Journal articles
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The most up-to-date publication list is available on Google Scholar.
Under review
Tongjia Liu and Ilias Mitrai. Symbolic Discovery of Iterative Algorithms: A Continuous Latent Space Bayesian Optimization Framework, arXiv preprint arXiv:2607.01552, 2026
Jiyong Lee, Erhan Kutanoglu, Michael Baldea, and Ilias Mitrai. Industrial electrification in the era of data centers: A Bayesian Optimization approach for grid-aware large load allocation, arXiv preprint arXiv:2606.23452, 2026
Ilias Mitrai and Wentao Tang. A constrained symbolic regression approach for Lyapunov function discovery. arXiv preprint arXiv:2606.10045, 2026
Jiyong Lee, Melody Agustin, Joanne Langsdorf, Erhan Kutanoglu, Michael Baldea, and Ilias Mitrai. Grid capacity expansion under data centers and electrified manufacturing large loads. arXiv preprint arXiv:2605.29053, 2026
Ilias Mitrai, Tongjia Liu, and Gabriel E Sanoja. Learning regime-dependent governing equations: A symbolic decision tree approach. arXiv preprint arXiv:2605.24275, 2026
Michael Baldea, Linda J. Broadbelt, Marianthi G. Ierapetritou, Akhilesh Jain, Ankur Kumar, Thomas A. Kwan, F`elix Llovell, Andrew J. Medford, Ilias Mitrai, Joel Paulson, Junyi Qiao, Matt Rivera, Kirti C. Sahu, Lev Sarkisov, Zachary P. Smith, Calvin Tsay, Ching-Mei Wen, Victor M. Zavala, Huacheng Zhang, and Dan Zhao. Atoms to processes: The role of artificial intelligence and machine learning in chemical engineering. 2026
Zhe Li and Ilias Mitrai. Learning interpretable and stable dynamical models via mixed-integer Lyapunov-constrained optimization. arXiv preprint arXiv:2604.07611, 2026
Bernard T. Agyeman, Zhe Li, Ilias Mitrai, and Prodromos Daoutidis. Feasibility-aware imitation learning for benders decomposition. arXiv preprint arXiv:2604.04801, 2026
Publications as an Assistant Professor after joining UT Austin
16. Agyeman, B.T., Li, Z., Mitrai, I. and Daoutidis, P., 2026. Graph‐based imitation and reinforcement learning for efficient Benders decomposition. AIChE Journal, 72(6), p.e70342.
15. Lim, J., Mitrai, I., Daoutidis, P. and Stamoulis, C., 2025, The adolescent functional connectome is dynamically controlled by a sparse core of cognitive and topological hubs. NeuroImage, 322:121562, 2025
14. Li, Z., Agyeman, B.T., Mitrai, I. and Daoutidis, P., 2025. Learning to control inexact Benders decomposition via reinforcement learning. Computers & Chemical Engineering, p.109461.
13. Mitrai, I., Palys, M.J. and Daoutidis, P., 2025. A multistage stochastic programming approach for renewable ammonia supply chain network design. Computers & Chemical Engineering, p.109443.
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]
Publications 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]