LEVIS: Large Exact Verifiable Input Spaces for Neural Networks
ICML 2025
UT Austin ECE ยท 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
Methods for learning decisions under uncertainty, distribution shift, and explicit risk specifications.
Stress testing, verification, and input-space construction for learned models used in constrained settings.
Fine-tuning and inference-time methods for language models that propose technical actions under constraints.
Power-system testbeds for studying learning, optimization, reliability, market participation, and control.
If intelligence is a cake, the bulk of the cake is self-supervised learning, the icing on the cake is supervised learning, and the cherry on the cake is reinforcement learning.
Selected Publications
ICML 2025
ICML 2025
arXiv 2024
News