Tyler Chen
I’m an Applied Research Lead on the quantum-inspired and randomized algorithms team within JPMorgan Chase’s Global Technology Applied Research division.
Broadly, my research aims design and analyze fast (in theory and practice) randomized algorithms for core linear algebra tasks.
I was previously a Courant Instructor at New York University (sponsored by Chris Musco) and did my PhD in Applied Math from the University of Washington (advised by Anne Greenbaum and Tom Trogdon).
Even earlier, I was an undergrad at Tufts University, where I studied math, physics, and studio art.
An overview of my research interests along with more detailed introductions to particular directions can be found here as well as by following the links on papers listed below.
Code to generate the figures from my papers can be found on Github.
Please feel free to contact me with any questions about my research.
I am happy with “they” or “he” as pronouns.
Key Info
Talks
Slides from past talks and a list of upcoming travel can be found here.
Monographs
[1]
The Lanczos algorithm for matrix functions: a handbook for scientists
Tyler Chen.
2024
.
Papers (in progress)
[4]
Preconditioning without a preconditioner: faster ridge-regression and Gaussian
sampling with randomized block Krylov methods
Tyler Chen, Caroline Huber, Ethan Lin, and Hajar Zaid.
2025
.
[3]
Randomized block Krylov subspace methods for low rank approximation of matrix functions
David Persson, Tyler Chen, and Christopher Musco.
2024
.
[2]
Fixed-sparsity matrix approximation from matrix-vector products
Noah Amsel, Tyler Chen, Feyza Duman Keles, Diana Halikias, Cameron Musco, and Christopher Musco.
2024
.
[1]
Optimal Polynomial Approximation to Rational Matrix Functions Using the Arnoldi Algorithm
Tyler Chen, Anne Greenbaum, and Natalie Wellen.
2023
.
Papers (published)
[18]
Randomized matrix-free quadrature: unified and uniform bounds for stochastic Lanczos quadrature and the kernel polynomial method
Tyler Chen, Thomas Trogdon, and Shashanka Ubaru.
SIAM Journal on Scientific Computing (to appear).
2025
.
[17]
Near-optimal hierarchical matrix approximation from matrix-vector products
Chen, Tyler, Keles, Feyza Duman, Halikias, Diana, Musco, Cameron, Musco, Christopher, and Persson, David.
Proceedings of the 2025 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA).
2025
.
[16]
Near-Optimal Approximation of Matrix Functions by the Lanczos Method
Noah Amsel, Tyler Chen, Anne Greenbaum, Cameron Musco, and Christopher Musco.
Conference on Neural Information Processing (NeurIPS).
2024
.
[15]
Faster Randomized Partial Trace Estimation
Chen, Tyler, Chen, Robert, Li, Kevin, Nzeuton, Skai, Pan, Yilu, and Wang, Yixin.
SIAM Journal on Scientific Computing.
2024
.
[14]
Near-optimal convergence of the full orthogonalization method
Chen, Tyler and Meurant, Gérard.
ETNA - Electronic Transactions on Numerical Analysis.
2024
.
[13]
On the fast convergence of minibatch heavy ball momentum
Bollapragada, Raghu, Chen, Tyler, and Ward, Rachel.
IMA Journal of Numerical Analysis.
2024
.
[12]
GMRES, pseudospectra, and Crouzeix’s conjecture for shifted and scaled Ginibre matrices
Chen, Tyler, Greenbaum, Anne, and Trogdon, Thomas.
Mathematics of Computation.
2024
.
[11]
A posteriori error bounds for the block-Lanczos method for matrix function approximation
Qichen Xu and Tyler Chen.
Numerical Algorithms.
2024
.
[10]
Stability of the Lanczos algorithm on matrices with regular spectral distributions
Chen, Tyler and Trogdon, Thomas.
Linear Algebra and its Applications.
2024
.
[9]
A spectrum adaptive kernel polynomial method
Tyler Chen.
The Journal of Chemical Physics.
2023
.
[8]
Krylov-Aware Stochastic Trace Estimation
Tyler Chen and Eric Hallman.
SIAM Journal on Matrix Analysis and Applications.
2023
.
[7]
Low-Memory Krylov Subspace Methods for Optimal Rational Matrix Function Approximation
Tyler Chen, Anne Greenbaum, Cameron Musco, and Christopher Musco.
SIAM Journal on Matrix Analysis and Applications.
2023
.
[6]
Numerical computation of the equilibrium-reduced density matrix for strongly coupled open quantum systems
Tyler Chen and Yu-Chen Cheng.
The Journal of Chemical Physics.
2022
.
[5]
Error Bounds for Lanczos-Based Matrix Function Approximation
Tyler Chen, Anne Greenbaum, Cameron Musco, and Christopher Musco.
SIAM Journal on Matrix Analysis and Applications.
2022
.
[4]
Analysis of stochastic Lanczos quadrature for spectrum approximation
Tyler Chen, Thomas Trogdon, and Shashanka Ubaru.
International Conference on Machine Learning (ICML).
2021
.
[3]
On the Convergence Rate of Variants of the Conjugate Gradient Algorithm in Finite Precision Arithmetic
Anne Greenbaum, Hexuan Liu, and Tyler Chen.
SIAM Journal on Scientific Computing.
2021
.
[2]
Non-asymptotic moment bounds for random variables rounded to non-uniformly spaced sets
Tyler Chen.
Stat.
2021
.
[1]
Predict-and-recompute conjugate gradient variants
Tyler Chen and Erin C. Carson.
SIAM Journal on Scientific Computing.
2020
.
Thesis
Lanczos based methods for matrix functions
[PDF]
[source]
[commentary on design]
Here are links to my Google Scholar profile and ORCID: 0000-0002-1187-1026.
Other
Materials from my past applications and proposals can be found here.