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!
PhD in Applied Mathematics
Cornell University
BS in Computer Science and Mathematics
University at Buffalo
I’ll introduce a two-stage rollout design to mitigate the high variance from extrapolation in interpolation-based estimators for the total treatment effect under unknown network interference. Then, I’ll quantify the bias and variance of this estimator as a function of a clustering used in the two-stage design and illustrate their trade-off through a series of experiments.
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. For the setting of cluster randomized designs, I’ll quantify the bias and variance as functions of the selected clustering.
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.
In Fall 2025, I am co-teaching CS 2110: Object-Oriented Programming and Data Structures with Leah Perlmutter.
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.
Finally, I have had positions through the Cornell Math Department’s Active Learning Initiative and the Cornell Computer Science Department to develop course materials.