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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.

Curriculum

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

Qualifications

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.

Employment

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.

Some MA and MA/PhD admitted students are made a funding offer as a part of their admission package. These offers depend on funding availability and the applicant pool for that year.

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

13.1% Admissions Ratio (19/145)
3.8 Average GPA of admitted applicants
88% Average Verbal GRE percentile
90% Average Quantitative GRE percentile
24 Average age upon admission
5 Average years of professional/research experience

Biostatistics 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

Alumni Directory

Kelly Street, MA Spring 2014
Accepted into the PhD program in Biostatistics, UC Berkeley

Anthony Oleg (“Toki”) Sherbakov, MA Spring 2015
Head of Business Development, Peregrine Technologies

Mary Ava Combs, MA Spring 2016
Accepted into the PhD program in Biostatistics, UC Berkeley

Cheng Ju, MA Spring 2016
Accepted into the PhD program in Biostatistics, UC Berkeley

Jonathan L. Larson, MA Spring 2016
Accepted into the PhD program in Biostatistics, Harvard

Jonathan Levy, MA Spring 2016
Accepted into the PhD program in Biostatistics, UC Berkeley

Minh Chau Thanh Nguyen, MA Spring 2016
Accepted into the PhD program in Biomedical Informatics, Stanford

Courtney Schiffman, MA Spring 2016
Accepted into the PhD program in Biostatistics, UC Berkeley

Denise Shieh, MA Spring 2016
Accepted into the PhD program in Biostatistics, Columbia University

Andre K. Waschka, MA Spring 2016
Assistant Professor of Statistics at Mercer University

Weixin Cai, MA Spring 2017
Deep Learning Researcher, Microsoft

Suzanne Dufault, MA Spring 2017
Accepted into the PhD program in Biostatistics, UC Berkeley

Nima Hejazi, MA Spring 2017
Accepted into the PhD program in Biostatistics, UC Berkeley

Ivana Malenica, MA Spring 2017
Accepted into the PhD program in Biostatistics, UC Berkeley

Lina Maria Montoya, MA Spring 2017
Accepted into the PhD program in Biostatistics, UC Berkeley

Thomas M. Carpenito, MA Spring 2018
Accepted into the PhD program in in Population Health, Northeastern University

Partow Imani, MA Spring 2018
Accepted into the PhD program in Biostatistics, UC Berkeley

Rachael Phillips, MA Spring 2018
Accepted into the PhD program in Biostatistics, UC Berkeley

Hector M. Roux de Bezieux, MA Fall 2018
Accepted into the PhD program in Biostatistics, UC Berkeley

Fausto Andres Bustos Carrillo, MA Spring 2019
Director of Epidemiology and Statistics for the Sustainable Sciences Institute

Sonali Dayal, MA Spring 2019
Machine learning engineer, seven.me

Maxwell R. Murphy, MA Spring 2019
Accepted into the PhD program in Biostatistics, UC Berkeley

James Philip Roose, MA Spring 2019
Quantitative Scientist, Flatiron Health

Linqing Wei, MA Spring 2019
Accepted into the PhD program in Biostatistics, UC Berkeley

Sarah GlenLyn Johnson, MA Fall 2019
into the MPH program in Maternal, Child, and Adolescent Health, UC Berkeley

Nicholas Sim, MA Fall 2019
Accepted into the PhD program in Biostatistics, UC Berkeley

Asem Berkalieva, MA Spring 2020
Biostatistician at the Icahn School of Medicine at Mount Sinai

Philippe Andre Boileau, MA Spring 2020
Accepted into the PhD program in Biostatistics, UC Berkeley

Edie Espejo, MA Spring 2020
Research Statistician @ NCIRE

Zhiyue Hu, MA Spring 2020
Accepted into the PhD program in Biostatistics, UC Berkeley

David Chen, MA Summer 2020
Accepted into the PhD program in Biostatistics, UC Berkeley

Yutong Wang, MA Summer 2020
Accepted into the PhD program in Biostatistics, UC Berkeley

Casey Breen, MA Spring 2021
PhD Candidate, UC Berkeley Department of Demography

Haodong Li, MA Spring 2021
Accepted into the PhD program in Biostatistics, UC Berkeley

Yang Li, MA Spring 2021

Lauren Liao, MA Spring 2021
Accepted into the PhD program in Biostatistics, UC Berkeley

Aidan McLoughlin, MA Spring 2021
Accepted into the PhD program in Biostatistics, UC Berkeley

Hao Wang, MA Spring 2021
Accepted into the PhD program in Biostatistics, UC Berkeley

Yi Li, MA Summer 2021
Accepted into the PhD program in Biostatistics, UC Berkeley

Feng Ji, MA Summer 2021
Accepted into the PhD program in Biostatistics, UC Berkeley

Student Directory

Nolan Gunter

nola@berkeley.edu

LinkedIn

Hello! My research interests include HIV/AIDS, causal inference, epidemiology and neuroimaging.

Andy Kim

a_kim@berkeley.edu

I’m interested in the application of causal inference towards complex observational data structures, including high-dimensional and longitudinal data. Specifically, I hope to understand and eventually learn to create methods that can handle the complex relationships specific to different -omics fields, especially when tracked over multiple timepoints. Outside of biostatistics, I’m a huge fan of cooking, producing and playing music using my guitar and keyboard, taking care of my plants, and spending as much time outdoors.

Keying Kuang

keying_kuang@berkeley.edu

I received my B.S in Biometry & Statistics and Agricultural Sciences from Cornell University and Zhejiang University. My research interest lies in solving statistical and computational issues arising in the analysis of complex high-dimensional datasets.

Website

Kirsten Landsiedel

kirsten_landsiedel@berkeley.edu

LinkedIn

GuitHub

My research interests are broadly in the applications of causal inference in public health.

Maura Lievano Nunez

Tyler Mansfield

tyler_mansfield@berkeley.edu

My primary areas of interest include statistical modeling and inference from healthcare data (including machine learning) with a secondary interest in the use of biomarkers in modern-day disease prevention, detection, and treatment. Some specific research subtopics that have caught my attention previously include tree-based models, dimensionality reduction, statistical methodology in medical diagnostic systems, and applications to mental health.

 

Joy Nakato

jznakato@berkeley.edu

Research Interests: Machine learning applications in Public Health, precision public health, disease modeling, casual inference, clinical trial design.

Noel Pimentel

noelpimentel@berkeley.edu

GuitHub

My research interests are in causal inference and its applications in longitudinal EHR to improve community health. Specifically, I’m interested in evaluating the effects of time-varying mental health delivery methods and treatment interventions on mental health outcomes within immigrant populations and BIPOC. I want to empower these communities about their mental health treatment decisions so that they can seek the services they need to improve their health.

Tian (Sky) Qiu

sky.qiu@berkeley.edu

Xueda Shen

shenxueda@berkeley.edu

Probably statistics with biomedical applications

Qiyu Wang

qiyu_wang@berkeley.edu

My research interests include machine learning, causal inference and their applications in biological sciences, medicine and public health.

Xiangyu Yu

Fanding Zhou

Tianyue Zhou

steven_zhou0930@berkeley.edu

I am now broadly interested in causal inference, machine learning, high-dimensional statistics and their applications in other areas.

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