About Me

Welcome! I am interested in how people's goals, values, and motivations structure their thoughts, decisions, and interactions with others.

My research combines approaches from cognitive science, social psychology, neuroscience, and computer science to develop novel computational theories of cognitive and social processes as well as identify design principles for building intelligent systems that interact with humans.

For updates on my research (including papers), check out my lab webpage:
Computation and Decision-Making Lab

My CV can be found here.

Feel free to reach out! My email is mark.ho.cs@gmail.com

Representative Papers

Ho, M. K., Abel, D., Correa, C. G., Littman, M. L., Cohen, J. D., & Griffiths, T. L. (2022). People construct simplified mental representations to plan. Nature.
This paper proposes a theory of value-guided construals: simplified but useful representations that people construct when planning. Also see my post for Nature's Behind the Paper.
[arxiv version]

Ho, M. K., Saxe, R., & Cushman, F. (2022). Planning with Theory of Mind. Trends in Cognitive Sciences.
This paper examines how planning processes shape Theory of Mind representations.

Ho, M. K., & Griffiths, T. L. (2022). Cognitive science as a source of forward and inverse models of human decisions for robotics and control. Annual Review of Control, Robotics, and Autonomous Systems.
A review of computational cognitive science for artificial intelligence, control, and robotics.

Ho, M. K., Cushman, F., Littman, M. L., & Austerweil, J. L. (2021). Communication in action: Planning and interpreting communicative demonstrations. Journal of Experimental Psychology: General.
This paper examines the computations underlying intentionally communicative actions.

Ho, M. K., Abel, D., Griffiths, T. L., & Littman, M. L. (2019). The Value of Abstraction. Current Opinion in Behavioral Sciences.
An accessible review of abstract representations in reinforcement learning.

For a complete list of papers (including links to pdfs and code), please see my lab's website.


Before September 2023, I was a Faculty Fellow / Assistant Professor at NYU's Center for Data Science and a post-doc in the Computer Science and Psychology departments at Princeton University, where I worked with Tom Griffiths and Jonathan Cohen on developing computational theories of human problem solving. Previously, I was affiliated with the Learn and Verify Group in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. I received my Ph.D. in Cognitive Science from Brown University, where I worked with Joseph Austerweil and Fiery Cushman on teaching and social learning. I also received my M.S. in Computer Science working with Michael Littman on interactive machine learning. As an undergraduate at Princeton, I majored in Philosophy and minored in Computer Science, and Gil Harman advised my senior thesis. I'm originally from New York City and currently live there.

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