Joint Seminar BME/CISE/Hariri: Jeremias Sulam, Johns Hopkins University

Imaging, Data, and Learning: Modern challenges in Biomedical Data Science

Modern machine learning methods are revolutionizing biomedical imaging and data science. We can now image and reconstruct, measure, and understand biological structures from data to a degree that was hardly imaginable a decade ago. Yet, with these more powerful tools come new challenges, including uncertainty quantification, interpretability, (un)fairness, among others. This talk will review works that illustrates these problems, and present some solutions for them.

Jeremias Sulam received his bioengineering degree from Universidad Nacional de Entre Ríos, Argentina, in 2013, and his PhD in Computer Science from the Technion – Israel Institute of Technology, in 2018. He joined the Biomedical Engineering Department at Johns Hopkins University in 2018 as an Assistant Professor, and he is also a core faculty at the Mathematical Institute for Data Science (MINDS) and the Center for Imaging Science at JHU. He is the recipient of the Best Graduates Award of the Argentinean National Academy of Engineering, and the Early CAREER award of the National Science Foundation. His research interests include machine learning, inverse problems, biomedical imaging, and mathematics of data science.

Faculty Hosts: Ayşe Coşkun, Yannis Paschalidis, John White