Published several papers, including 'A graph-empowered agent-based simulation: Impacts of coordination schemes on critical infrastructures resilience', 'A systematic review of optimization methods for recovery planning in cyber-physical infrastructure networks: Current state and future trends', 'Ensemble framework for causality learning with heterogeneous Directed Acyclic Graphs through the lens of optimization'.
Research Experience
Conducts research in optimization, machine learning, and decision science, particularly in scalable optimization for large-scale networks, learning-based solution algorithms for mixed-integer programming (MIP) and Quadratic Programming (QP) models. Also works on data-driven decision support systems (DSS) to address complex and uncertain problems across engineering, healthcare, and policy domains.
Education
Holds a Ph.D. in Systems Engineering and Operations Research from George Mason University.
Background
Postdoctoral Researcher at the Department of Systems Engineering and Operations Research at George Mason University. Combines research, teaching, and service to advance systems optimization and data-driven decision science. Develops data-driven methods in optimization, machine learning, and decision science to tackle complex societal challenges, from urban infrastructure to safety-critical systems.
Miscellany
Passionate about research, mentoring students, teaching courses in data analytics and operations research, and translating research into projects with broad impact.