Please note: This seminar will be given online.
Vikrant Singhal, Khoury College of Computer Sciences
Northeastern University
Please note: This seminar will be given online.
Chen Ma, School of Computer Science
McGill University
Please note: This seminar will be given online.
Jasper Lee, Department of Computer Science
Brown University
Please note: This PhD defence will be given online.
Shihabur Rahman Chowdhury, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Raouf Boutaba
Please note: This seminar will be given online.
Ahmed Saeed, Postdoctoral Associate
Computer Science and Artificial Intelligence Laboratory, MIT
Please note: This PhD seminar will be given online.
Guojun Zhang, PhD candidate
David R. Cheriton School of Computer Science
Supervisors: Professors Pascal Poupart and Yaoliang Yu
Please note: This master’s thesis presentation will be given online.
Kyle Robinson, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Dan Brown
Please note: This seminar will be given online.
Praveen Kumar, Department of Computer Science
Cornell University
Please note: This seminar will be given online.
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.
Please note: This PhD seminar will be given online.
Shihabur Chowdhury, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Raouf Boutaba
Please note: This PhD seminar will be given online.
Joseph (Yossef) Musleh, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Éric Schost
Please note: This seminar will be given online.
Dawei Zhou, Department of Computer Science
University of Illinois at Urbana-Champaign
Please note: This seminar will be given online.
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.