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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 this data is the fundamental concern of our program. We offer training in statistics and biostatistics theory, computer implementation of analytic methods, and opportunities to use this knowledge in areas of biological and medical research.

Berkeley Public Health and UC Berkeley’s Department of Statistics, together with other UC Berkeley departments, offer a broad set of opportunities to satisfy the needs of individual students. In addition, the involvement of faculty from UCSF’s  Department of Biostatistics and Epidemiology enriches our instructional and research activities.


Our master’s program is a two-year program consisting of 48 units with courses selected from biostatistics and statistics, public health, and biology.

The oral comprehensive examination is designed to test a candidate’s breadth of understanding and knowledge, as well as the ability to articulate and explain the basic concepts gained from the curriculum. Alternatively, a thesis may be submitted to fulfill requirements. However, the decision to submit a thesis rather than take the oral examination must be made early in the final semester of the program.

Students should take the following courses:

  • STAT 201A: Introduction to Probability at an Advanced Level
  • STAT 201B: Introduction to Statistics at an Advanced Level
  • PH C240A: Introduction to Modern Biostatistical Theory and Practice

In addition to Statistics 201A and 201B and PH C240A, students are expected to take PH252D (Introduction to Causal Inference) and at least two other courses from the following list:

  • PH C240B: Biostatistical Methods: Survival Analysis and Causality

  • PH 240C: Computational Statistics

  • PH 252E: Advanced Topics in Causal Inference

  • PH 244: Big Data: A Public Health Perspective

  • CS 294.150: Machine Learning and Statistics Meet Biology
  • PH C242C: Longitudinal Data Analysis

  • PH 290.X: Targeted Learning in Biomedical Big Data


Previous coursework in calculus and linear algebra is required.

Common undergraduate majors for admitted applicants include statistics, biomedical and biological sciences, mathematics, and computer science.


Some students pursuing the MA degree intend to continue directly into a PhD program, while others take research positions in tech companies, federal agencies, state and local health departments, health care delivery organizations, and private industry. MA students interested in continuing into the UC Berkeley Biostatistics doctoral program immediately following their MA degree should apply to the new degree program through the Online Application for Admission during their second year of study during the normal admissions cycle.

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 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

12% Admissions Ratio (17/141)
3.71 Average GPA of admitted applicants
85% Average Verbal GRE scores of admitted applicants
86% Average Quantitative GRE scores of admitted applicants

Biostatistics Faculty

Clinical Faculty


Faculty Associated in Biostatistics Graduate Group

  • 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

Cam Adams

Advisor: Lisa Barcellos

Casey Breen


Advisor: Maya Petersen

I am interested in the application of social network analysis and computational methods to questions in population health.

Jessica Briggs


Advisor: Alan Hubbard

I am an infectious diseases physician at UCSF, interested in applying novel statistical methods to the genomic epidemiology of malaria and infectious disease serosurveillance.

Pablo Freyria


Advisor: Maya Petersen

I studied applied mathematics in Mexico and became mainly interested in precision medicine and patient empowerment technologies while working in a healthcare consultancy

Sophia Fuller


Advisor: Mi-Suk Kang Dufour

Research interests include applications of causal inference and targeted learning in women’s health, cancer, and HIV.

Feng Ji

Advisor: Sophia Rabe-Hesketh

Yunwen Ji

Advisor: Maya Peterson

Still exploring 🙂

Haodong Li

Advisors: Alan Hubbard and Mark van der Laan

A Super-enthusiastic Learner focusing on the applications of targeted learning and casual inference. CV-TMLE is my default estimator for now, what is yours?

Yang Li


Advisor: Alan Hubbard

Yi Li

Advisor: Jingshen Wang

I am an incoming Phd Student in Biostatistics. My research interests are causal inference, high dimensional statistics, mendelian randomization and TMLE.

Lauren Liao


Advisors: Alan Hubbard and Yeyi Zhu

My research interests lie in causal inference, experimental design and analysis. I am interested in cognitive neuroscience, maternal health, and a wide range of public health issues.

Aidan McLoughlin


Advisors: Haiyan Huang, Lexin Li

In the computational biology domain, I’m interested and working on gene coexpression analysis. Methodologically, I’m excited by reinforcement learning and similar methods as they may be applied to biomedical or public health questions such as brain stimulation therapy and infectious diseases.

Jia-ye Pan

Advisor: Jingshen Wang

Christopher Rowe

Junming (Seraphina) Shi

Advisors: Lexin Li, Elizabeth Purdom

Lei Shi


I’m interested in high dimensional stat, causal inference and modern ML/DL theory together with their application in public health.

Namita Trikannad

Hao Wang

Advisors: Elizabeth Purdom

Hello! My research interests are in the fields of statistical genomics and computational biology. I’m currently working on the Single-cell RNA sequencing (scRNA-seq) projects. I enjoy cooking, playing the harp and imagining I have a cat 🙂

Yunzhe Zhou


I am interested in deep learning, statistical learning, graphical model and network anaysis.