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.
EDUCATION
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.
Master's Thesis, Kording Lab @ Penn 2021
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.
2020
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)
2019
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
