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.

Me? I'm fascinated by our understanding of the information contained in signals: from the image of the mountain peak to the rendering of an ultrasound beam in medical imaging. This understanding is often formalized through the construction of (hopefully universal) models, thereby 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 am an Assistant Professor in the Biomedical Engineering department at Johns Hopkins University, and I'm affiliated with the Mathematical Institute for Data Science (MINDS). I received my Bioengineering degree from UNER (Argentina) in 2013, and my PhD in Computer Science from Technion in 2018 under Michael Elad's supervision. My research interests are focused on general signal and image processing, sparsity-inspired modeling, machine learning and their application to biomedical sciences.

I'm looking for excellent and motivated students to work with. If you are interested in tackling some fascinating problems in the frontier of machine learning and biomedical sciences, contact me.

Contact: Office 230B, Clark Hall, Homewood Campus (Baltimore, MD)


Phone: 410-516-9776 / Fax 410-516-4594


  • October 2018: I have joined the BME Department and the MINDS institute at Johns Hopkins University!
  • July 2018 – Our new review paper, Theoretical Foundations of Deep Learning via Sparse Representations: A Multilayer Sparse Model and Its Connection to Convolutional Neural Networks has just been featured in Signal Processing Magazine.
  • June 2018 – Our paper on Multi-Layer Sparse Modelling just accepted to IEEE-TSP!
  • April 2018 – Travelling to ICASSP to present our work Projecting onto the Multi-Layer Convolutional Sparse Coding Model at the Special Session on Learning Signal Representation using Deep Learning!
  • November 2017 – Thanks to Gitta Kutyniok and all the organizers for the invitation to present our work at the CoSIP Intense Course on Deep Learning! Here at the slides of my talk.