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.

Education

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

Teaching

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)

Papers

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)


Backgammon is Hard

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)

More Writing

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.