Meet the Fellow: Mark Ho

NYU Center for Data Science
2 min readJun 29, 2022

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This entree is a part of our Meet the Fellow blog series, which introduces and highlights Faculty Fellows who have recently joined CDS.

Mark Ho, CDS Faculty Fellow

Meet CDS Faculty Fellow Mark Ho who will be joining us this fall. Originally from New York City, Mark currently works as a post-doc in the Computer Science and Psychology departments at Princeton University on developing computational theories of human planning. In 2023, Mark will be taking on a position at Stevens Institute of Technology as an Assistant Professor in the Department of Computer Science.

Prior to his post-doc work, Mark earned his PhD in Cognitive Science and MS in Computer Science from Brown University and also received a BA in Philosophy with a minor in Computer Science from Princeton.

Mark’s research explores how people think and act by combining approaches from psychology, neuroscience, computer science, and philosophy. His work gives insights into human planning and social cognition by developing computational theories using probabilistic models, reinforcement learning, and neural networks.

“I’m excited to be part of NYU CDS because it is a unique, multidisciplinary community of scholars interested in how research on human cognition can inform machine learning and vice versa,” said Ho. “I’m especially looking forward to developing new collaborations that apply insights about human problem solving to topics in language, robotics, and human-machine interaction.”

Mark’s research has been published in Nature; NeurIPS; AAAI; Annual Review of Control, Robotics, and Autonomous Systems; Journal of Experimental Psychology: General; Current Opinion in Behavioral Sciences, among other outlets.

To view all our current faculty fellows, please visit the CDS Faculty Fellow page on our website.

By Meryl Phair

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NYU Center for Data Science
NYU Center for Data Science

Written by NYU Center for Data Science

Official account of the Center for Data Science at NYU, home of the Undergraduate, Master’s, and Ph.D. programs in Data Science.

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