Ryan Goldade, PhD candidate
David R. Cheriton School of Computer Science
Barrett Ens, Department of Human Centred Computing
Monash University, Australia
Ke Li, Research Scientist
Google
In this talk, I will present our work on overcoming two long-standing problems in machine learning and computer vision:
Abel Molina, PhD candidate
David R. Cheriton School of Computer Science
Mark Elendt and Jeff Lait are two Academy Award winning developers at SideFX who have a long history in the industry. They will be discussing the unique challenges involved in developing 3D tools for artists and what it’s like to be a software developer in this very exciting industry.
Mohammad Hajiabadi, Postdoctoral Researcher
Computer Science Division, EECS Department
Renjie Liao, Department of Computer Science
University of Toronto
Kate Larson
David R. Cheriton School of Computer Science
Axiomatic approaches are an appealing method for designing fair algorithms, as they provide a formal structure for reasoning about and rationalizing individual decisions. However, to make these algorithms useful in practice, their axioms must appropriately capture social norms.
We explore this tension between fairness axioms and socially acceptable decisions in the context of cooperative game theory for the fair division of rewards.
Masoumeh Shafieinejad, PhD candidate
David R. Cheriton School of Computer Science
Carl Yang, Department of Computer Science
University of Illinois at Urbana–Champaign