Some of my work includes considering group fairness and biased data FORC 2020, fairness and screening problems FAACT 2022, strategic classification ICML 2023, adversarial robustness (under review) , and fair-ERM with malicious noise (under review).

I am pleased to announce I will be interning at J.P. Morgan AI (NYC) during Summer 2023, working with the excellent Margarita Boyarskaya! I have served as a reviewer for ITCS, FAACT, and ICALP.

Generally speaking, I am most interested in formally modelling problems in order to pin down complex behavior and ensure reliable and beneficial systems. My email is my first name at ttic dot edu.

I studied mathematics in undergrad at UCLA and benefited substantially from the Applied Math REU there.

Publications: Here is my ArXiv and my google scholar.

Talks:

• Sequential Strategic Screening, IDEAL: Workshop on Machine Learning, Interpretability, and Logic, Chicago (USA), April 2023
• Multi Stage Screening: Enforcing Fairness and Efficiency in a Pre-existing Pipeline, TOC 4 Fairness , online, June 2022, (video)
• Fairness in Machine Learning in Two Contexts: Biased Data and Pipelines, UIC Computer Science Theory Seminar , Chicago (USA), March 2022
• Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?, Foundations of Responsible Computing 2020 , online, May 2020, (video) May 2020

• TA: Fall 2019, TTIC 31020 Introduction to Machine Learning
• TA: Winter 2019, TTIC 31010 / CMSC 37000 Algorithms
• TA: Summer 2018, Introduction to Machine Learning Summer School

• TTIC Outstanding TA Award, 2019