Jeremias Sulam

Jeremias Sulam is an Assistant Professor in Biomedical Engineering, Computer Science, and Applied Mathematics & Statistics at Johns Hopkins University. He holds affiliations with the Center for Imaging Science, Kavli Neuroscience Discovery Institute, and Mathematical Institute for Data Science (MINDS). His research bridges foundational machine learning theory with biomedical applications, focusing on interpretability, robustness, and ethical AI. Key interests include inverse problems in imaging, algorithmic fairness, and biomarker discovery in neuroscience and pathology.

Education: PhD in Computer Science (Technion Israel Institute of Technology, 2018); Biongineering (Universidad Nacional de Entre Ríos, Argentina, 2013).

Research Focus: Developing robust ML models for medical imaging tasks such as radiology and digital pathology. Recent work addresses adversarial robustness, bias mitigation in AI, and explainable algorithms for diagnostic systems. His Sulam Group explores applications in CT denoising, MRI reconstruction, and tumor microenvironment analysis.

Awards:

• NSF CAREER Award (2023)
• Johns Hopkins Catalyst Award (2024)
• Chan Zuckerberg Initiative Award (2024)
• Discovery Award (2024)

Grants & Collaborations: Active in cross-disciplinary projects funded by NIH and industry partners. Leads initiatives on trustworthy AI for healthcare through Johns Hopkins' collaborative networks.