CV
Kamiar Khayambashi
Ph.D. Candidate in Engineering
University of Virginia
Charlottesville, VA
Email: bmb2tn@virginia.edu
LinkedIn
GitHub
Education
Ph.D., Engineering — University of Virginia (Expected 2026)
Advisor: Negin Alemazkoor
M.Sc., Civil & Environmental Engineering — Sharif University of Technology
B.Sc., Civil Engineering — Isfahan University of Technology
Research & Professional Experience
Pacific Gas and Electric Company (PG&E) — Research Collaborator
Temporal graph neural networks for structure-level failure localization and cause prediction in transmission systems.
Maryland Department of Transportation (MDOT SHA) — Research Intern
Statewide GIS-based risk prioritization and multi-criteria optimization framework for stormwater infrastructure under climate stressors.
University of Virginia — Computational Analytics for Smart Systems Lab
Research Assistant
Selected work includes:
- Multi-agent reinforcement learning for post-hurricane grid restoration on >16,000-node distribution systems
- Multi-fidelity GNN surrogates for probabilistic power flow and chance-constrained OPF
- Capacity expansion modeling of data center growth and building electrification
- Large-ensemble uncertainty quantification for decarbonization planning
(Technical details available in Projects and Publications pages.)
Selected Publications
Khayambashi, K., Clarens, A. F., Shobe, W. M., & Alemazkoor, N. (2025).
Identifying key uncertainties in energy transitions: A Puerto Rico case study.
Nature Communications, 16(1), 9064.
Khayambashi, K., Hasnat, M. A., & Alemazkoor, N. (2024).
Hybrid chance-constrained optimal power flow under load and renewable generation uncertainty using enhanced multi-fidelity graph neural networks.
Journal of Machine Learning for Modeling and Computing, 5(4).
Taghizadeh, M., Khayambashi, K., Hasnat, M. A., & Alemazkoor, N. (2024).
Multi-fidelity graph neural networks for efficient power flow analysis.
Electric Power Systems Research, 237, 111014.
Khayambashi, K., & Alemazkoor, N. (2024).
Graph neural networks for precision-guaranteed compression of large-scale data.
Proceedings of the IEEE International Conference on Big Data.
Full publication list available on the Publications page.
Technical Skills
Machine Learning: Graph Neural Networks, Reinforcement Learning, Surrogate Modeling
Modeling & Optimization: Uncertainty Quantification, Monte Carlo Simulation, Chance-Constrained Optimization
Energy Systems: Power Flow, OPF, Capacity Expansion, Grid Resilience
Programming: Python, C++, SQL, MATLAB, VBA
Tools: PyTorch, PyTorch Geometric, TensorFlow, ArcGIS, CUDA
Awards
Science Synthesis Prize, U.S. DOE & NREL ($10,000)
SEAS Endowed Graduate Fellowship, University of Virginia
Environmental Futures Fellowship, University of Virginia
Selected Certifications
Deep Learning Specialization – DeepLearning.AI
Generative AI with Large Language Models – DeepLearning.AI & AWS
Fundamentals of GIS – UC Davis