Curriculum and Courses

Curriculum Overview

The Ph.D. program curriculum is designed to provide training in computational, statistical, and mathematical techniques along with a strong background in a biomedical specialty.  The curriculum has four components:  

  • Core courses in Biomathematics that are typically taken by the entire student cohort and are applicable to many biomedical domains; 

  • Elective courses in Applied Modeling that cover modeling and its application in a specific biomedical domain (in addition to the Biomath courses in this category, courses from other departments may also satisfy this component)

  • Elective courses in Quantitative Reasoning that are typically graduate courses in Mathematics, Computer Science or Statistics; 

  • Elective courses in Biomedical Sciences that provide training in the students’ biomedical domain of interest to ensure our graduates can collaborate fluently with domain scientists. 

Many of the Quantitative Reasoning and Biomedical Sciences elective requirements can be satisfied by upper-division or MS-level courses taken prior to enrolling in the Ph.D. program.  



Number of Courses

Required Core Biomathematics Courses

(courses that cover modeling theory applicable to many biomedical domains)


Elective Courses in Applied Modeling

(courses that cover modeling and its application in a specific biomedical domain)


Elective Courses in Quantitative Reasoning 

(these can be satisfied with Masters in quantitative science or previous coursework)


Elective Courses in Biomedical Sciences 

(these can be satisfied with previous coursework, including the first two years of MD)



More information on the curriculum can be found at

Recommended First-Year Course Plan for 2023 – 2024

All incoming Ph.D. students are encouraged to take the same set of courses their first year, which combines core curriculum courses with training in each of the three research areas.  The recommended courses for this academic year (with the typical quarter they are taught):

  • Biomath 200: Research Frontiers in Biomathematics (Fall, seminar course)
  • Biomath 201: Deterministic Models in Biology (Fall, core class)
  • Biomath 202: Biological Systems: Structure, Function, Evolution (Winter, core class)
  • Biomath 203: Stochastic Models in Biology (Winter, core class)
  • Biomath 204: Biomedical Data Analysis (Spring, core class)
  • Biomath 205: Top Computational Algorithms (Fall, core class)
  • Biomath 206: Introduction to Mathematical Oncology (Spring, elective)
  • Biomath 208: Geometric Methods in Medical Imaging (Spring, core class)
  • Biomath 211: Mathematical and Statistical Phylogenetics (Winter, elective)

Additional elective courses in applied modeling within Biomath (provided in 2024–2025) include:

  • Biomath 213: Modeling Vascular Networks (Spring, elective)
  • Biomath 207: Theoretical Genetic Modeling (Winter, elective)


More information on all these courses can be found at:


In addition, Biomath Ph.D. students are encouraged to start doing supervised research projects as soon as they feel settled into our program. These research projects often start gradually and ramp up if there is a good fit in the scientific interest and preferred working environment between the student and mentor. These projects can begin by simply attending group meetings and doing supervised background readings, moving to smaller joint projects, and growing to full-blown doctoral research. The student receives credit for this work by enrolling in

  • Biomath 596: Directed Individual Study or Research in Biomathematics


Elective Course Plans by Modeling Area

Beyond the first-year courses, there are many excellent courses (many of which are taught by department faculty) for students to obtain the background necessary for their dissertation research. The list of courses in the three broad Biomath research areas, and the faculty chairs of these areas, are a support structure to help students navigate which courses they should consider taking. Once a student officially joins their doctoral mentor’s group and starts their doctoral research, the direction of the research itself, with the mentor’s guidance, should inform what elective courses will be most useful. The roadmaps for the three Biomath research areas are linked here:


The faculty in Computational Medicine are dedicated instructors and many of them pioneered new courses that they regularly teach.  Courses taught by Computational Medicine faculty include:

  • Deterministic Models in Biology (Biomath 201)
  • Biological Systems: Structure, Function, Evolution (Biomath 202)
  • Stochastic Models in Biology (Biomath 203)
  • Biomedical Data Analysis (Biomath 204)
  • Top Computational Algorithms (Biomath 205)
  • Geometric Methods in Medical Imaging (Biomath 208)
  • Machine Learning in Bioinformatics (Biomath M226)
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