ISyE Seminar Series: Ju Sun

"Deep Learning with Nontrivial Constraints"

Ju Sun

Assistant Professor at the Department of Computer Science & Engineering
University of Minnesota Twin Cities

About the Seminar:

Despite the sweeping success and impact of deep learning in numerous domains, imposing explicit constraints is relatively new but increasingly pressing in deep learning (DL), driven by, for example, trustworthy AI that performs robust optimization over complex perturbation sets and scientific and engineering applications that require respect for physical laws and constraints. In this talk, we will (1) survey DL problems with nontrivial constraints across science, engineering, and medicine, (2) highlight the NCVX computing framework we have recently built, which provides deterministic solvers to solve constrained DL problems, and (3) invite the optimization community to solve the stochastic constrained DL problems.

About the Speaker:

Ju Sun is an assistant professor at the Department of Computer Science & Engineering, the University of Minnesota at Twin Cities (UMN). His research interests span computer vision, machine learning, numerical optimization, data science, computational imaging, and healthcare. His recent efforts are focused on the foundations and computation of deep learning, and on applying deep learning to tackle challenging problems in science, engineering, and medicine. Before this, he worked as a postdoc scholar at Stanford University (2016-2019), and obtained his Ph.D. degree from Columbia University's Electrical Engineering in 2016. He won the best student paper award from SPARS'15, honorable mention of doctoral thesis for the New World Mathematics Awards (NWMA) 2017, and AAAI New Faculty Highlight Programs 2021, Frontiers of Science Award in Mathematics 2024, and the McKnight Land-Grant Professorship of UMN 2025-2027.

If you wish to be added to the ISyE Graduate Seminar Series emailing list, please email Event Coordinator Emily Rice at [email protected].