Lianghao Cao
A Computational Science, Engineering, and Mathematics Researcher
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About Me
About Me
I am a Postdoctoral Scholar Research Associate in Computing and Mathematical Sciences at the California Institute of Technology, hosted by Andrew M. Stuart. Before this position, I obtained a Ph.D. in Computational Science, Engineering, and Mathematics under the supervision of J. Tinsley Oden and Omar Ghattas at the Oden Institute of the University of Texas at Austin. A brief professional bio can be found here.
My research combines mechanistic modeling, uncertainty quantification, and scientific machine learning to enhance the reliability of physical simulations and support risk-aware decision-making. I have experience in using modeling and simulation to address challenges in polymer science, multiscale inelasticity, epidemiology, and geophysics. Read more about my research here.
I am seeking full-time research positions!
I am seeking full-time research positions!
I am applying to tenure-track positions in engineering, statistics/data science, or computational mathematics. Please also feel free to contact me about industry research positions.
Examples of Research Topics
Examples of Research Topics
Inhomogenous Polymers
Learn to predict patterns using microscopy data
Inverse Problems
Riemannian Manifold Hamiltonian Monte Carlo
Multiscale Modeling
Stress response of a crystal plasticity unit cell
Material Testing
Image data from uniaxial testing
Sequential
Decision- Making
Greedy approach for finding signal location
Updates
Updates
April 2026
Our paper titled "Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws" is published in Computer Methods in Applied Mechanics and Engineering. This is a joint work with Kaushik Bhattacharya and Andrew Stuart. [link]
March 2026
Our preprint titled "Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws" is now available on arXiv. This is a joint work with Kaushik Bhattacharya and Andrew Stuart. [link]
March 2026
Our paper titled "LazyDINO: Fast, Scalable, and Efficiently Amortized Bayesian Inversion via Structure-Exploiting and Surrogate-Driven Measure Transport" is published in the Journal of Machine Learning Research. This is a joint work with Joshua Chen (joint first author), Michael Brennan, Thomas O'Leary-Roseberry, Youssef Marzouk, and Omar Ghattas. [link]
Presentations
Presentations
July 2026
I will give an invited talk at the SIAM Annual Meeting 2026, in MS4 (8:30 A.M., Room 6, Monday, July 6th). I will present LazyDINO, an amortized Bayesian inversion method that uses first-order model evaluations to achieve state-of-the-art efficiency. See the published paper here.
June 2026
I will give a contributed talk (Monday, June 22nd) at USNCTAM 2026, in the MS on Data-driven approaches for solid mechanics. I will present how to efficiently and reliably learn inelasticity constitutive laws from optimized experiments. See the published paper here.
April 2026
I gave an invited talk at the CaCao workshop hosted by the Scripps Institution of Oceanography at UC San Diego. The topic is amortized sequential Bayesian inference and experimental design.
See past updates and presentations here.
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