The doctoral program provides training in two domains: Computational Sciences and Health Sciences. Students will develop proficiency in both, with most students focusing on one of the two to develop greater depth and expertise.
Parallel computing and data at scale, algorithms, numerical analysis, machine learning and AI, natural language processing, data storage & processing (Databases, ETL); principles of statistical inference and modeling, statistical computing, estimation and inference with high dimensional sparse data, loss-based estimation and cross validation, formal causal inference; health informatics: electronic health records, data standards, privacy, IT policy, interoperability.
Clinical decision sciences and cognitive informatics: e.g., diagnosis, treatment planning, decision support systems, evidence-based medicine, medical ontologies; clinical delivery: learning health systems, patient-centered care, etc.; clinical research: decentralized (mobile) clinical trials, hyper-personalized studies (e.g., N-of-1, Just-in-Time Adaptive Interventions (JITAI)), implementation science; health information policy: e.g., data sharing during pandemics.
- Complete coursework at both the UCSF and UC Berkeley campuses
- Have a “home campus” that matches the home campus of their PhD advisor
- Have access to resources on both campuses
Computational Precision Health Cornerstone course series (3 semester units x 2 terms)
This course series, which uses Problem-Based Learning to build student’s ability to work effectively in interdisciplinary teams, from ideation to development, testing, and validation in the real world.
Foundational courses (at least 12 semester units)
Foundational courses are personalized to compliment students’ backgrounds and training, and support their interests. Courses will be drawn from Electrical Engineering and Computer Science, Statistics, Public Health, Epidemiology and Biostatistics, Medicine, and other departments and schools at UCSF and UC Berkeley.
Race and Racism in Science, and Ethics and the Responsible Conduct of Research (2.3 semester units)
Students will enroll in the UCSF courses GRAD 202 Race and Racism in Science (2 quarter units=1.3 semester units), which discusses the historical background of systemic racism in scientific research, and UCSF GRAD 214 Ethics and the Responsible Conduct of Research, L. Silva (1.5 quarter units=1 semester unit), which addresses key issues affecting the responsible conduct of scientific research.
Advanced Electives (at least 6 semester units)
A minimum of two advanced electives are required, and will be determined in consultation with the student’s Academic Advisor
Computational Precision Health Practicum (2 semester units x 2 terms)
This series, taken during year two of the program, augments the Cornerstone to provide deep and continuing exposure to clinical and public health context in which CPH advances are to be deployed. Students will have in-depth real world exposure to clinical, research, and operational work in inpatient, outpatient, community health, and/or public health settings.
Computational Precision Health Doctoral Seminar (2 semester units x 6 terms)
Students will enroll in six terms of the doctoral seminar, including the first two terms after matriculation. Seminar will consist of journal club-style discussion of recent literature in computational precision health, talks by guest faculty, and presentations by second and third year students on work in progress.
Rotations (at least 4 units)
Students will take two 10-week research group rotations in their first year. One rotation will be on each campus, with one rotation in a predominantly computational science “lab” (with a health emphasis) and one in a health science “lab” (with a computational emphasis).