Farzane Ezzati
Research Assistant & PhD Candidate
Contact
Farzane Ezzati
Research Assistant & PhD Candidate
I am Farzane Ezzati, a PhD student in Industrial Engineering at the University of Houston, working with Gino Lim, PhD, Xin Jiang, PhD. I earned my M.Sc. from Sharif University of Technology in 2020 and my B.Sc. from Amirkabir University of Technology in 2018.
My research focuses on:
My research focuses on:
- mathematical modeling,
- mixed-integer optimization algorithms,
- manifold optimization,
- optimization under uncertainty (stochastic programming, conditional value at risk),
- machine learning in application and for optimization.
I develop algorithms for large-scale and distributed mixed-integer problems and apply my work to real-world NP-Hard MIP problems, microgrid and power system planning and operation, addressing energy resilience and equity, and machine learning problems.
I am also actively collaborating on projects involving cutting-plane methods for mixed-integer programming, including Chvátal–Gomory cut generation, as well as beam angle optimization.
📍 I am currently looking for internship opportunities in Operations Research (modeling and algorithm development) and Data Science for Summer 2027.
I am also actively collaborating on projects involving cutting-plane methods for mixed-integer programming, including Chvátal–Gomory cut generation, as well as beam angle optimization.
📍 I am currently looking for internship opportunities in Operations Research (modeling and algorithm development) and Data Science for Summer 2027.
Projects
(2025) Riemannian ADMM for Binary Optimization
A Riemannian Manifold-Modified ADMM to Solve Large Scale/Distributed Binary Optimization Problems
(2025) Distributed Optimization for Energy Trading Problems
A resilience-cost-oriented energy trading model for socially vulnerable community microgrids and the solution approach.
(2024) Benders Decomposition for Stochastic Microgrid Planning
Stochastic 20-year microgrid planning model with Benders Decomposition
(2024) Seq2Seq Attention for Power Demand Prediction
GRU-based Seq2Seq model with attention to predict three quantiles of residential power demand in Texas
(2023) ML Classification for Cardiovascular Risk Prediction
Course project: ML classifiers (RF, SVM, LR, LDA) predicted cardiovascular disease with 72% accuracy
View all
Publications
Resilient Microgrid Planning for Socially Vulnerable Communities
Farzane Ezzati, Zhijie Sasha Dong, Gino Lim, Junfeng Jiao
Applied Energy, 2026
Farzane Ezzati, Qingyang Xiao, Zhijie Sasha Dong, Junfeng Jiao, Alyson Vargas, Vincent Yeh, Thomas Ptak, Kai Pan
Communications Earth & Environment, vol. 6, Nature Publishing Group UK London, 2025, p. 294
Energy equity-centered planning of community microgrids
Behnam Sabzi, Jian Shi, Gino Lim, Farzane Ezzati, Kailai Wang
Sustainable Cities and Society, vol. 130, 2025, p. 106485
Equitable Energy Trading in Microgrids to Enhance Resilience and Cost Efficiency
Farzane Ezzati, Gino Lim, Zhijie Sasha Dong
2025 IEEE International Communications Energy Conference (INTELEC), IEEE, 2025 29, pp. 49-54