People

graduate student

I am a family physician and current PhD candidate in neuroscience at Princeton University. I grew up near Toronto and completed my B.Sc. in physics and physiology at McGill University, my M.D. from the University of Toronto and my medical residency in Ottawa. My research now focuses on using modern tools of artificial intelligence, such as deep reinforcement learning, to build models of human cognition. More specifically, asking the question: are there benefits to constructing artificial agents with “multiple selves”, and could those benefits explain why humans experience psychological conflict?

graduate student

Many aspects of cognition, such as learning, planning, and memory, must navigate the trade-off between specificity and generality. For example, optimizing performance for known tasks while remaining capable of flexibly learning new tasks appears to come naturally to humans but has been difficult to model. I am broadly interested in the role that context and abstraction play in shaping this balance, seeking to understand how the current internal and environmental conditions interact with abstract representations of goals and tasks. My research approaches these questions using a combination of computational modeling and the analysis of behavioral experiments, with the ultimate goal of understanding the normative computational principles underlying these behaviors.

[website]

collaborator

I am a theorist interested in problems at the intersection of machine learning, neuroscience and biophysics. I am interested in understanding the principles behind how networks of neurons can form cognitive maps and learn abstract relations.

Currently, I am a C.V. Starr Fellow and a CPBF Fellow at Princeton University, hosted by the Department of Physics and the Princeton Neuroscience Institute. Prior to this, I spent a semester as a Simons-Berkeley Research Fellow participating in The Brain and Computation program at the Simons Institute for Theory of Computing. I completed my graduate studies at the University of Pennsylvania.

graduate student

I am a PhD candidate at the Princeton Neuroscience Institute. Before that, I graduated from Yale University with majors in Computer Science and Statistics & Data Science. During undergrad, I worked with Marvin Chun and Nicholas Turk-Browne (dept of Psychology). 

A core essence of human intelligence is the constant drive to understand the world around them. I am broadly interested in how human brains take experience in the world and extracts abstract knowledge that helps them learn faster. I'm interested in studying this process in a variety of domains such as reinforcement learning, language processing, and visual perception. I utilize a combination of artificial neural network simulations, large-scale online behavior experiments, and brain imaging analysis in my research. 

graduate student

I am a sixth-year Ph.D. student in Electrical Engineering at Princeton University. My research lies at the intersection of machine learning and cognitive neuroscience, taking inspiration form the mechanisms that identify human intelligence and develop neural network models that can demonstrate systematic and sample-efficient generalization in abstract reasoning. I have a background in applied machine learning, and I earned my B.Tech in Electronics and Electrical Communcations Engineering from the Indian Institute of Technology, Kharagpur. 

[website]

postdoc

I'm interested in how humans and other animals achieve their goals through planning and hard work, often from the perspective of (optimal) control theory.

My research uses behavioral experiments (e.g., psychophysics, model-based planning), neuroscience (e.g., fMRI, OP-MEG, iEEG/ephys collaborations), and computational modelling (e.g., evidence accumulation, dynamical systems, inverse optimal control) to triangulate how we control our thoughts and actions.

I completed my MSc at University of Western Ontario with Ingrid Johnsrude and my PhD at Brown University with Amitai Shenhav, Michael J. Frank, and Matthew Nassar (hon).

I am currently a C.V. Starr Postdoctoral Fellow at the Princeton Neuroscience Institute with Jonathan Cohen and Nathaniel Daw.

[website]

graduate student

I am a second-year Ph.D. student in the Department of Electrical and Computer Engineering at Princeton University. With a background in machine learning, I have done some research in medical image analysis and diffusion model-based generative modeling since my master's studies at Tsinghua University. After I joined the group, I have been studying how the context is induced in event segmentation tasks. Recently, I am broadly interested in using mechanistic interpretability to understand the emergence of cognitive abilities in Large Language Models.