Research Focus
Numerical analysis, scientific computing, and deep learning
I am interested in the study and development of numerical algorithms in applied mathematics. I mainly work in the following three areas: (1) Novel spectral methods for the solution of differential equations, (2) Low-rank techniques, and (3) Theoretical aspects of deep learning.
Publications
- Dense networks that do not synchronize and sparse ones that do (with M. Stillman and S. H. Strogatz), Chaos, 30 (2020), 083142.
- Fast algorithms using orthogonal polynomials (with S. Olver and R. M. Slevinsky), Acta Numerica, 29 (2020), pp. 573-699.
- Stable extrapolation of analytic functions (with L. Demanet), Foundations of Computational Mathematics, 19 (2019), pp. 297-331.
- Bounds on the singular values of matrices with displacement structure (with B. Beckermann), SIAM Review, 61 (2019), pp. 319-344.
- Why are big data matrices approximately low rank? (with M. Udell), SIAM Journal on Mathematics of Data Science, 1 (2019), pp. 144-160.
In the news
- Twelve new Klarman Fellows to pursue innovative, timely research in A&S
- New center merges math, AI to push frontiers of science
- Arts and Sciences faculty featured on Academic Minute
- Weiss teaching award honors eight exceptional faculty
- Rational neural network advances machine-human discovery
- Six A&S professors named 2022 Simons fellows
- Computing with rational functions
- Eleven assistant professors win NSF early-career awards
- 30 Arts & Sciences faculty honored with endowed professorships
- Grants create engagement opportunities for students
- Grants create community-engaged opportunities for students
- Math professor mentors winner of science talent search
MATH Courses - Spring 2025
- MATH 2310 : Linear Algebra for Data Science
- MATH 4900 : Supervised Research
- MATH 4901 : Supervised Reading
- MATH 7290 : Seminar on Scientific Computing and Numerics