Courses

Teaching

I teach graduate courses on computational mathematics in the Caltech CMS Department. Recent offerings include probability theory (CMS 117), probability in high dimensions (ACM 217), matrix analysis (ACM 204), randomized matrix computations (ACM 206), and convex geometry (ACM 204). Lecture notes for some of these courses are available.

AY 2023

• Fall 2023. CMS 117: Probability Theory & Computational Mathematics
• Winter 2024. ACM 217: Random Matrix Theory
• Spring 2024. ACM 217: Free Probability (joint with Dr. Jorge Garza-Vargas)

AY 2022

• Fall 2022. CMS 117: Probability Theory & Computational Mathematics
• Winter 2023. ACM 217: Probability in High Dimensions

AY 2021

• Fall 2021. CMS 117: Probability Theory & Computational Mathematics
• Winter 2022. ACM 204: Matrix Analysis

Short courses

I regularly present short courses and tutorials at international conferences. Some highlights:

• 2023 CIME Summer School: Randomized matrix computations
• 2019 ENS Summer School: Matrix concentration and computational linear algebra
  • Notes
  • Video 1
  • Video 2
  • Video 3
  • Video 4
  • Video 5
  • Video 6
  • Video 7
  • Video 8
  • Video 9
• 2017 Statistics Spring School: Structured signal processing via convex programming
  • Slides
• 2014 ICML Tutorial: Finding structure with randomness: Probabilistic algorithms for linear algebra
  • Slides
  • Paper 1
  • Paper 2
• 2013 Simons Big Data Bootcamp: User-friendly tools for studying random matrices
  • Slides
  • Video 1
  • Video 2
  • Video 3
• 2012 NeurIPS Tutorial: User-friendly tools for studying random matrices
  • Slides
  • Video
  • Paper

Lecture Notes

Many of my courses are supported by typed lecture notes. For certain classes, student scribes prepare the notes as part of their course work. Some of these notes are publicly available:

• CMS 117: Probability Theory & Computational Mathematics (Fall 2023)
• ACM 204: Matrix Analysis (Winter 2022)
• ACM 217: Probability in High Dimensions (Winter 2021, corrected Winter 2023)
• ACM 204: Randomized Algorithms for Matrix Computations (Winter 2020)
• ENS Short Course: Matrix Concentration and Computational Linear Algebra (Paris, July 2019)
• ACM 270: Quantum and Classical Information Processing with Tensors (Spring 2019, instructor Prof. Richard Kueng)
• ACM 204: Lectures on Convex Geometry (Fall 2018)
• NeurIPS Tutorial: An Introduction to Matrix Concentration Inequalities (Stateline, December 2012)

Caveat lector: Lecture notes carry no warranty of correctness, completeness, or proper citation of scholarly sources.