Mikhail A. Bragin

 

Mikhail A. Bragin

Assistant Research Professor

Ph.D., University of Connecticut (2016)

Phone: (860) 486-1801

Email: mikhail.bragin@uconn.edu

      

Overview

I hold a courtesy appointment as an Assistant Research Professor in the Department of Electrical and Computer Engineering at the University of Connecticut. I hold 5 academic degrees (1 Bachelor’s, 3 Master’s, and 1 Ph.D.), I have 34 journal publications across several disciplines: Power Systems, Operations Research, Information Systems, Robotics and Automation, Mathematical Optimization, and Environmental Engineering, I was nominated 6 times for the best paper award, I received 1 recognition of teaching excellence, and I secured or helped secure ~$3M in external funding (~$2 as PI/Co-PI). My research work has been supported by the U.S. National Science Foundation, BNL, MISO, ISO-NE, ABB, CESMII, and AFRL. My research is geared toward solving complex technical and societal challenges within smart grids, supply chains, and artificial intelligence. Accordingly, my research interests include operations research, mathematical optimization, including power system optimization, grid integration of renewables (wind and solar), energy-based operation optimization of distributed energy systems, stochastic scheduling within manufacturing systems, and machine learning through deep neural networks. My research has appeared in top journals such as the Journal of Optimization Theory and Applications, Decision Support Systems, IEEE Transactions on Power Systems, IEEE Transactions on Automation Science and Engineering, and IEEE Robotics and Automation Letters as well as in top conferences such as INFORMS Annual Meeting, INFORMS Computing Society Conference, INFORMS Optimization Society Conference and International Joint Conference on Artificial Intelligence.

Update: I am currently a full-time employee at Southern California Edison.

Updated: 08/31/2023

Selected Publications by Discipline

Artificial Intelligence and Machine Learning:
1. A.-B. Liu, P. B. Luh, K. Sun, M. A. Bragin, and B. Yan, “Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling,” accepted to IEEE Transactions on Automation Science and Engineering.

2. S. Zhou, M. A. Bragin, L. Pepin, D. Gurevin, C. Ding, and F. Miao, “Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning,” accepted to Transactions on Design Automation of Electronic Systems

3. J. Wu, P. B. Luh, Y. Chen, M. A. Bragin, and B. Yan, “Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment,” accepted to IEEE Transactions on Power Systems.

4. W. Wan, P. Zhang, M. A. Bragin, and P. B. Luh, “Safety-Assured, Real-Time Neural Active Fault Management for Resilient Microgrids Integration,” accepted to iEnergy. DOI: 10.23919/IEN.2022.0048

5. S. Zhou, X. Xu, M. A. Bragin, and J. Bai, “Combining Multi-view Ensemble and Surrogate Lagrangian Relaxation for Real-time 3D Biomedical Image Segmentation on the Edge,” Neurocomputing, Volume 512, November 2022, pp. 466 – 481. DOI: 10.1016/j.neucom.2022.09.039

6. D. Zhdanov, S. Bhattacharjee, and M. A. Bragin, “Incorporating FAT and Privacy-Aware AI Modeling Approaches into Business Decision Making Frameworks,” Decision Support Systems, Volume 155, April 2022, 113715. DOI: 10.1016/j.dss.2021.113715.

7. Z. Wang, B. Li, X. Xiao, T. Zhang, M. A. Bragin, B. Yan, C. Ding and S. Rajasekaran, “Automatic Subnetwork Search Through Dynamic Differentiable Neuron Pruning,” accepted to ISQED 2023

8. D. Gurevin‡, M. A. Bragin‡, C. Ding‡, S. Zhou, L. Pepin, B. Li, and F. Miao, “Enabling Retrain-Free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation,” Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21), pp. 2497-2504. DOI: 10.24963/ijcai.2021/344 (Acceptance rate: 13.9%)

Quantum Computing:
1. F. Feng, P. Zhang, M. A. Bragin, and Y. Zhou, “Novel Resolution of Unit Commitment Problems through Quantum Surrogate Lagrangian Relaxation,” accepted to IEEE Transactions on Power Systems. DOI: 10.1109/TPWRS.2022.3181221.

