Tianmin Shu

Tianmin Shu

NEW I will join the Department of Computer Science at Johns Hopkins University as an Assistant Professor in January 2024. I will also hold a joint appointment with the Deparment of Cognitive Science. My group will focus on Social AI, Human-Robot Interaction, and Computational Social Cognition. I will be recruiting PhD students for Fall 2024. If you are interested in working with me, please feel free to drop me an email. You can learn more about my research below or from my research statement.

I am a Research Scientist at MIT advised by Josh Tenenbaum and Antonio Torralba. My research goal is to advance human-centered AI by engineering machine social intelligence to build socially intelligent systems that can understand, reason about, and interact with humans in real-world settings. I approach this from an interdisciplinary perspective, connecting machine learning, computer vision, robotics, and social cognition to study machine social intelligence.

Email: tshu [at] mit.edu

Google Scholar / Thesis


07/2023: Our work on test-time policy diagnosis & adaptation was covered by MIT News.

07/2023: Invited talk in the Cognitive-AI Benchmarking workshop at CogSci 2023.

07/2023: Co-organized RSS Workshop on Social Intelligence in Humans and Robots.

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.

Reseach Highlights

Social Scene Understanding

Multi-agent Cooperation

Imitation & Human-in-the-Loop Learning

Intuitive Psychology