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Lab Overview
Lab Overview
Research at the D3A lab focuses on developing data-driven optimization and data analytics methods for solving complex decision-making problems under uncertainty and in game-theoretic settings. Specifically, the research methodology has two main branches: (1) data-driven mathematical optimization under uncertainty that integrates machine learning models in designing efficient decomposition algorithms; (2) Machine learning (ML), data analytics, and ML-based optimization (i.e., Bayesian active learning, Bayesian optimization) for complex black-box functions in extremely data-scarce environments.
Applications of these methods at D3A span critical infrastructure protection, disaster preparedness and recovery, supply chain resilience, security of cyber-physical systems, new materials design and discovery, cyber and wireless network security, and power systems resiliency and operations with distributed energy resources. Several of these areas involve close collaborative research with DOE National Laboratories, including Idaho, Oak Ridge, and Argonne National Labs. Our goal is to provide practical decision-support tools that help organizations allocate resources effectively, reduce risk, and sustain essential services under disruption.
Top News
Top News
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 National Formosa University (NFU). Thanks to NFU.
December 1, 2024: Dr. Bhuiyan was 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 2024.
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.
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