UW–MADISON / COURSEWORK

Courses.

Selected coursework in mathematics, statistics, and computer science. Click a course to read a short description.

(H) Honors(G) Graduate-levelFall 2026 marked in progress

Course constellation.

01 / A MAP OF WHAT I'VE STUDIED

An interactive map of shared ideas across my coursework. Courses are positioned using a spectral embedding of their topic similarities; select a node to explore its connections.

Similarity is based on curated course topics, not official prerequisite relationships. Layout uses a normalized graph Laplacian.

Mathematics & Statistics

MATH 632 / Stochastic Processes(H)

Markov chains, Poisson processes, renewal theory, and stochastic models of evolving systems.

MATH 525 / Linear Optimization

Linear programs, polyhedral geometry, the simplex method, duality, and proof-based optimization.

MATH 521 / Real Analysis I (H)

A rigorous study of limits, continuity, differentiation, integration, and convergence in metric spaces.

STAT 451 / Statistical Machine Learning

Classification, regression, clustering, and dimensionality reduction, with an emphasis on statistical model evaluation.

STAT 436 / Data Visualization

Exploratory visualization of complex data, dimensionality reduction, and interactive statistical graphics.

STAT 431 / Probability Theory

Probability spaces, random variables, conditional expectation, concentration bounds, and limit theorems.

MATH 421 / Theory of Calculus

Proof-based calculus, emphasizing precise arguments for limits, continuity, differentiation, and integration.

MATH 340 / Linear Algebra

Vector spaces, linear maps, eigenvalues, diagonalization, orthogonality, and matrix methods.

STAT 305 / R for Statistics III

Using R with high-performance computing tools for scientific and statistical computation.

STAT 304 / R for Statistics II

Programming in R with functions, loops, matrices, data extraction, and advanced graphics.

STAT 303 / R for Statistics I

Data manipulation, exploratory analysis, visualization, and basic statistical workflows in R.

Computer Science

CS 760 / Machine Learning (G)

Mathematical and algorithmic foundations of supervised learning, model evaluation, and modern learning methods.

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CS 639 / Game Theory & Learning

Nash equilibria, learning dynamics, no-regret algorithms, mechanism design, and algorithmic games.

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CS 577 / Algorithms

Greedy methods, divide and conquer, dynamic programming, reductions, and computational hardness.

CS 566 / Computer Vision in progress

Feature detection, image formation, geometric transformations, motion, and 3D reconstruction.

CS 564 / Database Systems

Relational databases, SQL, indexing, query processing, concurrency, and recovery.

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CS 544 / Big Data Systems

Distributed storage and computation with data pipelines, streaming, Spark, and cloud tools.

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CS 532 / Matrix Methods in ML

Least squares, regularization, singular value decomposition, and linear-algebraic learning methods.

CS 400 / Programming III

Graphs, balanced trees, hashing, algorithm analysis, and larger-scale software development.

CS 354 / Machine Organization

C programming, assembly, memory management, caching, and how software interacts with hardware.

CS 300 / Programming II

Object-oriented programming, fundamental data structures, recursion, and software testing.

CS 252 / Computer Engineering

Boolean logic, combinational and sequential circuits, and introductory computer organization.

CS 240 / Discrete Mathematics

Logic, sets, combinatorics, induction, graph theory, and proofs of program correctness.