CV

Education

Massachusetts Institute of Technology, Cambridge, MA (2022–2026)
B.S. in Mathematics, B.S. in AI & Decision Making, Minor in History. GPA: 5.0/5.0.
Selected coursework: Measure-theoretic probability, stochastic calculus, algorithmic statistics, information theory, probabilistic methods in combinatorics, nonlinear optimization, analysis on manifolds, minimal surface theory, PDE, algebraic topology, natural language processing.
Programming: Python, C++, Julia, SQL, React.

Publications and preprints

• Shotgun Assembly of Random Regular Graphs (arXiv, 2025)
  Brice Huang, Elchanan Mossel, Nike Sun, Claire Zhang, LZ (alphabetical).
• Least-Squares Multi-Step Koopman Operator Learning for Model Predictive Control (arXiv, 2026)
  Liang Wu, Wallace Gian Yion Tan, LZ, Richard D. Braatz, Jan Drgona.
• Rates of Estimation for Semi-supervised Learning with Latent Space Models (2026)
  In submission; manuscript available upon request.
  Guy Bresler, Alina Harbuzova, LZ (alphabetical).

Teaching and service

• Spring 2026 — Teaching Assistant, CC.1803 Differential Equations
• Fall 2025 — Teaching Assistant, 18.604 Seminar in Probability
• Fall 2025 — Presenter & Participant, Online Convex Optimization reading group
• Spring 2025 — Teaching Assistant, CC.1802 Multivariable Calculus
• 2023–Present — Associate Advisor to first-year undergraduates at MIT

Honors and awards

• 2024 — MIT Math Summer Program in Undergraduate Research (department funded)
• 2023 — MIT History Undergraduate Writing Prize (top 3%, only freshman winner)

Selected class projects

• Fall 2025 (NLP) — Measuring LLM re-alignment through iterative fine-tuning
• Fall 2025 (Algorithmic Statistics) — Sample complexity of (multi-step) Koopman operator learning for model predictive control
• Spring 2025 (Seminar in Probability) — Expository paper on recurrence of planar graph local limits
• Spring 2024 (Discrete Probability) — Expository paper on the expander property of random geometric graphs