Adarsh Subbaswamy

I am currently a Staff Fellow (Regulatory Scientist) at the U.S. Food and Drug Administration in the Division of Imaging Diagnostics and Software Reliability at the Center for Devices and Radiological Health.

I recently completed my PhD in computer science at Johns Hopkins University, where I was advised by Prof. Suchi Saria. My research focuses on improving the reliability of machine learning models (see our tutorial here) and expanding the technical toolkit available for answering the question: "Is a machine learning model safe and effective?" This includes developing new learning paradigms, methods for monitoring and evaluating performance, and model updating strategies. I most often use tools from causal inference, graphical models, and robust optimization to develop solutions.

I'm particularly interested in applications in healthcare—a safety-critical domain in which the cost of failure is high. I am motivated by the observation that, as a field, we currently lack tools to determine the safety and efficacy of machine learning models developed for healthcare. Over the course of my PhD, I have worked to bridge this gap with respect to the problem of dataset shift: systematic differences between the conditions in which a model was trained and the conditions in which it is deployed or tested that can lead to failures. Some useful starter resources include:

Previously, I received B.S. degrees in computer science and mathematics from Vanderbilt University, where I worked on ensuring privacy through de-identification in electronic medical records with Professor Brad Malin.



Baltimore, Maryland, USA

adarsh (dot) subbaswamy (at) fda (dot) hhs (dot) gov

My GitHub

My Google Scholar