Measure risk
Model uncertainty, distribution shift, and downside outcomes before learned systems make decisions.
UT Austin ECE / Reliable learning systems
Reliable machine learning for risk, constraints, and decisions.
I am a Ph.D. student in Electrical and Computer Engineering at The University of Texas at Austin, advised by Prof. Hao Zhu. My research develops risk-aware and verifiable learning methods for decision-making under uncertainty, with applications in reinforcement learning, large language models, and power systems.
I received my B.Eng. in Electrical and Computer Engineering, with a minor in Mathematics, from the American University of Beirut, where I worked with Prof. Rabih Jabr and Prof. Sami Karaki. I have also interned at Los Alamos National Laboratory with Dr. Wenting Li, Dr. Brian Bell, and Dr. Russell Bent.
Research
From uncertainty quantification to deployment in reliability-critical systems.
Model uncertainty, distribution shift, and downside outcomes before learned systems make decisions.
Build verification and stress-testing tools for learned models in constrained technical domains.
Translate RL and LLM methods into actionable decisions for systems where failures are expensive.
Selected Publications
ICML 2025
ICML 2025
arXiv 2024
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