Hello! I am a Ph.D. candidate in the Uncertainty Quantification Group at the University of Houston, advised by Dr. Ruda Zhang.
I expect to complete my Ph.D. in May 2027 and am seeking postdoctoral positions or research-oriented industry roles in trustworthy scientific AI and uncertainty quantification, starting Summer/Fall 2027. I am happy to talk with prospective hosts, collaborators and teams. Reach me by email or on LinkedIn.
My research builds predictive models that know when they don’t know. Engineering decisions increasingly rest on models nobody can fully check: fast surrogates standing in for simulations too expensive to run, and pretrained foundation models emulating physics. They return a confident number whether or not they are still in a regime where they work. We make those models report how far they should be trusted, across model uncertainty in computational mechanics, calibration of scientific foundation models, and the optimization that makes both practical.
I came to this from structural health monitoring, where a damage signal and a seasonal temperature swing look alike in the data. A monitoring system that cannot tell them apart reports a crack that is not there, and does it with complete confidence.
See Research for the methods, Projects for the systems they were built for, and Publications for the papers.
Education
Ph.D. in Civil Engineering, University of Houston, May 2027*
Thesis: Quantifying and Reducing Model Uncertainty using Stochastic RepresentationsM.Tech (Research) in Civil Engineering, Indian Institute of Science, Bangalore, June 2023
Thesis: Structural Health Monitoring Accounting for Thermal Variability and Damage Using Approximate Bayesian Computation (ABC)B.Tech in Civil Engineering, Indian Institute of Technology, Roorkee, May 2018
Thesis: Design of Hydro Power Project
News
August 19, 2026 – Selected as a recipient of the UH Chevron Energy Graduate Fellows Award for 2026–2027, awarded for energy-related research in collaboration with Dr. Ruda Zhang.
August 13, 2026 – Our paper Nonparametric Stochastic Subspaces via the Bootstrap Method for Characterizing Model Error is published in the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering.
July 24, 2026 – Presented Calibrated Uncertainty for Fine-tuned Scientific Foundation Models via Stochastic Attention at the 17th World Congress on Computational Mechanics and 10th European Congress on Computational Methods in Applied Sciences and Engineering (WCCM/ECCOMAS 2026) in Munich, Germany.
Contact
ayadav4 ‘at’ uh ‘dot’ edu
I am interested in collaborations on calibrated uncertainty for scientific foundation models, model-form uncertainty in computational mechanics, and decision-making under uncertainty. Please feel free to reach out by email. You can also reach me via LinkedIn.