About Me

I am currently pursuing a Ph.D. in Computer Science at Carnegie Mellon University, advised by Jason Li.

While I am broadly interested in theoretical computer science and combinatorics, I am particularly passionate about graph algorithms and extremal problems in discrete geometry, additive combinatorics, and graph theory.

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I graduated from the University of Michigan in 2023, majoring in mathematics and computer science. During the 2023–2024 academic year I completed Part III of the Mathematical Tripos at the University of Cambridge, passing with Distinction and earning a Master of Advanced Study, funded by a Churchill Scholarship. My Part III Essay was on Sharp Thresholds for Ramsey Properties, supervised by Julian Sahasrabudhe.

During the summer of 2023, I was a graduate mentor at the 2023 DIMACS REU program, working with Karthik C.S. and his students (Ashwin Padaki, Styopa Zharkov, and Kyrylo Karlov) on hardness of approximation of clustering objectives. I also mentored the students in his other project (Guillermo Gamboa, Josef Matějka, and Jakub Petr), constructing optimal Euclidean Steiner trees for the regular simplex.

I did my honors thesis with Greg Bodwin at the University of Michigan, devising sublinear time algorithms for constructing adjacency oracles of sparse spanning subgraphs, $k$-connectivity oracles, and multiplicative distance spanners. We also showed several lower bounds for our model. This work has since been published in ITCS 2024.

During the summer of 2022, I was an REU student at the DIMACS REU program, working with Karthik C.S. and his student Surya Teja Gavva on inapproximability of the Steiner Tree problem. The work resultant from this project has since been published in SODA 2024.

During the 2021 and 2022 academic years, I worked on quantifying the fairness of the redistricting plans drawn in the new redistricting cycle. I began as a student researcher in LoG(M) , advised by Tim Ryan and Samuel Hansen. With LoG(M), I developed a Python script for quantifying redistricting fairness in Michigan and provided feedback to the Michigan Independent Citizens Redistricting Commission on their initial practice maps. Later I became a policy analyst for CLOSUP at the Gerald R. Ford School of Public Policy. As a policy analyst, I worked with Gregory Herschlag and Jonathan Mattingly, processing Michigan geographic shapefiles and election data to apply a modified version of the Multi-scale Merge-split Markov chain redistricting fairness algorithm. Alongside Jon X. Eguia, I also quantified the fairness of every redistricting plan in the 2022 cycle according to simpler measures, co-authoring the IPPSR Partisan Advantage Tracker and issuing an associated policy report.

In the summer of 2021, I participated in the SMALL REU program at Williams College. At SMALL I was part of Steven J. Miller's Number Theory and Probability Theory group, and I worked on four subprojects spanning discrete geometry, random matrix theory, additive combinatorics, and combinatorial game theory. The discrete geometry subproject was co-advised by Eyvindur A. Palsson and Charles Wolf.

During the summer of 2020, I participated (virtually) in the Extremal Graph Theory and Dynamical Systems REU at Rochester Institute of Technology. I studied the existence of efficient $(j,k)$-dominating functions on graphs with Brendan Rooney.