R. Teal Witter

370 Jay St. Brooklyn, NY • rtealwitter [at] nyu.edu • CVGithubGoogle Scholar

I am a PhD candidate at NYU Tandon where I am fortunate to be advised by Lisa Hellerstein and Chris Musco. My work is generously supported by an NSF Graduate Research Fellowship.

I design and analyze algorithms, leveraging ideas from theoretical computer science and machine learning. My recent research has focused on randomized algorithms for problems with social impact.

I received my undergraduate degrees in Mathematics and Computer Science from Middlebury College. At Middlebury, I designed and analyzed quantum algorithms with Shelby Kimmel and applied math to 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


In the tradition of theoretical computer science, an asterisk (*) indicates that authors are listed in alphabetical order.

Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf Values

Yurong Liu*, R. Teal Witter*, Flip Korn, Tarfah Alrashed, Dimitris Paparas, Juliana Freire

Preprint

Provably Accurate Shapley Value Estimation via Leverage Score Sampling

Christopher Musco*, R. Teal Witter*

Preprint

Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm

R. Teal Witter, Christopher Musco

Conference on Neural Information Processing Systems (NeurIPS 2024)

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 wrote lecture notes to accompany Chris Musco’s graduated algorithmic machine learning and data science class. I used a subset of these notes for my own randomized algorithms for data science class.

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 my deep learning class.

I curated code-based demos that accompany Chinmay Hegde’s graduate deep learning class and my own undergraduate deep learning class. Recordings of the demos are available here.

After struggling for years, I compiled a how-to guide for NYU’s high performance computing cluster.