Tianmin Shu
I am a postdoc at MIT advised by Josh Tenenbaum and Antonio Torralba. My research goal is to engineer and reverse engineer social intelligence. I develop (1) cognitively inspired machine learning and AI methodologies for building socially inteligent systems, and (2) principled computational models that help uncover the cognitive mechanisms underlying human social intelligence. I received my Ph.D. in Statistics from University of California, Los Angeles under the supervision of Song-Chun Zhu, and have interned at Facebook AI Research and Salesforce Research.
Email: tshu [at] mit.edu
News
03/2023: Invited talk in the Social Cognitive Neuroscience Lab at the University of Iowa.
12/2022: Invited talk in the Computational Cognition, Vision, and Learning Group at Johns Hopkins University.
10/2022: Invited talk at Robotics Seminar, University of New Hampshire.
10/2022: Co-organized ECCV Workshop on Machine Visual Common Sense.
07/2022: Invited talk at Columbia University, Johns Hopkins University, and University of Maryland.
07/2022: Co-organized RSS Workshop on Social Intelligence in Humans and Robots.
01/2022: Invited talk in VITA Lab at EPFL.
11/2021: Our work on machine social common sense was covered by ScienceNews for Students and VentureBeat; MIT News also covered our work on social modeling.
10/2021: Invited talk at AI Seminar, USC ISI.
06/2021: Co-organized ICRA Workshop on Social Intelligence in Humans and Robots.
05/2021: Co-organized ICLR 2021 Social "Social AI Virtual Gathering."
02/2021: Invited talk at Sony CSL, Paris.
11/2020: Invited talk at Virutal Computational Neuroscience (VCN) Journal Club hosted by Stanford, MIT/Harvard, and Princeton.
