academics
awards & coursework
school
> graduated summa cum laude from Princeton University with a degree in Computer Science.
- Phillip Goldman '86 Senior Prize in Computer Science: The CS department's top honor for academic excellence. [link]
- Lore von Jaskowsky Memorial Prize: Princeton Engineering's top prize for undergraduate research. [link]
- Accenture Prize in Computer Science: Recognizing academic excellence through the end of junior year. [link]
- Outstanding Senior Thesis Prize and Exemplary Independent Work Prize: For work on convex learning of linear dynamical systems.
- Computer Science Service Award: For leading Princeton ACM, Princeton's largest undergraduate CS organization.
> Early inductee to Phi Beta Kappa and Tau Beta Pi; member of Sigma Xi.
computer science
| cos217 | Introduction to Programming Systems taught by Christopher Moretti | ||
| cos226 | Algorithms and Data Structures taught by Gillat Kol | ||
| cos326 | Functional Programming taught by David Walker | ||
| cos418 | Distributed Systems taught by Michael Freedman and Wyatt Lloyd | ||
| cos435 | Introduction to Reinforcement Learning taught by Benjamin Eysenbach | ||
| ece434 | Theoretical Machine Learning taught by Chi Jin | ||
| ece435 | Machine Learning and Pattern Recognition taught by Peter Ramadge and Hossein Valavi | ||
| cos445 | Economics and Computing taught by Mark Braverman, Marcel Dall'Agnol, and Matt Weinberg | ||
| cos484 | Natural Language Processing taught by Danqi Chen, Tri Dao, and Vikram Ramaswamy | ||
| cos487 | Theory of Computation taught by Gillat Kol | ||
| cos521 | Advanced Algorithm Design taught by Huacheng Yu | ||
| cos522 | Computational Complexity taught by Gillat Kol | ||
| orf543 | Deep Learning Theory taught by Boris Hanin | ||
| cos585 | Information Theory and Applications taught by Ran Raz | ||
| cos597a | Long Term Memory in AI: Vector Search and Databases taught by Edo Liberty and Matthijs Douze | ||
| cos597d | Systems in Machine Learning taught by Kai Li | ||
| cos597r | Inference in Action: Probabilistic Topics in Reinforcement Learning taught by Benjamin Eysenbach | ||
| cos598b | Formal Methods with-and-for Machine Learning taught by Aarti Gupta | ||
| cos598c | Theory of Natural Algorithms taught by Bernard Chazelle |
mathematics
| mat216 | Multivariable Analysis and Linear Algebra I taught by Alan Chang | |
| mat218 | Multivariable Analysis and Linear Algebra II taught by Alan Chang | |
| mat377 | Combinatorial Mathematics taught by Matija Bucic | |
| mat385 | Probability Theory taught by Dmitry Krachun | |
| mat478 | Topics in Combinatorics: The Probabilistic Method taught by Noga Alon |