My research examines the cognitive, motivational, and social processes that underpin human problem solving. I am especially interested in questions surrounding the structure and interpretation of intentional action, such as: How do people organize their thoughts and actions when pursuing individual or shared goals? How do people interpret and influence the intentional behavior of others using theory of mind? How do complex social phenomena emerge from the interplay of individual intentions? I am also interested in how approaches from computational cognitive science can inform the development of intelligent machines that interact with people.
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My research interests lie in human behavior in complex tasks, specifically where humans need to process large amounts of information in limited time, to make decisions. I primarily rely on statistical and computational modeling to identify how individuals filter and integrate complex information and adapt to dynamic task contexts. I am particularly focused on skill acquisition and expert strategies for optimal behavior in complex paradigms. I completed my PhD in Cognitive Science from Rensselaer Polytechnic Institute under my advisors Dr. Wayne Gray and Dr. Chris Sims, studying team coordination in complex tasks. As postdoctoral associate at the Computation and Decision-Making Lab, I will be conducting research on optimal strategies for teaching people how to drive.
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I'm a developmental cognitive scientist studying how children learn about social categories. How do children form beliefs about social groups? How do children think about social structures? I'm currently a postdoctoral fellow in psychology advised by Marjorie Rhodes. I received my PhD in psychology from Stanford University, advised by Ellen Markman, and my BA in psychology with a minor in philosophy from the University of Chicago.
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In my PhD, I am excited to explore decision-theoretic and program synthesis models of human cognition and behavior. For instance, how do we represent the tasks we want to accomplish, and when do different representations elicit different attitudes and behaviors? How do we decide what representations to prioritize and communicate? In the past, I have worked as a lab manager in the Computational Cognitive Science & Concepts and Cognition Labs at Princeton, earned my B.A. in Cognitive Science from UC Berkeley, and worked as a computer science educator at the Lawrence Hall of Science.

I am a Ph.D. candidate at the Icahn School of Medicine at Mount Sinai, advised by Angela Radulescu. Motivated by a broad interest in how self-efficacy beliefs arise, update, and steer behavior, I formalize self-efficacy within reinforcement-learning frameworks and test these models in both computational agents and human participants. My work bridges reinforcement learning and computational psychiatry to understand how fluctuations in self-efficacy shape decision making and contribute to manic risk in bipolar disorder. I build self-efficacy models in RL, design behavioral tasks, and ultimately aim to translate these insights into digital diagnostics and adaptive interventions for mood disorders.

Ryan is a PhD student in the Cognition & Perception program, advised by Cate Hartley. At the Ho Lab, Ryan is working on goal-directed exploration, task decomposition, and abstraction for reinforcement learning. Broadly, Ryan is interested in modeling the cognitive algorithms underlying open-ended autotelic decision-making in animals, and building similarly capable artificial agents.

Jeroen Olieslagers is a PhD candidate in the Center for Neural Science at NYU, advised by Wei Ji Ma. He is broadly interested in how people solve problems. Currently, he is investigating the mechanisms of planning in complex problem solving using the games of Rush Hour and four-in-a-row. Before NYU, Jeroen got his MEng and BA in Computer and Information Engineering from the University of Cambridge.

Hello! I'm Sevan Harootonian, but I go by Sev. I'm interested in studying mentorship and understanding the cognitive mechanisms that contribute to its success. One project I've been working on examines how people infer others' knowledge to determine the best way to teach them. I use Reinforcement Learning and Bayesian models to figure out what mental strategies people apply in teaching situations. Teaching is just one aspect of mentorship, and I'm also interested in other important factors, such as inspiration and motivation.

I am an undergraduate at Barnard College double majoring in mathematics and cognitive science. My current work focuses on what individual computational processes enable flexible specialization at the group level during joint problem-solving. I am also broadly interested in mathematical reasoning, including how people perceive “affordances” in abstract mathematical objects and effectively acquire these representations from others.

Hi! I’m Divya, graduate student majoring in Computer Science at NYU. I’m interested in language and decision making in humans and machines. I’m fascinated by how humans update their beliefs by combining prior knowledge with new information, and the condition under which this breaks down. I am also interested in learner modeling and diagnostic inference, particularly how observers reason about the underlying cognitive processes that produced a given behavior. Outside the lab, I love crocheting and hiking!