My education

I graduated from Stanford with a M.S. in Computer Science (AI track) and B.S. in mathematics with distinction (CS theory track). During my undergrad, I won a Sterling award, for having one of the top 25 academic records in the School of Humanities and Sciences.

I worked as a graduate teaching assistant for CS 229: Machine Learning, and for the complexity theory track.

Undergrad GPA: 4.13 — Grad GPA: 4.10

My resume is linked here.

The above is all you need to read. But I think others sometimes find it useful to know classes I’ve taken and my grades—(particularly students starting/planning their Stanford education)—so the core is below. With one or two exceptions, I really enjoyed the classes listed and would take them again. Do get in touch if you have a particular class you’re interested in.

Graduate CS:

  • 144: Networking (A)

  • 149: Parallel Computing (A)

  • 163: Practice of Theory Research (A)

  • 166: Data Structures (A+)

  • 168: Modern Algorithmic Toolbox (S)*

  • 199: CS Research (3 A’s)

  • 221: AI: Principles and Techniques (A)

  • 224W: Machine Learning with Graphs (A+)

  • 228: Probabilistic Graphical Models (A)

  • 229: Machine Learning (A+)

  • 234: Reinforcement Learning (A+)

  • 254: Complexity Theory (A+)

  • 254B: Complexity Theory II (S)*

  • 261: Optimisation and Algorithmic Paradigms (A)

  • 330: Deep multi-task and meta-learning (A)

  • 359A: Research Seminar in Complexity Theory (A)

  • 369Z: Dynamic Data Structures for Large Graphs (S)*

  • Stats 305A: PhD-level applied statistics (A+)

Math:

  • 51-53: Multi-variable calculus sequence (Average: A+)

  • 114: Scientific Computing (A+)

  • 120: Groups and Rings (Honors) (A)

  • 122: Modules and representation theory (S)*

  • 136: Stochastic Processes (Theory) (A) — AKA: Stats 219

  • 143: Differential Geometry (A)

  • 151: Theory of Probability (A+)

  • 154: Algebraic Number Theory (A+)

  • 159: Discrete Probabilistic Methods (A+)

  • 171: Real Analysis (Honors) (A)

  • 175: Functional Analysis (A)

  • 230A: Graduate Theory of Probability (A)

  • 233B: Matroid Theory (Taken for credit)

  • 233C: Probabilistic Combinatorics (S)*

*All courses with (S) were taken when letter grades were not offered, usually during COVID-19.

The data structure we made for CS 166: it’s called a “Relaxed Margin-Maximising Hyperplane” tree. It deals well with low intrinsic dimensionality, and outperformed basically every other K-D Tree variant for nearest neighbour search.

The data structure we made for CS 166: it’s called a “Relaxed Margin-Maximising Hyperplane” tree. It deals well with low intrinsic dimensionality, and outperformed basically every other K-D Tree variant for nearest neighbour search.