PhD Defence • Algorithms and Complexity — Related Orderings of AT-Free Graphs
Please note: This PhD defence will be given online.
Jan Gorzny, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Jonathan Buss
Jan Gorzny, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Jonathan Buss
Anton Mosunov, Digital Assets Group
University of Waterloo
Akshitha Sriraman, Computer Science and Engineering
University of Michigan
Greg Philbrick, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Craig Kaplan
Dallas Card, Postdoctoral scholar
NLP Group and the Data Science Institute, Stanford University
Machine learning and natural language processing have become increasingly influential, both in commercial applications and as key tools for research in the natural and social sciences. In both cases, however, research in these fields raises numerous concerns related to bias, transparency, robustness, and how we communicate information.
Dawei Zhou, Department of Computer Science
University of Illinois at Urbana-Champaign
Joseph (Yossef) Musleh, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Éric Schost
Shihabur Chowdhury, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Raouf Boutaba
Hongyang Zhang, Postdoctoral Fellow
Toyota Technological Institute at Chicago
Deep learning models are often vulnerable to adversarial examples. In this talk, we will focus on robustness and security of machine learning against adversarial examples. There are two types of defenses against such attacks: 1) empirical and 2) certified adversarial robustness.
Praveen Kumar, Department of Computer Science
Cornell University