About Me

Hi my name is Lance Ying. I’m a third year PhD student affiliated with MIT Brain and Cognitive Science, CSAIL and Harvard SEAS. I'm advised by Josh Tenenbaum and Sam Gershman. My research interests lie in the intersection between Cognitive Science, AI, and Human-AI interaction. I'm particularly interested in building computational models of social cognition and developing social intelligence in machines.

Before starting my PhD, I completed my Bachelor’s degree from University of Michigan - Ann Arbor with a quadruple major in Computer Science (with honors), Psychology (with honors), Mathematics, and Cognitive Science (with honors). After finishing my Bachelor’s degree, I spent a gap year in Paris completing my Diplôme du Cuisine from Le Cordon Bleu Paris. My long-term personal and academic interests are to build a robot sous-chef for my future restaurant.

I spend my free time cooking, reading and visiting new places. I have so far visited 35 countries.

Research Interests

My research agenda is expansive and covers the following topics. For a full list of publications, please see my Projects page.

Computational models of social cognition:

How do people make sense of the social world and interact with it? How/When/Why do we infer other agents' mental states even if they are not observable? How do we communicate with others about our mental models, verbally or non-verbally?

• Ying, L., Truong, R., Tenenbaum, J., & Gershman, S. J. (2025). Adaptive Social Learning using Theory of Mind. PsyArxiv


• Ying, L.*, Zhi-Xuan, T.*, Wong, L., Mansinghka, V., & Tenenbaum, J. (2024). Understanding epistemic language with a Bayesian Theory of Mind. In Transactions of the Association for Computational Linguistics (To Appear)

Cooperative AI Assistants:

My current research applies rich theories and computational models of social cognition in Human-AI team. I'm particularly interested in building agents with Theory of Mind capabilities that can effectively collaborate with humans in complex multi-agent tasks.

• Zhi-Xuan, T.*, Ying, L.*, Mansinghka, V., & Tenenbaum, J. B. (2024, May). Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning. AAMAS 2024.


• Ying, L., Jha, K., Aarya, S., Tenenbaum, J. B., Torralba, A., & Shu, T. (2024). GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment. IROS 2024.

Building and evaluating general human-like intelligence:

A long term ambitious goal of mine and my collaborators' is to build machines that can make sense of the world and interact with it, learning new skills from little training data, with humans like humans. We approach this problem with a cognitively inspired neuro-symbolic model, which tries to endow LLMs/VLMs with core human-like meta-cognitive representations for perception, reasoning, and planning.

• Ying, L., Collins, K. M., Wong, L., Sucholutsky, I., Liu, R., Weller, A., ... & Tenenbaum, J. B. (2025). On Benchmarking Human-Like Intelligence in Machines. Arxiv


• Ying, L., Collins, K. M., Wei, M., Zhang, C. E., Zhi-Xuan, T., Weller, A., ... & Wong, L. (2023). The neuro-symbolic inverse planning engine (nipe): Modeling probabilistic social inferences from linguistic inputs. 2023 ICML ToM Workshop