Teaching & Advising

I am the John C. Malone Professor in the Department of Statistics & Data Science at Yale, and I served as Director of Graduate Studies for the Applied Mathematics program (2023–2025). I teach undergraduate and graduate courses in applied mathematics, computer science, and statistics & data science, and I am always happy to hear from Yale students — undergraduate or graduate — who are interested in the topics on my projects page.

Current Ph.D. students

• Zijian Wang, Applied Mathematics, Yale (expected 2026)
• Isay Katsman, Applied Mathematics, Yale (expected 2026)
• Asaf Etgar, Applied Mathematics, Yale (expected 2027)

Former Ph.D. students

• Joon-Hyeok Yim, Yale 2025
• Rishi Sonthalia, Michigan 2021 — now at UCLA
• Claire Lin, Michigan 2021
• Alexander Vargo, Michigan 2020
• Umang Varma, Michigan 2019 — now at Google
• Yitong Sun, Michigan 2019 — now at Huawei
• Audra McMillan, Michigan 2018 — now at Apple Research
• Yan-shuo Tan, Michigan 2018
• Jeremy Hoskins, Michigan 2017 — now at the University of Chicago
• Paul Shearer, Michigan 2013
• Jae Young Park, Michigan (EECS) 2013 — now at Apple Research
• Praveen Yenduri, Michigan (EECS) 2012 — now at MathWorks
• Ray Maleh, Michigan 2009 — now at L3 Communications
• Joel Tropp, UT Austin 2004 — now at Caltech

Postdoctoral fellows

• Kevin O'Neill, Yale, 2021–2024
• Howard Levinson, Michigan, 2018–2020
• Lalit Jain, Michigan, 2016–2017
• Brett Hemenway, Michigan, 2009–2012
• Joel Tropp, Michigan, 2004–2007

Undergraduate research (senior theses, REU, UROP)

• Mihai Esanu, Yale (summer research project), 2025
• David Gold, Yale (senior thesis), 2022
• Stephen Newman, Yale (senior thesis), 2021
• Yulan Zhang, Yale (senior thesis), 2021
• Justin Shetty and James Wich, Michigan (UROP), 2016–2017
• Yi Wang, Michigan (EECS), 2006
• Kyle Herrity, Michigan (REU), 2005
• Daniel Sikora, Michigan (REU), 2005
• Kirill Levchenko, UC San Diego (at AT&T Labs-Research), 2003
• Joel Tropp, UT Austin (at AT&T Labs-Research), 2002
• Jing Zou, Princeton (at AT&T Labs-Research), 2002
• Maya Gupta, Stanford (at AT&T Labs-Research), 2000
• Stephane Seuret, ENST (at AT&T Labs-Research), 1999
• Youngmi Joo, Stanford (at AT&T Labs-Research), 1999
• Jennifer Steichen, Univ. Illinois (at AT&T Labs-Research), 1998

Courses taught

Yale University

• Fall 2025 — E&CE 202, Introduction to Communications and Control (Electrical & Computer Engineering)
• Spring 2025 — CPSC 366, Intensive Algorithms (Computer Science)
• Fall 2024 — E&CE 202, Introduction to Communications and Control (Electrical & Computer Engineering)
• Spring 2024 — CPSC 366, Intensive Algorithms (Computer Science)
• Spring 2023 — Math 752, Topics in Sparse Analysis (Mathematics)
• Fall 2022 — S&DS 569, Numerical Linear Algebra (Statistics & Data Science)
• Spring 2022 — Math 421, The Mathematics of Data Science (Mathematics)
• Fall 2021 — S&DS 431/631, Computation and Optimization (Statistics & Data Science)
• Spring 2021 — S&DS 431/631, Computation and Optimization (Statistics & Data Science)
• Fall 2020 — Math 863, Topics in Sparse Analysis (Mathematics)

University of Michigan

• Winter 2020 — Math 651, Mathematical Analysis and Algorithms of Machine Learning
• Winter 2019 — Math 650, Fourier Analysis
• Fall 2018 — Math 556, Applied Functional Analysis
• Fall 2016 — Math 571, Numerical Linear Algebra
• Fall 2016 — Math 556, Applied Functional Analysis
• Fall 2015 — Math 556, Applied Functional Analysis
• Winter 2015 — Math 416, Randomized Algorithms
• Fall 2014 — Math 571, Numerical Linear Algebra
• Winter 2014 — Math 416, Randomized Algorithms
• Fall 2013 — Math 556, Applied Functional Analysis
• Fall 2012 — Math 471, Introduction to Numerical Methods
• Fall 2012 — Math 571, Numerical Methods for Scientific Computing I
• Winter 2012 — Math 651, Sparse Analysis
• Fall 2011 — Math 571, Numerical Methods for Scientific Computing I
• Winter 2010 — Math 471, Introduction to Numerical Methods
• Fall 2009 — Math 571, Numerical Methods for Scientific Computing I
• Fall 2008 — EECS 598, Compressive Sensing (Electrical and Computer Engineering)
• Winter 2008 — Math 571, Numerical Methods for Scientific Computing I
• Fall 2007 — Math 471, Introduction to Numerical Methods
• Winter 2007 — Math 650, Fourier Analysis
• Winter 2006 — Math 650, Fourier Analysis
• Fall 2005 — Math 425, Introduction to Probability
• Fall 2005 — Math 454, Boundary Value Problems for Partial Differential Equations
• Fall 2004 — Math 454, Boundary Value Problems for Partial Differential Equations
• Fall 2004 — Math 450, Advanced Mathematics for Engineers

Short courses and other

• Summer 2011 — Short course on Sparse Approximation, Women in Mathematics Program, Institute for Advanced Study
• Summer 2010 — Short course on Sparse Approximation, Park City Mathematics Institute
• Winter 2000 — Time/Frequency Analysis, Department of Mathematics, Stanford University