People

collaborator

I am a former postdoc, now Assistant Professor at Dartmouth College, interested in the computational principles and neural systems that allow the human mind to be so flexible in some cases—for example, language, reasoning, and planning—and so capacity limited in others—for example, short term forms of memory and attention. My recent theoretical work has approached these questions from the perspectives of information-theory and classic neural network formalisms, in pursuit of some general principles.

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.