About me
Hello! I'm a junior at Princeton studying mathematics, with a particular interest in statistics and optimization. Previously, I worked with the Princeton Astrophysical Data Laboratory and NASA-JPL's Europa team to apply machine learning and numerical analysis to some pretty exciting problems.
In my free time, I raise a bunny named Hippo, read sci-fi/futurism books, flail around in ping-pong, and think hard about big questions. My Mandarin name is 林鸿彬 (Línhóngbīn).
Core Interests
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Abstract Mathematics
Random structures, optimization theory, and reinforcement learning.
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Computational Physics
Modern numerical techniques to advance fundamental research.
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Quantitative Finance
Tournaments Officer at Princeton Quantitative Traders.
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Global Affairs
Former Tour Director for the Princeton Debate Panel.
Previously at...
Selected Coursework
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High-Dimensional Prob.
MAT 529 - Spring 2026
Nonasymptotic methods for the study of random structures in high dimension. Concentration of measure; functional, transportation cost, martingale inequalities; isoperimetry; Markov semigroups, mixing itmes, random fields; hypercontractivity; thresholds and influences; Stein's method; suprema of random processes; Gaussian and Rademacher inequalities; generic chaining; entropy and combinatorial dimensions.
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Functional Analysis
MAT 520 — Fall 2025
The theory of linear operators on infinite-dimensional vector spaces. Topics included convexity, duality, Banach algebras, Hilbert spaces, holomorphic functional calculus, C-star algebras, and the spectral theorem for bounded and unbounded operators.
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Online Optimization
COS 511 - Spring 2026
The online convex optimization framework. Applications to learning with partial observability, reinforcement learning theory, and learning in dynamical systems.
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Advanced Optimization
ORF 522 — Fall 2024
Theoretical analysis via monotone operators. ADMM, proximal operators, acceleration schemes, branch-and-bound, robust optimization, and data-driven optimization.
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Reinforcement Learning
COS435 — Spring 2026; ECE 524 — Fall 2025
DQN, policy gradient methods, actor-critic approaches, and post-training techniques (COS435). Markov Decision Processes, sample complexities of the generative model, linear Bellman completeness and realizability, and Markov games (ECE524).
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Machine Learning Theory
ECE 435 — Fall 2024
A dense mathematical treatment of learning theory. Clustering metrics, dimensionality reduction, adaptive optimizers, neural network architectures, approximation theory, generalization theory, and reinforcement learning. Implemented in Tensorflow.
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Theory of Algorithms
COS 423 — Spring 2024
Rigorous analysis of algorithms with emphasis on Tarjan's work. Proved amortized bounds on modifications of classical and modern data structures. Paper topics included hollow heaps, zip trees, and rank-balanced trees.
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Algorithmic Game Theory
COS 445 — Spring 2025
Systematic approach to game-theoretic problems. Generalized matching, voting, auctions, scoring rules, anarchy, behavioral game theory, and time-inconsistent planning.
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Operating Systems
COS 417 — Spring 2025
The essentials of OS — processes, threads, synchronization primitives, lock-based concurrency, event-based concurrency, virtual memory, file systems, and distributed systems. Implemented in xv6 (ANSI C and RISC-V assembly).
