David Wajc, Motwani Postdoctoral Fellow in Theoretical Computer Science
Computer Science Department, Stanford University
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.
Qizhen Zhang
Department of Computer and Information Science, University of Pennsylvania
Utsav Das, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Olga Vechtomova
Chang Ge, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ihab Ilyas
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.
Naama Ben-David, Postdoctoral researcher
VMware Research Group
Huaicheng Li, Postdoctoral Researcher
Parallel Data Lab, Carnegie Mellon University
Milind Tambe
Gordon McKay Professor of Computer Science
Director of the Center for Research in Computation and Society, Harvard University
Director, AI for Social Good, Google Research India
Jason Altschuler, Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology