Dr. Hadi is an optimization engineer with extensive experience developing mathematical models for complex planning, operational, and market-facing decisions. His work spans long-horizon system planning, real-time and near-real-time operational optimization, and decision frameworks designed for settings where cost, performance, and uncertainty must all be managed together. In addition to solver-based optimization models, his work has included the development of heuristic methods to support practical decision-making in challenging real-world systems. 

• Project experience : Dr. Hadi has been the technical lead on projects including but not limited to large-scale planning models, operational dispatch and control, market-oriented optimization, electrification and infrastructure analysis, and post-optimization risk assessment. His work has included microgrid dispatch optimization, distributed energy resource modeling, production-cost and market modeling, and long-horizon capacity expansion. 
• Optimization Methods: Mixed-integer programming, two-stage stochastic optimization, robust optimization, scenario-based analysis, model predictive control, near-optimal alternative generation, and heuristic algorithm development. 
• Technical Tools: Python, Pyomo, Pandas, NumPy, Gurobi, CPLEX, and a range of optimization and energy systems modeling platforms including Reopt, MESSAGE, ReEDS, LEAP, OSeMOSYS, and Aurora. 

A distinguishing feature of Dr. Hadi’s work is the way optimization is combined with risk and sensitivity assessment to move beyond a single “best” answer. His work in both academic and industry settings has built a strong record of combining technical rigor with practical relevance. That foundation has shaped a career focused on developing optimization and decision-support methods that are analytically sound, implementable, and responsive to real-world complexity.