Shah Lab
Principal Investigator: Nigam H. Shah, MBBS, PhD
Advancing artificial intelligence, biomedical informatics, and learning health systems to improve healthcare delivery and patient outcomes.
Nigam H. Shah, MBBS, PhD.
Dr. Shah is Associate Director of the Division of Computational Medicine. He is a physician scientist and an expert in approaches that combine machine learning and knowledge of medical ontologies to enable use cases for the learning health system. He develops methods to analyze large unstructured data sets for use in data-driven medicine and to enable improvements is decision-making in medicine and health care. Dr. Shah joined the Division faculty in 2011. Prior to that he served as a Research Scientist in the Division and trained as a Postdoctoral Fellow with Dr. Mark Musen between 2005-2007.
Suhana Bedi
Suhana Bedi is a Biomedical Data Science PhD student at Stanford with a focus in building and evaluating Foundation Models for healthcare, and uncovering insights from complex medical datasets.
Alison Callahan
Alison Callahan is an Instructor and Clinical Data Scientist in the Center for Biomedical Informatics. In collaboration with Nigam Shah’s group, her work involves research and development of informatics methods for the analysis of biomedical and clinical data to derive insights and inform medical decision making. Her current research focuses on using informatics to improve the breadth and quality of data available from EHRs for studying perinatal and reproductive health.
Emma Sage
Emma is the project manager for GUIDE-AI, bringing multiple years of experience working in both health tech startup and academic research spaces. Her interests lie in accessible healthcare technology, genomics, and One Health.
Emma completed her undergraduate education at UCLA, where she studied biology with a premed focus. Following graduation, she worked at 23andMe for four years as a product specialist, helping to inform user experience and product strategy. She returned to UC Berkeley to pursue a Master’s in Public Health, concentrating on Environmental Health Science, where she conducted research on the effects of wildfire smoke on human health. Emma came to Stanford University as a staff research project manager in 2023, and joined Computational Medicine and the GUIDE-AI team in 2025.
Akshay Swaminathan
From Wood-Ridge, New Jersey, Akshay is pursuing an MD at Stanford School of Medicine. He graduated from Harvard University with a bachelor’s degree in statistics and a minor in global health and health policy. Akshay aspires to strengthen health systems in low resource areas by combining medicine, data science, and systems design. His research focuses on developing, deploying, and evaluating machine learning systems in clinical workflows. As director of Refresh Bolivia, a global health non-profit, he and his team built a primary healthcare clinic that serves 10,000 indigenous residents in Cochabamba. His industry experience includes developing methods for analyzing real-world oncology data at Flatiron Health and leading the data science team at Cerebral, a national tele-mental health company.
Alyssa Unell
Alyssa Unell is a Computer Science PhD student advised by Nigam Shah and Sanmi Koyejo, focusing on robust LLM evaluation and deployment in healthcare settings. She is broadly interested in model evaluation, synthetic data creation, and data-centric approaches to improve model performance in medical settings. Previously, she received her bachelor’s degree in computational neuroscience from MIT where her research utilized neuroscience insights to improve neural network performance.