Kexin Rong, Department of Computer Science
Stanford University
Khadija Tariq, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Nancy Day
Sepideh Mahabadi
Toyota Technological Institute at Chicago
Searching and summarization are two of the most fundamental tasks in massive data analysis. In this talk, I will focus on these two tasks from the perspective of diversity and fairness.
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
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.