I am always looking for skilled and motivated folks to work with. I typically work with students from the Department of Mathematics or the School of Computing at Utah. Typically I desire a foundation of knowledge that spans some subset of linear algebra, differential equations, numerical methods, proof-based analysis, and probability/statistics. If you are interested, please send me a CV and a short description of your interests.

Please note that if you are a prospective graduate student in Mathematics, intending to apply to the University of Utah, you should follow the standard admissions procedures.


Current group members


Name Position Institution Project/thesis focus
Haoyu Chen

(jointly advised w/ Anna Little)

PhD, Mathematics
Expected May, 2026
University of Utah Spectral clustering, path-based metrics
Jarom Hogue

(jointly advised w/ Mike Kirby)

Postdoctoral Scholar, SCI Institute
2020-present
University of Utah deep learning, large-scale numerical linear algebra


Group alumni


Name Post-degree position Degree/Position Institution Project/thesis focus
Xuesong Bai

(jointly advised w/ Elena Cherkaev)

Postdoctoral scholar, Brandeis University PhD, Mathematics
May 2022
University of Utah fractional PDEs, inverse problems, optimization, numerical algorithms
Dihan Dai

(jointly advised w/ Yekaterina Epshteyn)

Applied Scientist, Amazon PhD, Mathematics
May, 2022
University of Utah Numerical methods for hyperbolic equations under uncertainty, structure-preserving function approximation via optimization
Zexin Liu Algorithms Engineer; Quantaeye, Inc. PhD, Mathematics
May, 2022
University of Utah uncertainty quantification, cardiac biomedical applications, orthogonal polynomials
Ryleigh Moore Research engineer, The Mathworks PhD, Mathematics
May, 2022
University of Utah numerical methods for partial and stochastic differential equations
Yiming Xu

(jointly advised w/ Tom Alberts)

Research analyst, Wells Fargo PhD, Mathematics
May, 2022
University of Utah high-dimensional probability and approximation, statistical learning, multifidelity methods
Vidhi Zala

(jointly advised w/ Mike Kirby)

Intel Corporation PhD, Computing
December 2021
University of Utah convex optimization, structure-preserving function approximation, numerical methods for PDEs
Jacia Abplanalp Data analysis internship at Zions Bank BS Mathematics
May 2021
University of Utah graph spectral clustering
Vignesh Iyer Graduate student at UC Irvine BS, Mathematics
May 2021
University of Utah numerical methods for spatial fractional PDEs
Erin Linebarger Research scientist, Neya Systems PhD, Mathematics
May 2020
University of Utah robotics path planning and decision-making
Jiahua Jiang Postdoctoral scholar, Virginia Tech PhD, Engineering and Applied Science
May 2018
University of Massachusetts Dartmouth reduced order modeling, uncertainty quantification
Mani Razi

(jointly advised w/ Mike Kirby)

Senior quantitative risk analyst, USAA Postdoctoral Scholar, SCI Institute
2016-2019
University of Utah multifidelity methods, uncertainty quantification
Vahid Keshavarzzadeh

(jointly advised w/ Mike Kirby)

Machine Learning Scientist, General Motors Postdoctoral Scholar, SCI Institute
2017-2022
University of Utah computational mechanics, high-dimensional quadrature, uncertainty quantification, multifidelity algorithms
Gurpartap Bhatti Software engineer, Navitaire BS Mathematics
December 2018
University of Utah sparse approximation, dynamical systems