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.
Updates
Updates
December 2025
Our paper titled "Derivative-Informed Fourier Neural Operator: Universal Approximation and Applications to PDE-Constrained Optimization" is now available on arXiv. This is a joint work with Boyuan John Yao, Dingcheng Luo, Nikola Kovachki, Thomas O'Leary-Roseberry, and Omar Ghattas. [link]
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]
March 2025
George Stepaniants, Margaret Trautner, and I organized a mini-symposium at SIAM CSE25 (MS249, Thursday, 2:10–3:25 pm and 4:20–5:35 pm). It focuses on data-driven methods for multiscale modeling and homogenization.
Presentations
Presentations
March 2026
I will give a talk at SIAM UQ26 in MS 89 (Monday, March 23, 6:00–6:25 PM) about my ongoing work regarding amortized sequential Bayesian inference and experimental design. See the abstract of the talk here.
October 2025
I gave a talk at PhysicsX on derivative-informed neural surrogates and their applications in accelerating Bayesian inversion [slides]. Open the slides in Adobe Acrobat for animations. Feedback is welcome!
October 2025
I gave a talk at a DDCR MURI meeting. I presented ongoing work on optimal experimental design for reliable learning of history-dependent constitutive laws [slides]. Open the slides in Adobe Acrobat for animations. Feedback is welcome!
See past updates and presentations here.
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