About Me

I am a lecturer in Cornell’s Computer Science, where I teach large undergraduate courses on discrete mathematics and programming. I have a passion for teaching and curricular development. You can find some materials that I developed for the Cornell Math Department’s Active Learning Initiative and the Cornell Engineering Academic Excellence Workshops linked on this site. I plan to update it soon to include more of my course materials. I completed my PhD in the Cornell Center for Applied Mathematics, where I was advised by Siddhartha Banerjee and Christina Lee Yu. Outside of academia, I am a fan of games and puzzles (in particular, the New York Times crosswords). I also enjoy cooking and baking. Finally, as a Buffalo native, I am a devoted Bills fan!

Interests
  • Causal Inference
  • Online Algorithms
  • Mechanism Design
  • Combinatorics
Education

  • PhD in Applied Mathematics

    Cornell University

  • BS in Computer Science and Mathematics

    University at Buffalo

Research
My research focuses on the development of tools to make decisions with societal implications. This ranges from developing algorithms for online team formation, finding ways to fairly distribute goods in settings such as public health and education where the normative allocation criteria are often at odds, and using statistical tools from causal inference to estimate the effectiveness of an intervention that propogates through a social interference network. I seek to understand the combinatorial structure inherent in all of these problems so that I can leverage it to design better algorithms and estimators.
Publications
Talks and Posters

Causal Inference under Low-Order Interference

Interference effects, where the treatment of one individual has an effect on the outcome of another, are pervasive in real-world settings but violate assumptions of many classical causal estimators. While the Horvitz-Thompson estimator can account for interference, it has prohibitively high variance. In this talk, we’ll survey recent approaches to improve on this variance guarantee by imposing additional structural assumptions on the potential outcomes model or the interference network. Then, I’ll introduce a class of estimators, pseudoinverse estimators, that can be adapted to any experimental design and have strong bias and variance guarantees. Finally, I’ll show how theoretical bounds on the performance of the pseudoinverse estimator can provide practical advice when selecting an experimental design.

Low-Degree Outcomes and Clustered Designs: A Combined Approach for Causal Inference Under Interference

Recent work on causal inference under interference falls under two approaches, using structural assumptions on the interference effects to select a good randomized design or using structural assumptions on the potential outcomes to select a good estimator. In this work, we quantify the gains that can be made when these approaches are considered together, in particular by studying pseudoinverse estimators under cluster randomized designs.

See all events
Teaching Experience

In Spring 2025, I am co-teaching CS 2110: Object-Oriented Programming and Data Structures with Curran Muhlberger.

In Fall 2024, I taught CS 2800, now called Mathematical Foundations of Computing.

In Fall 2023, I co-taught CS 2800, previously called Discrete Structures with Noah Stephens-Davidowitz.

In Summer 2023, I taught ENGRI 1101: Engineering Applications of Operations Research as part of Cornell’s Pre-Collegiate Summer Scholars Program.

In Fall 2022, I co-taught CS 2800 with Alexandra Silva.


In addition, I have been a teaching assistant for the following courses:

Cornell University

  • CS 2111: Programming Practicum, Java (Spring 2022)
  • CS 4820: Introduction to Analysis of Algorithms (Fall 2021)
  • MATH 1106: Modeling with Calculus for the Life Sciences (Spring 2020, 2021)

University at Buffalo

  • CSE 250: Data Structures in C++ (Spring 2017, 2018)
  • CSE 191: Discrete Mathematics (Fall 2017)
  • MTH 241: Multivariable Calculus (Spring 2017)
  • MTH 141: Introductory Calculus (Fall 2016)

Finally, I have had positions through the Cornell Math Department’s Active Learning Initiative and the Cornell Computer Science Department to develop course materials.