Jeremias Sulam
Portrait
Assistant Professor and William R. Brody Faculty Scholar, Johns Hopkins University

I am the William R. Brody Faculty Scholar and an Assistant Professor in Biomedical Engineering at Johns Hopkins University, with secondary appointments in Computer Science and Applied Mathematics and Statistics. I am also a core member of MINDS, CIS, the Kavli Neuroscience Discovery Institute, and the Data Science and AI Institute.

My research bridges computer science, applied mathematics, and biomedical sciences. I develop theory-grounded and interpretable machine learning methods with applications in inverse problems, radiology and biomedical imaging, and biomarker discovery.

My research is/has been supported by NIH, NSF, the Chan Zuckerberg Initiative, CISCO Research, CANON Medical Research, and the Toffler Charitable Trust, and empowered by an amazing team of students.

Curriculum Vitae
Google Scholar GitHub Twitter
Education
  • Technion
    Technion
    Department of Computer Science
    Ph.D., Advisor Miki Elad
    2013 - 2018
  • Universidad Nacional de Entre Rios
    Universidad Nacional de Entre Rios
    B.S. in Bioengineering
    2007 - 2013
Honors & Awards
  • William R. Brody Faculty Scholar
    2025
  • NSF Career Award
    2023
  • Karen Toffler Scholar
    2022
  • Best Graduates award, Argentinean National Academy of Engineering
    2021
News
2025
I am honored to be appointed the William R. Brody Faculty Scholar in the school of Engineering. Read more »
Sep 01
Ramchandran defends his thesis - good luck Ram!
Jul 11
Our tutorial on Foundations of Interpretable AI was presented at CVPR, together with Aditya Chattopadhyay and René Vidal
Jul 01
2024
Honored to the receive the Johns Hopkins Catalyst Award this year. Read more »
Oct 01
Invited for a talk at BIRS Workshop on Computational Harmonic Analysis in Oaxaca, MX
Sep 01
Ambar defended his PhD thesis - Good luck Ambar!
Jul 01
Jun 01
Apr 01
2023
Two papers presented at NeurIPS
Dec 01
Invited talk at SLMath Institute @ Berkeley
Sep 01
Selected Publications (view all )
Beyond Scores: Proximal Diffusion Models
Beyond Scores: Proximal Diffusion Models

Zhenghan Fang, Mateo Diaz, Sam Buchanan, Jeremias Sulam

arXiv 2025

Diffusion ModelsGenerative AITheory

[Paper]

Beyond Scores: Proximal Diffusion Models

Zhenghan Fang, Mateo Diaz, Sam Buchanan, Jeremias Sulam

arXiv 2025

Diffusion ModelsGenerative AITheory

[Paper]

Multiaccuracy and Multicalibration via Proxy Groups
Multiaccuracy and Multicalibration via Proxy Groups

Beepul Bharti, Mary Versa Clemens-Sewall, Paul H. Yi, Jeremias Sulam

International Conference of Machine Learning 2025

ML TrustworthinessTheory

[Paper]

Multiaccuracy and Multicalibration via Proxy Groups

Beepul Bharti, Mary Versa Clemens-Sewall, Paul H. Yi, Jeremias Sulam

International Conference of Machine Learning 2025

ML TrustworthinessTheory

[Paper]

Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns from Breast Multiplexed Digital Pathology
Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns from Breast Multiplexed Digital Pathology

Zhenzhen Wang, Cesar A. Santa-Maria, Aleksander S. Popel, Jeremias Sulam

Patterns: Cell Press 2025 Cover feature

ML InterpretabilityBiomarker DiscoveryDigital Pathology

[Code] [Paper] [Press]

Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns from Breast Multiplexed Digital Pathology

Zhenzhen Wang, Cesar A. Santa-Maria, Aleksander S. Popel, Jeremias Sulam

Patterns: Cell Press 2025 Cover feature

ML InterpretabilityBiomarker DiscoveryDigital Pathology

[Code] [Paper] [Press]

Testing Semantic Importance via Betting
Testing Semantic Importance via Betting

Jacopo Teneggi, Jeremias Sulam

NeurIPS 2024

ML InterpretabilityTheory

[Paper] [Code] [Demo]

Testing Semantic Importance via Betting

Jacopo Teneggi, Jeremias Sulam

NeurIPS 2024

ML InterpretabilityTheory

[Paper] [Code] [Demo]

What's in a Prior? Learned Proximal Networks for Inverse Problems
What's in a Prior? Learned Proximal Networks for Inverse Problems

Zhenghan Fang, Sam Buchanan, Jeremias Sulam

ICLR 2024

Inverse ProblemsTheoryImaging

[Paper] [Site] [Code]

What's in a Prior? Learned Proximal Networks for Inverse Problems

Zhenghan Fang, Sam Buchanan, Jeremias Sulam

ICLR 2024

Inverse ProblemsTheoryImaging

[Paper] [Site] [Code]

Sparsity-aware generalization theory for deep neural networks
Sparsity-aware generalization theory for deep neural networks

Ramchandran Muthukumar, Jeremias Sulam

COLT 2023

[Paper]

Sparsity-aware generalization theory for deep neural networks

Ramchandran Muthukumar, Jeremias Sulam

COLT 2023

[Paper]

Examination-level supervision for deep learning–based intracranial hemorrhage detection at head CT
Examination-level supervision for deep learning–based intracranial hemorrhage detection at head CT

Jacopo Teneggi, Paul H. Yi, Jeremias Sulam

Radiology: Artificial Intelligence 2023 Cover feature

[Paper] [Press]

Examination-level supervision for deep learning–based intracranial hemorrhage detection at head CT

Jacopo Teneggi, Paul H. Yi, Jeremias Sulam

Radiology: Artificial Intelligence 2023 Cover feature

[Paper] [Press]

DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging
DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging

Zhenghan Fang, Kuo-Wei Lai, Peter van Zijl, Xu Li, Jeremias Sulam

Medical Image Analysis 2023

[Paper] [Code]

DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging

Zhenghan Fang, Kuo-Wei Lai, Peter van Zijl, Xu Li, Jeremias Sulam

Medical Image Analysis 2023

[Paper] [Code]

All publications