I develop uncertainty-aware optimization and decision-making methods for mobility and energy systems, with emphasis on EV-grid coordination, online control, and data-driven infrastructure operations. My work combines MILP, MPC, reinforcement learning, and multi-agent negotiation to make robust real-time decisions under uncertainty.
Online control under uncertain EV arrivals, building loads, and user behavior, balancing user utility with grid objectives.
Scenario generation, stochastic optimization, MCTS, and rolling-horizon MPC for real-time cyber-physical control.
Strategy-aware incentive mechanisms and negotiation frameworks that align individual agents with system-level goals.
Open-source toolchains for transit and mobility-energy planning: E-Transit-Bench, OPTIMUS, MoveOD.
Languages: Python, C++, SQL, Java, JavaScript, MATLAB, LaTeX
Web: D3.js, React, Angular · DevOps: Docker, Git, AWS (EC2/S3), Linux
Optimization: MILP (CPLEX, Gurobi), MPC, MDP, Game Theory
ML/RL: PyTorch, TensorFlow, Scikit-learn, DDPG/PPO, RLlib, MCTS
Transport: SUMO, BTE-Sim, MoveOD · Energy: GridLAB-D, E-Transit-Bench, OPTIMUS
Big Data: PySpark, Census processing, GIS
VLSI design, 3D printing, RC plane/drone construction, embedded systems (Arduino, ARM), microcontroller programming, sensor integration.
Outside the lab, I build things in both software and hardware. I design and fly RC planes and drones, use 3D printing for rapid prototyping, and bring a strong hardware foundation from my undergraduate background in Electronics and Communication Engineering. I also enjoy reading comics and participating in the maker community.