Unmanned Air Traffic Management
Developing optimization models, algorithms, and simulation tools for safe and efficient management of unmanned aircraft systems (UAS) in shared airspace. Topics include strategic deconfliction, fleet routing, coverage path planning, and vertiport operations.
Optimization & Machine Learning
Building interpretable and high-performance machine learning models including decision trees (bsnsing), random forests (brif), and gene expression programming (gepR). Also includes p-center problems, covering problems, and combinatorial optimization.
Energy & Transportation Systems
Modeling and optimization of electricity markets, demand response, security-constrained economic dispatch, and smart grid operations. Earlier work in power systems; current focus on ground and air transportation infrastructure.
Research Demonstrations
Multi-agent Semi-cooperative UTM — Separation assurance model
Optimized Fleet Landing — Congested air traffic and vertiports
Minimum Covering Circle Algorithm — p-center problem in irregular areas
bsnsing — Decision tree induction with optimal Boolean rules
SAR Coverage Path Planning — UAV search and rescue simulation
Dense Aerial Traffic Motion Planning — 2D high-density traffic simulation
Software Tools
Selected Publications
View the complete publication list with BibTeX citations →
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