Tianmin Shu

Tianmin Shu


Dr. Tianmin Shu is a postdoctoral associate in the Department of Brain and Cognitive Sciences at Massachusetts Institute of Technology, working with Joshua Tenenbaum and Antonio Torralba. He studies social AI and computational social cognition, with the goal of building socially intelligent agents that can understand and interact with humans. His work has received the 2017 Cognitive Science Society Computational Modeling Prize in Perception/Action, a Best Paper Award at the Cooperative AI workshop at NeurIPS 2020, and a Best Paper Award at the Shared Visual Representations in Human and Machine Intelligence workshop at NeurIPS 2020. He received his Ph.D. degree from University of California, Los Angeles in 2019.

Benchmarking Machine Social Intelligence

No other species on Earth can play a symphony, form systems of government, and debate scientific issues with one another, quite like humans can. Social intelligence — including our capacities to understand each other’s minds and actions, and to collaborate and compete with each other — has played a key role in our progress as intelligent beings. Despite recent interests in building socially intelligent agents, there has been little work on developing benchmarks that systematically and rigorously evaluate the social intelligence of machine agents. In this talk, I will introduce two social AI challenges that we recently developed, each of which proposes a set of cognitively inspired tasks evaluating different aspects of machine social intelligence.