Shah Lab
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.
Miguel Fuentes
Miguel Fuentes is a second year MS Computer Science student specializing in language models, with a focus on their applications in clinical settings. His interests also include optimizing model training and improving inference efficienecy
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.