370 Jay St. Brooklyn NY 11201 • rtealwitter [at] nyu.edu • CV • Github • Google Scholar
I am a PhD candidate at NYU Tandon where I am fortunate to be advised by Lisa Hellerstein and Chris Musco. My research is generously supported by an NSF Graduate Research Fellowship.
My primary research focuses on how deep learning, discrete optimization, and randomized algorithms can help solve problems with social impact. I am also interested in designing algorithms for quantum computers and how to make these algorithms robust to errors.
I received my undergraduate degrees in Mathematics and Computer Science from Middlebury College. At Middlebury, I designed quantum algorithms for graph theory problems with Shelby Kimmel and worked on applications of math in recreational board games with Alex Lyford.
New York University
PhD in Computer Science • September 2020 - Present
Middlebury College
BA in Mathematics, BA in Computer Science • Summa Cum Laude • February 2017 - May 2020
Middlebury CSCI 1052: Randomized Algorithms for Data Science
Course Instructor (Winter 2024)
Middlebury CSCI 1051: Deep Learning
Course Instructor (Winter 2023)
NYU CS-GY 6953: Deep Learning
Course Assistant (Fall 2022, Spring 2023, Fall 2023)
NYU CS-GY 6763: Algorithmic Machine Learning and Data Science
Course Assistant (Fall 2021, Spring 2022, Fall 2023)
NYU CS-GY 6923: Machine Learning
Course Assistant (Spring 2021, Spring 2023)
Note: As is the tradition in theoretical computer science, authors are ordered alphabetically by last name unless otherwise noted with an asterisk.
I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets Initiatives*
R. Teal Witter, Lucas Rosenblatt
AAAI Conference on Artificial Intelligence (AAAI 2024)
Robust and Space-Efficient Dual Adversary Quantum Query Algorithms
Michael Czekanski, Shelby Kimmel, R. Teal Witter
European Symposium on Algorithms (ESA 2023)
Counterfactual Fairness Is Basically Demographic Parity
Lucas Rosenblatt, R. Teal Witter
AAAI Conference on Artificial Intelligence (AAAI 2023)
A Local Search Algorithm for the Min-Sum Submodular Cover Problem
Lisa Hellerstein, Thomas Lidbetter, R. Teal Witter
International Symposium on Algorithms and Computation (ISAAC 2022)
Adaptivity Gaps for the Stochastic Boolean Function Evaluation Problem
Lisa Hellerstein, Devorah Kletenik, Naifeng Liu, R. Teal Witter
Workshop on Approximation and Online Algorithms (WAOA 2022)
How to Quantify Polarization in Models of Opinion Dynamics
Christopher Musco, Indu Ramesh, Johan Ugander, R. Teal Witter
International Workshop on Mining and Learning with Graphs (MLG 2022)
R. Teal Witter
International Conference on Combinatorial Optimization and Applications (COCOA 2021)
A Query-Efficient Quantum Algorithm for Maximum Matching on General Graphs
Shelby Kimmel, R. Teal Witter
Algorithms and Data Structures Symposium (WADS 2021)
Applications of Graph Theory and Probability in the Board Game Ticket to Ride*
R. Teal Witter, Alex Lyford
International Conference on the Foundations of Digital Games (FDG 2020)
Applications of the Quantum Algorithm for st-Connectivity
Kai DeLorenzo, Shelby Kimmel, R. Teal Witter
Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019)
I developed code-based tutorials on adversarial image attacks, neural style transfer, variational autoencoders, and diffusion for Chris Musco’s graduate machine learning class.
I wrote notes on contrastive learning, stable diffusion, and implicit regularization for the deep learning class I taught at Middlebury.
After struggling for years, I compiled a how-to guide for NYU’s high performance computing cluster.
I curated code-based demonstrations that accompany Chinmay Hegde’s graduate deep learning class at NYU and my own undergraduate deep learning class at Middlebury. Recordings of the demos are available here.