Senior Research Scientist
Education
- M.S. Applied Mathematics (2019), Rensselaer Polytechnic Institute
- Ph.D. Electrical Engineering (2019), Rensselaer Polytechnic Institute
Expertise
- Electric Energy Systems
- Machine Learning, Artificial Intelligence, Complex Systems
- Combinatorial and Integer Optimization
- Combinatorial, Convex and Robust Optimization
- Linear and Nonlinear Optimization
- Simulation
About
Wenting Li has been a Research Scientist at the Georgia Institute of Technology since 2026. Prior to joining Georgia Tech, she gained extensive research experience at the University of Texas at Austin and Los Alamos National Laboratory (LANL). She completed her postdoctoral training at LANL’s Center for Nonlinear Studies (CNLS) and Theoretical Division in 2023. She received her Ph.D. in Electrical Engineering and M.S. in Applied Mathematics from Rensselaer Polytechnic Institute (RPI) in 2019, where she was advised by Dr. Meng Wang.
Research
Her research focuses on developing trustworthy AI algorithms for complex dynamic systems, with a particular emphasis on power grids. Her work spans physics-informed architectures, robust training with formal verification, and safe agentic AI system design. While her research is strongly motivated by the need to enhance power grid monitoring, control, and planning, the underlying optimization principles and algorithmic techniques are broadly applicable to many other domains.
Awards and Honors
- Best Paper Award
- DisrupTECH & UC–LANL Postdoc Entrepreneur Fellowship
- Founders Award of Excellence
Representative Publications
- Wenting Li, Russell Bent, Saif Kazi, Brian Wesley Bell, Duo Zhou, Huan Zhang, "E-Globe: Scalable ε-Global Verification of Neural Networks via Tight Upper Bounds and Pattern-Aware Branching", https://arxiv.org/abs/2602.05068, 2026.
- Mohamad Fares El Hajj Chehade, Wenting Li, Brian Wesley Bell, Russell Bent, Hao Zhu, "LEVIS: Large Exact Verifiable Input Spaces for Neural Networks", ICML, 2025
- Wenting Li, Deepjyoti Deka, Krishnamurthy (Dj) Dvijotham, "Physics-Constrained Interval Bound Propagation for Robustness Verifiable Neural Networks in Power Grids", AI for Energy Innovation Workshop in the 37th AAAI Conference, 2023.
- Wenting Li, Deepjyoti Deka, Ren Wang, Mario Arrieta Paternina, "Perturbation-Robust Neural Networks for Stochastic Power Grids", Artificial Intelligence on Transaction, 2023.
- Wenting Li, Deepjyoti Deka, "PPGN: Physics-Preserved Graph Networks for Fault Location with Limited Observation and Labels", Hawaii International Conference on System Sciences (HICSS), IEEE, 2023.
- Wenting Li, Deepjyoti Deka, "Physics-Informed Neural Networks for High Impedance Fault Detection", IEEE PowerTech, Madrid, Spain, 2021, pp. 1–6.
- Wenting Li, Deepjyoti Deka, "Physics-Informed Graph Neural Networks for Fault Location in Power Grids", ICML Climate Change Workshop, 2021, Best Paper Award.
- Wenting Li, Ming Yi, et al., "Real-time Energy Disaggregation at Substations with Behind-the-Meter Solar Generation", IEEE Transactions on Power Systems, vol. 36, no. 3, pp. 2023–2034, 2021.
- Wenting Li, Meng Wang, "Identifying Successive Events through a Shallow Convolutional Neural Network (CNN)", IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 4762–4772, 2019.
- Wenting Li, Deepjyoti Deka, Michael Chertkov, Meng Wang, "Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks", IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 4640–4651, 2019.