Summary
Jordan Lei is a Neuroscience PhD candidate at NYU who builds deep learning and reinforcement learning models to explain planning, attention, and complex reasoning in humans and animals. With nine years of experience spanning computational neuroscience, quant finance internships, and data-science roles, Jordan bridges rigorous theory and production-ready tools—having deployed online experiments via a full-stack JavaScript platform and shipped models that predict eye movements and neural correlates in primates. Their work combines biological plausibility (recurrent and encoder-decoder architectures) with practical impact in experimental design, showing, for example, how uncertainty modulates planning depth. Jordan’s research has been presented at COSYNE, SfN, and CogSci and includes cross-disciplinary collaborations that link cognitive modeling to real-world decision systems.
9 years of coding experience
2 years of employment as a software developer
Bachelor of Science in Economics, Operations, Information, and Decision Making, 3.90, Bachelor of Science in Economics, Operations, Information, and Decision Making, 3.90 at The Wharton School
4.7, 4.7 at Westview High School
Doctor of Philosophy - PhD, Neuroscience, 3.9, Doctor of Philosophy - PhD, Neuroscience, 3.9 at New York University
Master of Engineering - MEng, Computer Science, 4.0, Master of Engineering - MEng, Computer Science, 4.0 at University of Pennsylvania
English, Chinese