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Rachel Lee

Department: Princeton Neuroscience Institute
Faculty Adviser: Nathaniel Daw and Ilana Witten
Year of Study: G5
Undergraduate School: Stanford University
Undergraduate Major: Applied Math

Personal Bio

Hi! I’m a fifth-year graduate student in the Daw and Witten Lab. As an undergraduate at Stanford, I studied Applied Math and started doing Computational Cognitive Neuroscience research in my sophomore year For my undergraduate thesis, I built a deep neural network with back-propagation gradient descent to simulate neural data from several perceptual learning studies. After college, I spent a few years working in industry before returning to research by joining Princeton Neuroscience Institute. I’m also someone who is passionate about being a woman in STEM, especially one who works on computational modeling. I picked up coding in college when I was told I needed to learn how to code to do Computational Neuroscience research and I have spent extensive time helping teach coding courses both as an undergrad and graduate student

Fun Fact

I used to play the carillon in high school and a bit of college. I have yet to make it up the carillon bells at Princeton though!

Research Pitch

My work at Princeton focuses on developing models and frameworks to better understand how dopamine neurons help facilitate reinforcement learning in the brain. The dopaminergic system is fascinating and exciting to me because of its well-studied role in reward-based learning and motivation, and the potential benefit of this research for cognitive disorders such as addiction and memory impairment and movement disorders such as Parkinson’s.


I would describe my work as studying reinforcement learning and dopamine through the lens of the two mentors that I am co-advised by. With the Daw lab, I build computational models that can further the theoretical understanding of the role of dopamine neurons. With the Witten lab, I work closely with researchers to analyze recording of the dopaminergic system to test those theories.



Day to day I build computational models and analyze neural data to link together insights and results from my models to better understand neural data. What excites me most is the interdisciplinary nature of my work, and how I get to navigate between Neuroscience, Psychology, and Computational Modeling.

Plans for Summer 2022

Confirmed pairing; no longer looking for a mentee.

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