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Department:
MATH
Course Number:
8803
Hours - Lecture:
3
Hours - Total Credit:
3
Typical Scheduling:
Every Fall and Spring Semester
The following table contains a list of all graduate special topics courses offered by the School of Math within the last 5 years. More information on courses offered in the current/upcoming semester follows below.
| Semester | Instructor | Title |
|---|---|---|
| Spring 2026 | Hannah Choi | Neuronal Dynamics and Networks |
| Christian Houdré | From Longest Increasing Subsequences in Random Words to Quantum Statistics, II | |
| Thang Le | Introduction to Quantum Topology | |
| Anton Zeitlin | Representation Theory II | |
| Fall 2025 | Matt Baker | Matroid Theory |
| John Etnyre | Manifolds and Handlebodies | |
| Xiaoyu He | Ramsey and Turan Theory | |
| Christian Houdré | From Longest Increasing Subsequences in Random Words to Quantum Statistics | |
| Tobias Ried | Optimal Transport: Theory and Applications | |
| Anton Zeitlin | Representation Theory | |
| Wei Zhu | Mathematical Foundations of Machine Learning | |
| Spring 2025 | Gong Chen | Nonlinear Dispersive Equations |
| Alex Dunn | Analytic Number Theory II | |
| Jen Hom | Knot Concordance and Homology Cobordism | |
| Heinrich Matzinger | AI, Transformers, and Machine Learning Methods: Theory and Applications | |
| Fall 2024 | Alex Blumenthal | Big and Noisy: Ergodic Theory for Stochastic and Infinite-Dimensional Dynamical Systems |
| Mohammad Ghomi | Geometric Inequalities | |
| Michael Lacey | Discrete Harmonic Analysis | |
| Rose McCarty | Structure for Dense Graphs | |
| John McCuan | Mathematical Capillarity | |
| Haomin Zhou | Machine Learning Methods for Numerical PDEs | |
| Spring 2024 | Anton Bernshteyn | Set Theory |
| Greg Blekherman | Convex Geometry | |
| Hannah Choi | Mathematical Neuroscience | |
| Alex Dunn | Analytic Number Theory I | |
| John Etnyre | 3-Dimensional Contact Topology | |
| Chongchun Zeng | Topics in PDE Dynamics II | |
| Fall 2023 | Jen Hom | Knots, 3-Manifolds, and 4-Manifolds |
| Tom Kelly | Absorption Methods for Hypergraph Embeddings and Decompositions | |
| Zhiwu Lin | Topics in PDE Dynamics I | |
| Galyna Livshyts | Concentration of Measure and Convexity | |
| Cheng Mao | Statistical Inference in Networks | |
| Spring 2023 | Igor Belagradek | Diffeomorphism Groups |
| Fall 2022 | Hannah Choi | Neuronal Dynamics and Networks |
| John Etnyre | Topics in Algebraic Topology | |
| Christopher Heil | Measure Theory for Engineers | |
| Fall 2021 | Anton Bernshetyn | Descriptive Combinatorics |
| John Etnyre | The Topology of 3-Manifolds | |
| Christopher Heil | Measure Theory for Engineers | |
| Zhiyu Wang | Spectral Graph Theory |
In the lists below, Math 8803-XXX refers to the special topics course taught by the instructor whose last name begins with XXX.
Prerequisites:
Spring 2026:
Math 8803-CHO: MATH 2552 (Differential Equations) or equivalent*. Familiarity with Python, MATLAB, or other programming languages
Math 8803-HOU: Math 6241 and 6242, and Part I of the course. Some familiarity with Brownian motion, random matrices, and non-parametric statistics will also be assumed.
Math 8803-TLE: Math 6121 and 6441
Math 8803-ZEI: Math 6121 and 6452 recommended, or agreement of the instructor, and part I of the course. This course assumes familiarity with representations of Lie algebras and groups, as covered in a first course on representation theory.
*Note that undergraduate-level prerequisites for graduate courses are not checked by the registration system.
Course Text:
Spring 2026:
Math 8803-CHO: See syllabus
Math 8803-HOU: See syllabus
Math 8803-TLE: See syllabus
Math 8803-ZEI: See syllabus
Topic Outline:
Spring 2026:
Math 8803-CHO: This course covers various topics in neural dynamics at the level of single cell and population, and their connections to biological neural networks.
Math 8803-TLE: Topics include the Jones polynomial and quantum link invariants, Topological Quantum Field Theory (ribbon and modular tensor categories), quantum representations of mapping class groups, and skein modules/algebras.
Math 8803-HOU: The course will cover the following sequence of topics:
Introduction
Weak Invariance Principles
Longest Increasing Subsequences: The One Sequence Case
Maximal Eigenvalues of Matrices from the Gaussian Unitary Ensemble
Young Diagrams and the RSK Algorithm
Convergence of the Shape of RSK Young Diagrams
Spectra of GUE and Related Gaussian Matrices
A Conjecture for Markov Random Words
Two or More Random Words, Longest Common (and Increasing) Subsequences
Strong Invariance Principles and Rates of Convergence
Quantum Tomography
Quantum Estimation
Math 8803-ZEI: In the first part, we will continue studying representations of classical complex Lie algebras, followed by representations of compact Lie groups and the Peter-Weyl theorem. We will explore applications, including an explicit study of the hydrogen atom from a representation-theoretic perspective. The course then covers the structure of Lie groups, including the classification of compact and complex reductive groups, the topology of Lie groups and homogeneous spaces, and the Bruhat decomposition.
The second part is devoted to advanced topics in modern representation theory: representations of affine Lie algebras, elements of vertex algebra theory, and quantum groups. Topics include highest-weight representations of affine Lie algebras, constructions of vertex algebras with applications to conformal field theory, and quantum groups in the context of knot invariants and quantum topology.