Teaching

18.408: Algorithmic Aspects of Machine Learning, Spring 2023

Graduate course on modern algorithmic approaches in machine learning
See the course webpage here

6.S084/18.061: Linear Algebra and Optimization, Fall 2022

Undergraduate course on linear algebra and optimization co-taught with Pablo Parrilo
See the course webpage here

18.200: Principles of Discrete Applied Mathematics, Spring 2022

Undergraduate course on discrete math and proof writing co-taught with Michel Goemans
See the course webpage here

6.S084/18.061: Linear Algebra and Optimization, Fall 2021

Undergraduate course on linear algebra and optimization co-taught with Pablo Parrilo
See the course webpage here

18.408: Theoretical Foundations for Deep Learning, Spring 2021

Graduate course on theoretical foundations, and open questions, in deep learning
See the course webpage here

6.S084/18.S096: Linear Algebra and Optimization, Fall 2020

New undergraduate course on linear algebra and optimization co-taught with Pablo Parrilo
See the course webpage here

6.042/18.062: Mathematics for Computer Science, Fall 2019

Undergraduate course on discrete math and probability co-taught with Zachary Abel and Ronitt Rubinfeld
See the course webpage here

6.042/18.062: Mathematics for Computer Science, Fall 2018

Undergraduate course on discrete math and probability co-taught with Tom Leighton
See the course webpage here

18.200: Principles of Discrete Applied Mathematics, Spring 2018

Undergraduate course on discrete math and proof writing co-taught with Michel Goemans
See the course webpage here

18.408: Algorithmic Aspects of Machine Learning, Fall 2017

Graduate course on modern algorithmic approaches in machine learning
See the course webpage here

6.042/18.062: Mathematics for Computer Science, Fall 2016

Undergraduate course on discrete math and probability co-taught with Tom Leighton
See the course webpage here

6.854/18.415J: Advanced Algorithms, Spring 2016

Graduate course on advanced topics in algorithms
See the course webpage here

18.200: Principles of Discrete Applied Mathematics, Fall 2015

Undergraduate course on discrete math and proof writing co-taught with Peter Shor
See the course webpage here

18.409: Algorithmic Aspects of Machine Learning, Spring 2015

Redesigned graduate course on modern algorithmic approaches in machine learning
See the course webpage here

6.042/18.062: Mathematics for Computer Science, Fall 2014

Undergraduate course on discrete math and probability co-taught with Tom Leighton
See the course webpage here

18.434: Seminar in Theoretical Computer Science, Spring 2014

Undergraduate seminar on polytopes and optimization
See the course webpage here

18.S996: Algorithmic Aspects of Machine Learning, Fall 2013

New graduate course on modern algorithmic approaches in machine learning
See the course webpage here

The Math Behind the Machine, Summer 2013

Summer course for high school students introducing them to theoretical computer science
Taught at Rutgers University through the New Jersey Governor's School