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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.

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

Emeritus

Faculty Associated in Biostatistics Graduate Group

  • Sandrine Dudoit PhD
    Statistics
  • 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

Cam Adams

cam.adams@berkeley.edu

Advisor: Lisa Barcellos

Casey Breen

caseybreen@berkeley.edu

Website

Advisor: Maya Petersen

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

Jessica Briggs

jessica.briggs@berkeley.edu

Website

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

pablo_freyria@gmail.com

LinkedIn

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

sophia.fuller@berkeley.edu

Github

Advisor: Mi-Suk Kang Dufour

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

Feng Ji

fengji@berkeley.edu

Advisor: Sophia Rabe-Hesketh

Yunwen Ji

jiyunwen@berkeley.edu

Advisor: Maya Peterson

Still exploring 🙂

Haodong Li

haodong_li@berkeley.edu

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

liyangc@berkeley.edu

LinkedIn

Advisor: Alan Hubbard

Yi Li

yi_li@berkeley.edu

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

ldliao@berkeley.edu

Github

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

aidan_mcloughlin@berkeley.edu

Github

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

jiaye_pan@berkeley.edu

Advisor: Jingshen Wang

Christopher Rowe

Junming (Seraphina) Shi

junming_shi@berkeley.edu

Advisors: Lexin Li, Elizabeth Purdom

Lei Shi

leishi@berkeley.edu

Website

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

hao_wang@berkeley.edu

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

ztzyz615@berkeley.edu

LinkedIn

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