2. N. Nikmehr, P. Zhang, and M. A. Bragin, “Quantum Distributed Unit Commitment: An Application in Microgrids,” IEEE Transactions on Power Systems, Volume 37, Issue 5, September 2022, pp. 3592 – 3603. DOI: 10.1109/TPWRS.2022.3141794.

3. N. Nikmehr, P. Zhang, and M. A. Bragin, “Quantum-Enabled Distributed Unit Commitment,” to appear in Proceedings of the IEEE PES 2022 General Meeting (Best Paper Session)

Mathematical Optimization/Operations Research:

1. M. A. Bragin, “Survey on Lagrangian Relaxation for MILP: Importance, Challenges, Historical Review, Recent Advancements, and Opportunities,” accepted to Annals of Operations Research

2. M. A. Bragin, and E. L. Tucker, “Surrogate “Level-Based” Lagrangian Relaxation for Mixed-Integer Linear Programming,” Scientific Reports, Volume 12, Issue 1, 22417. DOI: 10.1038/s41598-022-26264-1

3. M. A. Bragin, P. B. Luh, and B. Yan, “Distributed and Asynchronous Coordination of a Mixed-Integer Linear System via Surrogate Lagrangian Relaxation,” IEEE Transaction on Automation Science and Engineering, Volume 18, Issue 4, June 2020, pp. 1191 – 1205. DOI: 10.1109/TASE.2020.2998048.

4. M. A. Bragin, P. B. Luh, B. Yan, and X. Sun, “A Scalable Solution Methodology for Mixed-Integer Linear Programming Problems Arising in Automation,” IEEE Transaction on Automation Science and Engineering, Volume 16, Issue 2, April 2019, pp. 531 – 541. DOI: 10.1109/TASE.2018.2835298. (2020 Best Transactions Paper Honorable Mention)

5. M. A. Bragin, P. B. Luh, J. H. Yan, and G. A. Stern, “An Efficient Approach for Solving Mixed-Integer Programming Problems under the Monotonic Condition,” Journal of Control and Decision, Volume 3, Issue 1, January 2016, pp. 44 – 67. DOI: 10.1080/23307706.2015.1129916

6. M. A. Bragin, P. B. Luh, J. H. Yan, N. Yu, and G. A. Stern, “Convergence of the Surrogate Lagrangian Relaxation Method,” Journal of Optimization Theory and Applications, Volume 164, Issue 1, 2015, pp. 173 – 201. DOI: 10.1007/s10957-014-0561-3.

Manufacturing:
1. A.-B. Liu, P. B. Luh, K. Sun, M. A. Bragin, and B. Yan, “Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling,” accepted to IEEE Transactions on Automation Science and Engineering.

2. T.-H. Tsai, B. Yan, P. B. Luh, H.-C. Yang, M. A. Bragin, and F.-T. Cheng, “Near-Optimal Scheduling of IC Packaging Operations Considering Processing-Time Variations and Factory Practice,” accepted to IEEE Robotics and Automation Letters.

3. Y. Sun, J. Tu, M. A. Bragin, and L. Zhang, “A Simulation-based Integrated Virtual Testbed for Dynamic Optimization in Smart Manufacturing Systems,” Journal of Advanced Manufacturing and Processing, 2022, e10141. DOI: 10.1002/amp2.10141.

4. A.-B. Liu, P. B. Luh, B. Yan, and M. A. Bragin, “A Novel Integer Linear Formulation for Job-shop Scheduling Problems,” IEEE Robotics and Automation Letters, Volume 6, Issue 3, June 2021, pp. 5937 – 5944. DOI: 10.1109/LRA.2021.3086422.

