Research Website

Educational Qualifications

Postdoctoral Scholar, Georgia Institute of Technology (2023-2024)
Ph.D., Chemical Engineering, University of Minnesota (2023)
Diploma, Chemical Engineering, Aristotle University of Thessaloniki, Greece (2018)

Focus

Artificial intelligence assisted optimization, control, and modeling of complex process systems.

Research

Our group is focused on developing efficient optimization and control algorithms for the solution of decision-making problems arising in the decarbonization of energy systems, resilient operation and design of supply chain networks, and the development of intelligent and autonomous systems. We combine tools from mathematical optimization, artificial intelligence, control theory, and chemical engineering. Current projects are focused on 1) using artificial intelligence to accelerate optimization algorithms, 2) supply chain management, and 3) design of hybrid surrogate models for complex process systems.

Awards & Honors

• CAST Directors' Student Presentation Award, Computing and Systems Technology Division of AIChE, 2023
• Doctoral Dissertation Fellowship, Graduate School, University of Minnesota, 2022
• Frank and Janis Bates Research Fellowship, University of Minnesota, 2018
• Limmat–Stiftung Foundation award, Aristotle University of Thessaloniki, 2018

Selected Publications

• Mitrai, I., and Daoutidis, P., 2023, A graph classification approach to determine when to decompose optimization problems, Comp Aided Chem Eng (Vol. 52, pp. 655-660), 2023
• Mitrai, I., and Daoutidis, P., A multicut Generalized Benders Decomposition approach for the integration of process operations and dynamic optimization for continuous systems, Comput Chem Eng, p.107859, 2022
• Mitrai, I., and Daoutidis, P., Learning to Initialize Generalized Benders Decomposition via Active Learning, Foundations of Computer Aided Process Operations / Chemical Process Control 2023, San Antonio TX, 2023
• Mitrai, I., Tang, W. and Daoutidis, P., Stochastic Blockmodeling for Learning the Structure of Optimization Problems, AIChE J, DOI: 10.1002/aic.17415l, 2022
• Mitrai, I., and Daoutidis, P., Efficient Solution of Enterprise-wide Optimization Problems Using Nested Stochastic Blockmodeling, Ind Eng Chem Res, 60(40), pp.14476-14494, 2021
• Tang, W., Allman, A., Mitrai, I. and Daoutidis, P., May. Resolving large-scale control and optimization through network structure analysis and decomposition: A tutorial review. In 2023 American Control Conference (ACC) (pp. 3113-3129). IEEE, 2023