Hello! I am a PhD Candidate in Uncertainty Quantification Group at the University of Houston, advised by Ruda Zhang.
My research centers on model-form uncertainty, developing probabilistic methods that improve the reliability of computational models in engineering and science. More recently, I have been working on uncertainty-aware scientific foundation models, aiming to make large-scale scientific models both accurate and trustworthy.
Education
Ph.D. in Civil Engineering, University of Houston, May 2027*
CGPA: 4.0/4.0
Thesis: Quantifying and Reducing Model-Form Uncertainty using Stochastic SubspacesM.Tech (Research) in Civil Engineering, Indian Institute of Science (IISc), Bangalore, June 2023
CGPA: 8.3/10
Thesis: Structural Health Monitoring Accounting for Thermal Variability and Damage Using Approximate Bayesian Computation (ABC)B.Tech in Civil Engineering, Indian Institute of Technology (IIT), Roorkee, May 2018
CGPA: 8.75/10 (First Division with Distinction)
Thesis: Design of Hydro Power Project
News
December 25, 2025 – Attended The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025) at San Diego, CA.
September 25, 2025 – Attended The Third Scientific Machine Learning for Differential Equations workshop at the Oden Institute for Computational Engineering & Sciences, UT Austin.
July 23, 2025 – Presented our work on Stochastic Reduced-Order Modeling for Characterization and Correction of Model-Error at USNCCM 2025 in Chicago, IL.
December 16, 2024 – Presented our poster on Stochastic Subspace via Bootstrap for Model-Form Uncertainty at the Conference on Applied AI & Scientific Machine Learning (CASML), IISc Bangalore.
November 19, 2024 – Awarded the Jimmie A. Schindewolf Academic Scholarship for academic and research excellence at the University of Houston.
Contact
ayadav4 ‘at’ uh ‘dot’ edu
For collaboration opportunities, project discussions, or professional inquiries, feel free to reach out via email. You can also reach me via LinkedIn.