Rohit Vashisht, PhD

I am a clinical data scientist at the University of California, San Francisco (UCSF), with appointments at the Bakar Computational Health Sciences Institute and the Department of Pediatrics in the School of Medicine, and an affiliation with the UCSF Center for Real-World Evidence. I earned a Ph.D. in Biomedical Science from the Academy of Scientific and Innovative Research in New Delhi, India, a Bachelor of Engineering in Biotechnology from Acharya Institute of Technology in Bangalore, and completed postdoctoral training at Stanford University.

My research focuses on advancing causal inference and machine learning methods to transform real-world clinical data into rigorous, actionable evidence for medical, public health, and regulatory decision-making. I develop and apply causal inference frameworks, including target trial emulation, alongside modern machine learning and generative AI to analyze large-scale electronic health record data from UCSF, across the UC Health system, and national resources such as the National COVID Cohort Collaborative (N3C).

I lead the development of scalable, distributed analytics frameworks that emulate clinical trials using observational data to evaluate the effectiveness and safety of chemical, biological, and digital therapeutics. This work underpins post-market surveillance, real-world evidence generation, and regulatory science. As part of this effort, I am leading the development of UCSF-GPT, a multimodal foundational model trained from scratch on clinical data, and diveEHR, a large-scale clinical text retrieval platform for efficient extraction of evidence from millions of clinical notes. Together, these systems accelerate evidence-based regulatory submissions, pre authorization workflows, and CMS quality reporting, while maintaining strict standards for causal validity, privacy, and reproducibility.

I collaborate closely with multidisciplinary teams across UC Health, the U.S. Food and Drug Administration, and the California Department of Public Health. With over nine years of experience working with large-scale, multi-institutional clinical data, I bring deep expertise in HIPAA compliant analytics, data governance, and observational research, with an emphasis on methodological rigor, transparency, and translational impact. In addition to my research, I help maintain data quality standards for the UCSF and UC Health clinical data warehouse, which uses the OMOP common data model and integrates EHR data from over nine million patients across California.

My work is currently funded by multiple FDA offices, including the Office of the Commissioner, the Center for Biologics Evaluation and Research, the Office of Women’s Health, and the Oncology Center of Excellence through the UCSF–Stanford Center of Excellence in Regulatory Science and Innovation.