuting Ye

Yue You

Advisors: Mark van der Laan, Alan Hubbard


Yue is a PhD student jointly advised by Prof. Alan Hubbard and Prof. Mark van der Laan. Her research interests are targeted statistical learning, causal inference and statistical computing. She received her BS in Statistics and BA in Chinese Literature from Fudan University, China in 2016.

Mingrui Zhang

Advisor: Lexin Li

Wenxin Zhang

Advisor: Mark van der Laan

Yun Zhou