Jeremias Sulam

For thousands of years explorers were inspired by the sight of uncharted shores, or by the defiant look of new and higher peaks - right after having overtaken the last. Others rejoiced with the discovery of a new star cruising across the sky, and thrived when realizing that they could predict where the bright dot would be with the passing of time. 

I'm fascinated by our understanding of the information contained in data, from the image of the mountain peak to immunohistochemistry images in digital pathology. This understanding is often formalized through the construction of models, thus capturing the information contained in these different data sources. If successful, one can deploy these constructions to tackle inverse problems of different kinds, prediction, clustering and other machine learning tasks, and more.  I'm particularly interested in the responsible use of machine learning, studying aspects of robustness and interpretability.

I am an Assistant Professor in the Biomedical Engineering Department, and hold a secondary appointment in the Computer Science Department, at Johns Hopkins University. I'm also affiliated with the Mathematical Institute for Data Science (MINDS), the Center for Imaging Science (CIS) and Kavli. I received my Bioengineering degree from UNER (Argentina) in 2013, and my PhD in Computer Science from Technion in 2018 with Miki Elad. I am a recipient of the National Science Foundation’s Early CAREER Award. My research interests are focused on general signal and image processing, sparsity-inspired modeling, machine learning and their application to biomedical sciences. 

Contact:  Office 320B, Clark Hall, Homewood Campus (Baltimore, MD)

Email: jsulam at jhu dot edu


I am very grateful to NSF, NIH, DARPA, CISCO Research, CANON Medical Research and the Toffler Cheritable Trust for sponsoring part of our research.