Tommaso Giovannelli

Tommaso Giovannelli is an Assistant Professor in the Industrial and Systems Engineering program within the Department of Mechanical and Materials Engineering at the University of Cincinnati. Previously, he was a postdoctoral researcher and adjunct faculty member at Lehigh University's Department of Industrial and Systems Engineering.

Education

• B.S. in Engineering Management, Sapienza University of Rome (2015)
• M.S. in Engineering Management, Sapienza University of Rome (2017)
• Ph.D. in Operations Research, Sapienza University of Rome (2021)

Research Interests

Giovannelli's research focuses on nonlinear optimization with applications in healthcare, transportation, and machine learning. Primary methodological interests include:

• Stochastic bilevel and multi-objective optimization
• Simulation-based optimization techniques
• Derivative-free and black-box optimization methods
• Machine learning optimization frameworks

His applied research targets healthcare management optimization, transportation system efficiency, and fairness in AI systems.

Publications

Recent publications demonstrate strong focus on bilevel optimization techniques, healthcare operations (especially emergency department management), transportation logistics, and sports analytics. Work frequently combines simulation with optimization and addresses real-world social impact challenges.

Awards

• Doctoral Award at ORAHS 2019 for simulation-based optimization research in emergency department management

Grants and Advising

Secured $500,000 from the Air Force Office of Scientific Research for optimization research. Advises undergraduate and graduate students on research projects in optimization and machine learning, with several co-authored publications in sports analytics and transportation. Organized major optimization conferences including MOPTA (2023-2025) and ICCOPT 2022.

Labs and Teams

Member of Lehigh University's Optimization and Machine Learning (OptML) Research Group. Current research team focuses on stochastic optimization methods and applications in social good domains.