An Investigation of Memorization Risk in Healthcare Foundation Models
Sana Tonekaboni
Sana is a postdoctoral fellow at the Broad Institute of MIT and Harvard. Her research focuses on developing methods that integrate multimodal biomedical data to better understand human health. She is also interested in challenges of deploying clinical ML in healthcare environments and finding solutions for effective and safe use of such tools in practice. Sana received her PhD in computer science from the University of Toronto, under supervision of Dr. Anna Goldenberg, where she was an Apple scholar in AI/ML and a CIHR health system impact fellow.
Lena Stempfle
Lena Stempfle is a postdoctoral associated with MIT CSAIL starting in the fall of 2025 with WASP International Postdoctoral Scholarship. She completed her PhD in Computer Science at Chalmers University of Technology in Sweden, where she was affilicated with the Healthy AI lab. Her research focuses on the intersection of machine learning and healthcare, with a particular interest in predictions with missing values at test time, time series, and causality. Lena’s aims to develop interpretable and accurate models to support clinical decision-making. Prior to her PhD, she completed a Master’s degree in Information Systems and Management at Karlsruhe Institute of Technology (KIT) in Germany.