Lianghao Cao
computational science, engineering, and mathematics researcher
My last name is pronounced ts'ao
Embedded Files
Check out our paper on amortized solutions of model-constrained Bayesian inverse problems using surrogate-driven measure transport.
I am a Postdoctoral Scholar Research Associate in Computing and Mathematical Sciences at Caltech, sponsored 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, The University of Texas at Austin.
My research addresses issues at the heart of computational engineering and sciences: understanding, enhancing, and controlling the quality, validity, and reliability of simulation-based predictions of complex physical systems. I have worked on various uncertainty quantification and optimization problems associated with models governed by parametric partial differential equations. I have extensively worked on application problems associated with computational polymer science. I am currently working on data-driven constitutive modeling for multiscale inelastic materials.
More on my research page and publication page.
Seeking full-time positions in the USA for computational science and engineering research
Seeking full-time positions in the USA for computational science and engineering research
I am interested in
A tenure-track position at an R1 research university.
A research position at a national lab or a company with a clear focus on addressing grand challenges in science, engineering, and medicine.
Please don't hesitate to contact me about potential job opportunities.
Links: [Google scholar] [LinkedIn] [CV]
Email: lianghao@caltech.edu
Updates
Updates
May 2025
Our paper titled "Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems" is published in the Journal of Machine Learning Research. [link]
Mar. 2025
George, Margaret, and I are organizing a mini-symposium at SIAM CSE25. The mini-symposium (MS249, Thursday, 2:10–3:25 pm and 4:20–5:35 pm) focuses on data-driven methods for multiscale modeling and homogenization. You are cordially invited to attend!
Feb. 2025
Our preprint titled "Learning Memory and Material Dependent Constitutive Laws" is now available on arXiv. This is joint work with Kaushik Bhattacharya, George Stepaniants, Andrew Stuart, and Margaret Trautner. [link]
Dec. 2024
The special issue on "Scientific Machine Learning" is now available online in Foundations of Data Science, a publication of the American Institute of Mathematical Sciences. I am one of the guest editors on this special issue. [link]
You can see more updates here.
Talks
Talks
October 2025
I gave a talk at PhysicsX. The talk was on derivative-informed surrogate models (i.e., DINO) and their applications in accelerating Bayesian inversion [slides]. Open the slides in Adobe Acrobat for animations.
October 2025
I gave a talk at a DDCR MURI meeting. I presented ongoing work on optimizing experimental design for inferring history-dependent constitutive models [slides]. Open the slides in Adobe Acrobat for animations.
July 2025
I gave two talks at USNCCM18 on learning history- and microstructure-dependent constitutive laws: (i) in MS 233 I presented ongoing work on Bayesian optimal experimental design [slides], and (ii) in the special session on SciML, I presented an operator learning approach from microscale simulations with a focus on theoretical results [slides]. Open the slides in Adobe Acrobat for animations.
Mar. 2025
I presented (on Zoom) at the Frontiers in Scientific Machine Learning Seminar Series hosted by University of Michigan on Mar. 28 at 12–1 PM EST. The talk was on derivative-informed surrogate models (i.e., DINO) and their applications in accelerating Bayesian inversion. [slides with typos fixed!]
You can see my old talks and slides here.
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