I am a first-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, Game Theory, and broader Computational Social Science questions. 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.

Research/Experience

• 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 here. 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 Doctoral Association (CISDA) Volunteer
• CS UPenn PhD Mentorship Program Volunteer
• Stanford Women in Math Mentoring Member
• Stanford Undergraduate Mathematics Organization Member
• Mathematics and Economics Tutor through the Stanford Center for Teaching and Learning

Hobbies

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