Chris Camaño | California Institute of Technology(Caltech)

About Me

I’m a first-year PhD student in the Applied and Computational Mathematics department at Caltech, advised by Professor Joel Tropp. My research focuses on the intersection of randomized numerical linear algebra (rNLA) and large-scale data science, with an emphasis on scalable algorithms and high-dimensional computation.

I am currently funded through the National Science Foundation Graduate Research Fellowship and the Kortschak Scholars Fellowship.

Research Interests

Randomized Numerical Linear Algebra: matrix sketching, fast linear solvers, randomized eigensolvers, algorithm acceleration.
Tensor Networks and Quantum Mechanical Simulation: MPO-MPS contraction, randomized methods for tensor networks, efficient Hamiltonian time evolution.
Approximate Gaussian Processes: sub-cubic gaussian process models, inducing point approximations, conjugate gradient methods for kernel inversion.

Research

arxiv

Successive randomized compression: A randomized algorithm for the compressed MPO-MPS product (2025)

Chris Camaño, Ethan N. Epperly, Joel A. Tropp

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arxiv

SoftKI: High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation (2024)

Daniel Huang, Chris Camaño

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arxiv

Push: Concurrent Probabilistic Programming for Bayesian Deep Learning (2023)

Daniel Huang, Chris Camaño, Jonathan Tsegaye, Jonathan Austin Gale

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News

[Apr. 2025] Invited to speak at Southern California Applied Math Symposium on randomized tensor networks.
[Apr. 2025] Invited to speak at UCSD Mathematics of Information, Data, and Signals Seminar on randomized tensor networks.
[Feb. 2025] Invited to speak at the Argonne National Laboratory Toward Next-Generation Ecosystems for Scientific Computing workshop on randomized tensor networks.
[March. 2024] Awarded the NSF GRFP & Kortschak fellowships 🎉.
[Feb. 2024] Accepeted to the Caltech PhD program 🎉.
[Jan. 2024] Invited to speak at the Joint Math Meeting 2024 on randomized tensor networks.
[Oct. 2023] Invited to speak on Uniform Manifold Approximate Projection (UMAP) at the Great Minds in STEM conference 2023 (3rd place).
[Jun. 2023] Invited to research with Joel Tropp and Ethan Epperly at Caltech University on randomized tensor networks.
[Jun. 2023] Invited to the Mathematical Science Research Institute(MSRI) Formalization of Mathematics summer school to learn the Lean4 language. Project culimated with an accepted pull request to mathlib4.
[Jan. 2023] Invited to speak on randomized eigensolvers & tensor networks at the Joint Math Meeting 2023.
[Jun. 2022] Invited to research with Xioye Li and Roel Van Beeumen on randomized eigensolvers & tensor networks at Lawrence Berkeley National Laboratory.