Seminar

Please note: This PhD seminar will be given online.

Michael Abebe, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Khuzaima Daudjee

Please note: This PhD seminar will be given online.

Catherine St-Pierre, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Éric Schost

Please note: This seminar will be given online.

Vishwas Bhargava
Department of Computer Science, Rutgers University

Host: Professor Rafael Oliveira

Please note: This seminar will be given online.

Emmanouil-Vasileios Vlatakis-Gkaragkounis
Department of Computer Science, Columbia University

Host: Professor Gautam Kamath

Please note: This seminar will be given online.

Jason Altschuler, Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology

Please note: This seminar will be given online.

Naama Ben-David, Postdoctoral researcher
VMware Research Group

Please note: This PhD seminar will be given online.

Chang Ge, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Ihab Ilyas

Please note: This seminar will be given online.

Yue Dong
School of Computer Science, McGill University
Mila

Natural language processing (NLP) offers incredible opportunities for automating tasks that involve human languages. However, numerous studies show that instead of learning, modern systems frequently memorize artifacts and biases. Furthermore, the texts produced by such models often contain factual errors.

Please note: This seminar will be given online.

Qizhen Zhang
Department of Computer and Information Science, University of Pennsylvania

Please note: This seminar will be given online.

Shalmali Joshi, Postdoctoral Fellow
Center for Research on Computation and Society, Harvard University

Machine Learning advances have revolutionized many domains such as machine translation, complex game playing, and scientific discovery. On the other hand, ML has only enjoyed modest successes in human-centered applications. To improve the utility, reliability, and robustness of Machine Learning (ML) models in human-centered domains, we need to address several foundational challenges.