Robotics Colloquium: Guest Ju Sun

TITLE: Robustness in deep learning: where are we? 

ABSTRACT: Deep learning (DL) models are not robust: adversarially constructed and irrelevant natural perturbations can break them abruptly. Despite intensive research in the past few years, surprisingly, there have yet to be tools for reliable robustness evaluation in the first place. I’ll describe our recent efforts toward building such a reliable evaluation package. This new computational capacity leads to more concerns than hopes: we find that adversarial training, a predominant framework toward achieving robustness, is fundamentally flawed. On the other hand, before we can obtain robust DL models, or trustworthy DL models in general, we must safeguard our models against making severe mistakes to make imperfect DL models deployable. A promising approach is to allow DL models to restrain from making predictions on uncertain samples. I’ll describe our recent lightweight, universal selective classification method that performs excellently and is more interpretable. 

BIO: Ju Sun is an assistant professor at the Department of Computer Science & Engineering, the University of Minnesota at Twin Cities. His research interests span computer vision, machine learning, numerical optimization, data science, computational imaging, and healthcare. His recent efforts are focused on the foundation and computation for deep learning and applying deep learning to tackle challenging science, engineering, and medical problems. Before this, he worked as a postdoc scholar at Stanford University (2016-2019), obtained his Ph.D. degree from Electrical Engineering of Columbia University in 2016 (2011-2016), and B.Eng. in Computer Engineering (with a minor in Mathematics) from the National University of Singapore in 2008 (2004-2008). 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.

Start date
Friday, Oct. 20, 2023, 2:30 p.m.
End date
Friday, Oct. 20, 2023, 3:30 p.m.

In-person: Drone Lab: 164 Shepherd Lab