Our lab develops data-driven mathematical optimization and machine learning methods to improve decision-making under uncertainty. We combine data-driven models with efficient algorithms to address complex planning and operational challenges involving limited resources and competing stakeholders. A central focus is decision-dependent uncertainty, where actions such as infrastructure investments or financial incentives influence the likelihood of future outcomes. Our methodological research spans stochastic programming, bilevel optimization, and customized decomposition and approximation algorithms, supported by machine learning and simulation.
Our applications include infrastructure protection, disaster preparedness and recovery, supply chain resilience, cyber and wireless network security, and renewable energy systems. We also study emerging transportation and logistics challenges, including drone delivery and electric vehicle deployment for emergency power restoration. Through these applications, we connect methodological advances with real-world needs, developing scalable solutions for complex systems. Our goal is to provide practical decision-support tools that help organizations allocate resources effectively, reduce risk, and sustain essential services under disruption.
September 15, 2025: Proposal on “Collaborative Research: MATH-DT: Computationally efficient hypercomplex variable-based sensitivity methods for rapid Digital Twin model updating” was funded by the National Science Foundation (NSF). Thanks to NSF.
January 15, 2025: Proposal on “Extension of Education, Training and Mentoring Program for NFU Students in Advanced Manufacturing” was funded by the National Formosa University (NFU). Thanks to NFU.
December 1, 2024: Dr. Bhuiyan has been awarded as the “Most Prolific Researcher of the Year” in the Mechanical, Aerospace, and Industrial Engineering Department.
May 22, 2024: Paper titled “A stochastic game-theoretic optimization approach for managing local electricity markets with electric vehicles and renewable sources” with PhD student Sayed Hamid Hosseini Dolatabadi won the Best Student Paper (2nd Prize) Award from the IISE Energy Systems Division in the IISE Annual Conference 2025.
December 13, 2023: Proposal on “Developing efficient optimization algorithms and decision-support tools for aerial drone routing” was funded by the U.S. Department of Energy (DOE). Thanks to the U.S. DOE.
February 1, 2023: Proposal on “Optimizing Mixed-fleet of Drones and Ground Vehicles for Efficient Delivery of Time-Sensitive Products” was funded by the U.S. Department of Energy (DOE) through Idaho National Laboratory. Thanks to the U.S. DOE.