Scholar

Gonzalo E. Constante Flores

Google Scholar ID: KFCcT3MAAAAJ

Postdoctoral Scholar, Purdue University

Power systemsOptimizationMachine Learning

Citations & Impact

All-time

Citations

465

H-index

i10-index

Publications

Co-authors

Contact

Resume

Academic Achievements

Publications:
- A Quadratically-Constrained Convex Approximation for the AC Optimal Power Flow, 2025.
- Physics-informed neural networks with hard linear equality constraints, 2024, Computers & Chemical Engineering.
- Diagnosing infeasible optimization problems using large language models, 2024, INFOR: Information Systems and Operational Research.
- Daily scheduling of generating units with natural-gas market constraints, 2024, European Journal of Operational Research.
- An effective hybrid decomposition approach to solve the network-constrained stochastic unit commitment problem in large-scale power systems, 2024, EURO Journal on Computational Optimization.
Awards: Finalist for the 2024 IEEE PES PEEC Outstanding Doctoral Dissertation Award.

Research Experience

Currently a Postdoctoral Researcher at Purdue University, advised by Prof. Can Li. Formerly a PhD student at The Ohio State University.

Education

PhD in Electrical Engineering from The Ohio State University, 2022, Advisor: Prof. Antonio Conejo; MSc in Electrical Engineering from The Ohio State University; BSc in Electrical Engineering from Escuela Politécnica Nacional.

Background

Interests: Optimization, Machine Learning, Power Systems. Field: Developing theory, algorithms, and models for large-scale optimization and machine learning with applications in power and energy systems.