PhD Course Requirements

PhD students are required to obtain 64 units of coursework from the following courses. Full time graduate student must register for a minimum of 12 units per quarter. These 12 units can be made up of a combination of required coursework as described below, additional elective coursework if any, and special study courses. All student course programs, as well as any changes throughout the quarter, must be approved by a faculty advisor prior to registering for classes each quarter.

Consult the course catalog here to look up course descriptions.

Required Courses (61 units)

  1. Required (Core) Courses in the Department of Mathematics (24 units)

    1. MATH 281 A, B,C (Mathematical Statistics I-II, 4 units each)

    2. MATH 282 A, B (Linear Models, 4 units each)

    3. MATH 284 (Survival Analysis, 4 units)

  2. Required (Core) Courses in Biostatistics (29 units) Each of:

    1. FMPH 221: Biostatistical Methods I (4 units)

    2. FMPH 222: Biostatistical Methods II (4 units)

    3. FMPH 223: Analysis of Longitudinal Data (4 units)

    4. FMPH 241: Biostatistics Rotation (2 quarters, 3 units each)

    5. FMPH 290: Biostatistics Seminar/Journal Club (3 quarters, 1 unit each)

    Two among the following courses:

    1. FMPH 224: Clinical Trials and Experimental Design (4 units)

    2. FMPH 225: Advanced Topics in Biostatistical Inference (4 units)

    3. FMPH226: Statistical Methods for Observational Studies (4 units)

    4. FMPH 227: Advanced Multivariate Methods (4 units)

    We note that all of the Biostatistics core courses except FMPH 290 carry a data analysis component. Students will be exposed to projects involving advanced data analyses to address complex life sciences problems. All courses except FMPH 290 are letter grade only.

  3. Required Life Sciences (8 units)

    Two courses at the upper division or the graduate level in Biomedical Sciences, Neurosciences, Epidemiology, Public Health, Biology, Systems Biology, Bioengineering, or Medicine, letter grade if possible. These courses are intended to provide the students with background in the life sciences and an introduction to complex life sciences problems that will constitute the area of application of their thesis and future research. The students are strongly encouraged to take further Life Sciences elective courses that are relevant to their research. Please consult the linked list of possible life science courses.  The selection of all Life Sciences courses should be made in consultation with the thesis advisor.

Elective Courses (3 units)

Students are required to take at least 3 additional units of elective courses for letter grade from the following list.

  1. Biostatistics Elective Courses

  2. The Biostatistics elective courses are listed under one umbrella course number: FMPH 242, Advanced Topics in Biostatistics (3 units). This course is taught in rotation by the Division of Biostatistics and Bioinformatics faculty, and the curriculum will vary. Among the topics are:
    1. Random field theory and applications in image analysis

    2. Advanced Statistical Computing

    3. Bayesian methods

    4. Statistical collaboration in health sciences

  3. Statistical Methods Electives:

    1. MATH 280 ABC (Probability Theory, 4 units)

    2. MATH 287 B (Multivariate Analysis, 4 units)

    3. MATH 287 D (Statistical learning, 4 units)

    4. MATH 287A, C (Time Series Analysis, 4 units each)

    5. MATH 202A (Applied Algebra I, 4 units)

    6. MATH 240ABC (Real Analysis, 4 units)

    7. MATH 271ABC (Numerical Optimization, 4 units)

    8. MATH 285 (Stochastic Processes, 4 units)

  4. Computer Science Electives:

    1. CSE 202 (Algorithm Design and Analysis)

    2. ECE 273 (Convex Optimization, 4 units)

    3. CSE 250B: (Learning Algorithms, 4 units)

    4. CSE 255: (Data mining and predictive analytics, 4 units)

    5. CSE 260: (Parallel computation)

    6. CSE 283 (Genomics, Proteomics, Systems Biology, 4 units)

Biostatistics Rotations (FMPH 241)

The Biostatistics Rotations are a singular feature of this PhD program that takes advantage of the extensive involvement of the program faculty in collaborative and interdisciplinary work within the Life Sciences. Students will complete at least two and up to five quarter-length rotations before advancing to candidacy, each in the form of an interdisciplinary applied data analysis project. They may work in collaboration with any UCSD faculty researcher who conducts studies or experiments which generate data in the medical, biological, public health or pharmacologic sciences, and who will serve as a subject area mentor, under the primary mentorship of any Biostatistics or Statistics member of the interdepartmental program. Each practicum will last a minimum of 10 weeks and will involve the analysis of original data. The student will prepare or substantially contribute to a project report, which will be reviewed and signed off on by the mentor. The rotation may be conducted as part of employment as a Graduate Student Researcher or as part of the dissertation research. A report based on an internship of at least 10 weeks duration at a facility, government health office, institute or company outside of UCSD focusing on biological or medical research can also be used to satisfy this requirement.

Examples of Past Rotation Topics


Winter 2022

coming soon!

Fall 2021


Faculty Rotation Title
Christian Pascual Sonia Jain Modeling Individual Goal Achievement Behavior Using Bayesian Networks
Lucy Shao Wes Thompson Covid-19 Survey Data Analysis by Generalized Propensity Score

Spring 2021


Faculty Rotation Title
Jiyu Luo Lily Xu Improving Efficiency of Time Averaged Treatment Effect in Survival Analysis
Junting Ren Karen Messer &  Xinlian Zhang Using Negative Control Outcome and Exposure to Adjust for Unmeasured Confounding

Winter 2021

Student Faculty Rotation Title
Junting Ren

Armin Schwartzman & Rany Salem

Association Between Phenotype Correlations and Disease Status Using UK Biobank Data
Morris Wu Xin Tu Investigation of Tree-structured Methods to Improve Inference Validity for Linear Models
Michael Cheung Florin Vaida Seemingly Unrelated Regression Equations for Longitudinal Clinical Trials
Chenyu Liu Loki Natarajan Multiple Imputation for Longitudinal Data with Missing Continuous Outcome

Fall 2020

Student Faculty Rotation Title
Michael Cheung Florin Vaida Efficient and Robust Design in Longitudinal, Cluster Randomized Clinical Trials with Repeated Measures
Morris Wu Steve Edland Mediation Analysis for Alzheimer’s Data
Jiyu Luo Wenxin Zhou Distributed Adaptive Huber
Chenyu Liu Xin Tu Partial Least Squares on Beta-Diversity

Spring 2020


Faculty Rotation Title
Yu Zhao   Armin Schwartzman Height Distribution of Local Maxima of Noncentered Gaussian Fields