Computational Precision Health

The joint UC Berkeley-UCSF Program in Computational Precision Health (CPH) will bridge medicine, statistics, and computation to improve the quality, efficiency, and equity of medicine and population health. BIDS Faculty Affiliate Maya Petersen is a CPH Co-Director, and CPH Core and Affiliate Faculty include BIDS Faculty Affiliates David BammanJoshua Blumenstock, and Bin Yu.

Computing and data are rapidly transforming how society operates, allowing researchers, health practitioners, and the broader scientific and medical community to address questions that were once intractable. The implications for medicine and health are only starting to be realized. Advances in computing, statistics, machine learning, and causal inference will harness new sources of biomedical and clinical data to revolutionize the diagnosis, treatment, and management of disease, extending life spans, improving quality of life, and eliminating some diseases entirely. However, to realize this potential, a new transdisciplinary field is needed.

The UC Berkeley-UCSF Joint Program in Computational Precision Health will bring together the world’s top faculty in computer science, statistics, engineering, clinical care, and population health sciences, together with newly recruited faculty in computational precision health. A key component of the program will be a new doctoral program to train the next generation to conduct transformational research at the interface of the computational and health sciences. Together, this community of scholars will work to understand and improve computational techniques, how health and other data are collected and shared, how both causal inferences and predictions should be made in complex healthcare settings, how advances in computational methods need to be integrated with real-world technology and human systems, and how to harness advances in these areas to reduce systemic inequalities.

The Computational Precision Health Program offers an exceptional high-performance computing and data infrastructure that provides investigators ready access to the latest AI tools and platforms, as well as de-identified health information in machine-readable formats from hundreds of millions of clinical encounters and curated data resources representing biomedicine from genetics and proteins all the way to population-level characteristics in both traditional databases and graph theoretical networks. Access to UCSF’s world-class clinical environment is also available for collaborative development and testing.