About
I am a fourth-year Ph.D. student in Computer Science at Arizona State University, studying the intersection of optimization, machine learning, and safe autonomy — how intelligent systems can make reliable decisions under uncertainty, especially in cyber-physical and autonomous systems.
My current research explores Bayesian optimization and probabilistic scenario generation, scaling these methods to complex systems and applying diffusion-based models to improve robustness and coverage in safety testing. Much of the work combines mathematical modeling, AI, and formal verification through collaborative programs like DARPA's ARCOS and ASIMOV.
Outside of research, I enjoy playing tennis, hiking, and experimenting with music — activities that keep me curious, balanced, and inspired.
Research Areas
Bayesian Optimization
Optimization under uncertainty
Sample-efficient methods for tuning and searching complex, expensive-to-evaluate systems — including grey-box and tree-structured Gaussian process models.
Safe Autonomy & CPS
Testing & verification
Search-based testing and falsification for cyber-physical systems, with probabilistic guarantees on coverage and requirement satisfaction.
Generative Safety Testing
Diffusion models
Applying diffusion-based generative models to produce diverse, realistic scenarios that stress-test autonomy and expand safety-testing coverage.
Formal Methods
Runtime verification
Parameter mining and verification of temporal-logic requirements, with probabilistic guarantees — contributing to the ARCH-COMP falsification benchmarks.
Publications
Service
Program Committee
Organizing & review committees
Journal & Conference Reviewing
Peer reviewer for the following venues
Teaching
IEE 575 · Applied Stochastic Operations Research Models
Teaching Assistant · Arizona State University, Tempe, AZ
- Conducted lab sessions for graduate students on stochastic optimization, Monte Carlo simulation, and Bayesian optimization techniques.
- Developed Python-based instructional material and guided student projects involving optimization and simulation models.
- Led hands-on sessions implementing Gaussian Process Regression and Bayesian optimization using Python's scientific libraries.
Contact
Get in Touch
I'm always happy to discuss research collaborations, optimization and safe-autonomy problems, or questions about my work.
tkhandai@asu.edu