[12]. Mitrai, I. and Daoutidis, P., 2025. Efficient Model Predictive Control Implementation via Machine Learning: An Algorithm Selection and Configuration Approach. Industrial & Engineering Chemistry Research. Invited article for Special Issue: AI/ML in Chemical Engineering. [link]
[11]. Mitrai, I., and Daoutidis, P., 2025, Accelerating process control and optimization via machine learning: A review, Reviews in Chemical Engineering [arXiV][link]
Prior to joining the University of Texas at Austin
[10]. Mitrai, I., and Daoutidis, P., 2024, Taking the human out of the decomposition-based optimization via artificial intelligence Part II: Learning to initialize, Computers & Chemical Engineering, p. 108686 [arXiv][link]
[9]. Mitrai, I., and Daoutidis, P., 2024, Taking the human out of the decomposition-based optimization via artificial intelligence Part I: Learning when to decompose, Computers & Chemical Engineering, p. 108688 [arXiv][link]
[8]. Mitrai, I. and Daoutidis, P., 2024. Computationally efficient solution of mixed integer model predictive control problems via machine learning aided Benders Decomposition. Journal of Process Control, 137, p.103207. [arXiv][link]
7. Mitrai, I., Palys, M., and Daoutidis, P., 2024. A two-stage stochastic programming approach for the design of renewable ammonia supply chain networks, Processes, 12(2), [link]
6. Mitrai, I., Jones, O. V., Dewantoro, H., Stamoulis, C., and Daoutidis, P., 2023, Internal Control of Brain Networks via Sparse Feedback. AIChE Journal, p.e18061. [link]
5. Palys M., Mitrai, I., and Daoutidis, P., 2023, Renewable hydrogen and ammonia for combined heat and power systems in remote locations: Optimal design and scheduling, Optimal Control Applications and Methods, https://doi.org/10.1002/oca.2793 [link]
4. Mitrai, I., and Daoutidis, P., 2022, A multicut Generalized Benders Decomposition approach for the integration of process operations and dynamic optimization for continuous systems, Computers and Chemical Engineering, p.107859 [link]
3. Mitrai, I., Tang, W. and Daoutidis, P., 2022, Stochastic Blockmodeling for Learning the Structure of Optimization Problems, AIChE Journal, DOI: 10.1002/aic.17415l. [link]
2. Mitrai, I., and Daoutidis, P., 2021, Efficient Solution of Enterprise-wide Optimization Problems Using Nested Stochastic Blockmodeling, Industrial & Engineering Chemistry Research, 60(40), pp.14476-14494. [link]
1. Mitrai, I., and Daoutidis, P., 2020, Decomposition of integrated scheduling and dynamic optimization problems using community detection, Journal of Process Control, 90, 63-74. [link]