Neuroscience | Machine Learning | Art


Hey there! I'm Jordan Lei. I'm a PhD Student in Neuroscience at New York University.
I consider myself an inquisitive person - I love figuring out what makes things tick, particularly when it comes to understanding human intelligence and the brain. I'm especially passionate about the intersection of Neuroscience and Deep Learning. Before the singularity, you'll find me painting, playing the guitar, or snuggling up with a good book.


New York University
PhD Program in Neuroscience
GPA: 3.85
Rotation Advisors: Wei Ji Ma, Eero Simoncelli, Robert Froemke

University of Pennsylvania
Master's Program in Computer Science
GPA: 4.0, Summa Cum Laude, Class of 2021
MSE: Computer Science, School of Engineering and Applied Sciences
Thesis: “Object-Based Attention Through Internal Gating”
Advisor: Konrad Kording

University of Pennsylvania
Jerome Fisher Program in Management and Technology
GPA: 3.88, Summa Cum Laude, Class of 2020
BS in Economics: Operations, Informations, and Decisions, The Wharton School
BSE: Computer Science, School of Engineering and Applied Sciences
Dean's List 2016-17, 2017-18, 2018-19

Westview High School
GPA: 4.7, Class of 2016
Valedictorian, Class Rank 1st of 603
National Merit Scholar Finalist, Presidential Scholar Semifinalist




TA, Mathematical Tools for Neuroscience @ NYU
Fall 2022
Designed curricula and taught linear algebra, coding, and mathematical concepts to graduate students.

PhD Rotations
2021 - 2022
Rotation Labs: Froemke Lab, Simoncelli Lab, Ma Lab


Master's Thesis, Kording Lab @ Penn
Project: Biologically-inspired algorithms for visual object based attention using Deep Learning. Used recurrence, encoder-decoder architectures, and targeted loss functions to model objectbased attention for classification tasks.

Lead TA, Penn Deep Learning Academy
Spring 2021
Led TA training and content creation efforts for the Penn Deep Learning Academy (CIS 522), an open-source in Deep Learning taught by Konrad Kording and Lyle Ungar in an inverted-classroom format. Organized enrollment, grading, and feedback infrastructure. Special topics: Deep Learning Ethics.


MindCORE Summer Research Fellow, Gold Lab @ Penn
Summer 2020 - 2021
Project: Assess limitations of Reward Modulated Hebbian Learning relative to Deep Reinforcement Learning (DRL) in auditory discrimination tasks. Research funded by Lila R. Gleitman MindCORE Summer Research Fellowship. Preliminary results and figures submitted as part of a major grant proposal; presented at 2020 Neuromatch Conference

TA, Deep Learning @ Penn
Spring 2020
Prepared course material, developed and graded homework assignments, held recitations and weekly office hours, led project meetings. Special topics: Neural Network Debugging (HW), GANs (recitation, slides), NLP/Transformers (slides)


Finance Intern, Unilever
May-August 2019
Developed RateDash, a functional dashboard for automated rate validation for the monthly rolling forecast as part of Sales and Operations Planning. Reduced rate validation end-to-end time by 90 percent.

TA, Machine Learning @ Penn
Spring 2019, Fall 2019
Prepared course material, including slides and in-class activities, graded exams and homework. Special topics: Decision Trees, Perceptrons, Bayesian Inference, AI and Data ethics


Let's get in touch!
Email: jordanlei dot work at gmail dot com