I am a second-year PhD student in computer science at the University of Pennsylvania. I am fortunate to be advised by Michael Kearns, Aaron Roth, and Duncan Watts. I am broadly interested in the intersection of Machine Learning Theory, Algorithmic Game Theory, and Reinforcement Learning/Control (especially their practical and social applications). Prior to this, I graduated from Stanford University with a B.A.S. in Mathematics and Economics (Honors). During my time there, I was lucky to work with and be advised by Matthew Jackson, Itai Ashlagi, and Alain Schläpfer. I also spent a year working in the Dynamic Stochastic General Equilibrium (DSGE) Team at the New York Federal Reserve.

Publications

(note: in my field, authorship is primarily in alphabetical ordering)

• Oracle-Efficient Adversarial Reinforcement Learning via Max-Following. Joint work with Z. Mhammedi and T. Marinov. ICML: EXAIT 2025. Manuscript coming soon.
• Replicable Reinforcement Learning with Linear Function Approximation. Joint work with E. Eaton, M. Hussing, M. Kearns, A. Roth, and J. Sorrell ($\alpha-\beta$). Manuscript coming soon.
• Boss LLM: Adaptation via No-Regret Learning. Joint work with Y. Feng, A. Khare, and N. Nguyen ($\alpha-\beta$). ICLR: SSI-FM 2025, Manuscript.
• Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces. Joint work with E. Eaton, M. Hussing, M. Kearns, A. Roth, and J. Sorrell ($\alpha-\beta$). JHU DSAI Symposium on Human-AI Alignment 2025, ICML: Main 2025.
• Oracle-Efficient Reinforcement Learning for Max Value Ensembles. Joint work with M. Hussing, M. Kearns, A. Roth, and J. Sorrell ($\alpha-\beta$). ICML: ARLET, WIML, NeurIPS: Main 2024.
• Estimating HANK for central banks. Joint work with S. Acharya, W. Chen, M. Del Negro, K. Dogra, A. Gleich, S. Goyal, E. Maitlin, D. Lee, and R. Sarfati ($\alpha-\beta$). FRB of New York Staff Report.

Talks

• Oracle-Efficient Reinforcement Learning for Max Value Ensembles; RL Theory Seminar.
• Oracle-Efficient Reinforcement Learning for Max Value Ensembles; Google Research Learning Theory Group NYC.
• Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces; Johns Hopkins University Theory Seminar.

Research/Experience

• Amazon Robotics Research Scientist Intern (2025)
• Cooperative AI Summer School (2025)
• Women in Theory (2025)
• Advised by Matthew Jackson for my Honors Thesis, I studied the role of homophily in the malleability of social networks using techniques drawn from Mean-Field Game Theory here
• Worked on online estimation of Heterogeneous Agent New Keynesian Models (HANK) using Sequential Monte Carlo at the NY Federal Reserve. I also briefly worked on speeding up Hamiltonian Monte Carlo (HMC) to estimate medium-scale DSGE models.
• Developed an algorithm with Itai Ashlagi that matches students in the San Francisco area with schools based upon a generalized version of the Probabilistic Serial Dictatorship mechanism with distributional constraints
• Collaborating with Alain Schläpfer on Reinforcement Learning and Imitation Learning for Optimal Stopping Problems here
• Civic Digital Fellow in Research & Statistics through Coding it Forward where I worked on Imputation of Revenue for the County Business Patterns dataset for the U.S. Census Bureau

Mentorship/Involvement

• Women in Machine Learning Volunteer
• Computer and Information Science Student Seminar Co-Founder
• Computer and Information Science Theory Seminar Volunteer
• CS UPenn PhD Mentorship Program Volunteer
• Stanford Women in Math Mentoring Member
• Stanford Undergraduate Mathematics Organization Member

Teaching

• TA for Algorithmic Game Theory (Spring 2025)
• Mathematics and Economics Tutor through the Stanford Center for Teaching and Learning

Awards/Honors

• NSF GRFP Fellow
• Phi Beta Kappa
• Graduated with Distinction (Top 15% of Undergraduate Body)
• Coleman Sellers Award

Hobbies

Outside of research, I love to play chess, juggle, and watch films!