BMIR Labs and Staff

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.

Ben Viggiano

Ben is currently a second-year academic master’s student in the Biomedical Informatics program. Prior to Stanford, Ben completed undergraduate degrees in computer science and biomedical engineering at the University of Wisconsin-Madison, specializing in artificial intelligence and medical imaging.

Research Interests: Multimodal approaches to enable clinical decision support from diverse data sources, and fairness/bias analysis of healthcare focused models.

Jason Fries

Jason is a staff research scientist in the Shah Lab. His interests fall in the intersection of computer science and medical informatics. His research interests include:
• Machine learning with limited labeled data, e.g., weak supervision, self-supervision, and few-shot learning.
• Multimodal learning, e.g., combining text, imaging, video and electronic health record data for improving clinical outcome prediction
• Human-in-the-loop machine learning systems.
• Knowledge graphs and their use in improving representation learning

Akash Chaurasia

Akash is a 2nd year Master’s student studying Computer Science focusing on training and evaluating clinical foundation models. He has undergraduate degrees in Biomedical Engineering and Computer Science from Johns Hopkins University. His current research focuses on evaluating generative models for clinical workflows, as well as building new generative models by combining structured and unstructured electronic health record data. His previous work includes computer vision, multimodal modeling, and meta-learning.

Michael Wornow

Michael is a 3rd year computer science PhD student focused on developing and operationalizing large-scale AI models in healthcare systems. He is interested in developing methods that integrate multimodal data from electronic health records (particularly clinical text and structured codes) into "foundational models" for clinical data, as well as developing a better understanding for how we can more effectively evaluate and benchmark the performance of such models and measure their real-world utility for patients.

Zepeng 'Frazier' Huo

Zepeng 'Frazier' Huo as a Postdoctoral Scholar to the Center for Biomedical Informatics Research as part of Shah Lab. He earned his Ph.D. and master's degree in computer science from Texas A&M University. In his previous work, he investigated the heterogeneity aspect of medical AI method in terms of population level and individual level and how that might have affected machine learning models in different ways. The approaches he took included uncertainty quantification, Mixture-of-Experts, domain adaptation and continual learning. At Stanford, he is interested in investigating the potential benefits of Foundation Models in healthcare, which have shown amazing results in text generation, dialogue, and even production of art.