Sumit Chopra

Professor Sumit Chopra is an Associate Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University, and an Associate Professor and Director of Machine Learning Research in the Department of Radiology at NYU Grossman School of Medicine. His research focuses on developing machine learning—particularly deep learning—methods for representation learning, with a strong emphasis on healthcare applications. Professor Chopra’s work challenges the prevailing paradigm of AI in healthcare, which largely relies on retrospectively collected, human-interpretable data such as medical images and electronic health records. Instead of training models to merely mimic clinical inference, his research aims to shift AI toward discovery. His lab focuses on two central themes: learning optimal data acquisition strategies to identify the most informative signals for AI systems, and uncovering “unknown unknowns” by detecting hidden patterns within existing datasets. Through this approach, his work seeks to unlock previously inaccessible information and enable more impactful, reliable, and scalable clinical AI systems. A major application area of his research is medical imaging, particularly magnetic resonance imaging (MRI). His group develops end-to-end learning frameworks that operate directly on raw measurement data (e.g., k-space) and explores adaptive, patient-aware imaging systems to improve efficiency, accessibility, and diagnostic performance. His research has been recognized through publications at leading venues such as NeurIPS, ICML, ICLR, and major medical imaging journals, as well as support from NIH, NSF, and NYU research initiatives.