5. B. Yan, M. A. Bragin, and P. B. Luh, “An Innovative and Systematic Formulation Tightening Method for Job-Shop Scheduling,” IEEE Transactions on Automation Science and Engineering, Volume 19, Issue 3, June 2021, pp. 2526 – 2539. DOI: 10.1109/TASE.2021.3088047.

6. A.-B. Liu, P. B. Luh, M. A. Bragin, and B. Yan, “Ordinal-Optimization Concept Enabled Decomposition and Coordination of Mixed-Integer Linear Programming Problems,” IEEE Robotics and Automation Letters, Volume 5, Issue 4, October 2020, pp. 5051 – 5058. DOI: 10.1109/LRA.2020.3005125.

7. B. Yan‡, M. A. Bragin‡, and P. B. Luh, “Novel Formulation and Resolution of Job-Shop Scheduling Problems,” IEEE Robotics and Automation Letters, Volume 3, Issue 4, October 2018, pp. 3387 – 3393. DOI: 10.1109/LRA.2018.2850056

Power Systems:
1. J. Wu, P. B. Luh, Y. Chen, M. A. Bragin, and B. Yan, “Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment,” accepted to IEEE Transactions on Power Systems.

2. W. Wan, P. Zhang, M. A. Bragin, and P. B. Luh, “Safety-Assured, Real-Time Neural Active Fault Management for Resilient Microgrids Integration,” accepted to iEnergy. DOI: 10.23919/IEN.2022.0048

3. Y. Chen, F. Pan, F. Qiu, A. S. Xavier, T. Zheng, M. Marwali, B. Knueven, Y. Guan, P. Luh, L. Wu, B. Yan, M. A. Bragin, H. Zhong, A. Giacomoni, R. Baldick, B. Gisin, Q. Gu, R. Philbrick, and F. Li “Security-Constrained Unit Commitment for Electricity Market: Modeling, Solution Methods, and Future Challenges,” accepted to IEEE Transactions on Power Systems. DOI: 10.1109/TPWRS.2022.3213001.

4. N. Nikmehr, M. A. Bragin, P. B. Luh, and P. Zhang, “Computationally Distributed and Asynchronous Operational Optimization of Droop-Controlled Networked Microgrids,” IEEE Open Access Journal of Power and Energy, Volume 9, July 2022, pp. 265 – 277. DOI: 10.1109/OAJPE.2022.3188733.

5. F. Feng, P. Zhang, M. A. Bragin, and Y. Zhou, “Novel Resolution of Unit Commitment Problems through Quantum Surrogate Lagrangian Relaxation,” accepted to IEEE Transactions on Power Systems. DOI: 10.1109/TPWRS.2022.3181221.

6. W. Wan, P. Zhang, M. A. Bragin, and P. B. Luh, “Cooperative Fault Management for Resilient Integration of Renewable Energy,” Electric Power Systems Research, Volume 211, October 2022, 108147. DOI: 10.1016/j.epsr.2022.108147.

7. M. A. Bragin, B. Yan, A. Kumar, N. Yu, and P. Zhang, “Efficient Operations of Micro-Grids with Meshed Topology and Under Uncertainty through Exact Satisfaction of AC-PF, Droop Control and Tap-Changer Constraints,” Energies, 2022, Volume 15, Issue 10, 3662. DOI: 10.3390/en15103662.

8. N. Raghunathan, M. A. Bragin, B. Yan, P. B. Luh, K. Moslehi, X. Feng, Y. Yu, C.-N. Yu, and C.-C. Tsai, “Exploiting Soft Constraints within Decomposition and Coordination Methods for Sub-Hourly Unit Commitment,” International Journal of Electrical Power & Energy Systems, Volume 139, July 2022, 108023. DOI: 10.1016/j.ijepes.2022.108023.

9. M. A. Bragin, and Y. Dvorkin, “TSO-DSO Operational Planning Coordination through “l1-Proximal” Surrogate Lagrangian Relaxation,” IEEE Transactions on Power Systems, Volume 37, Issue 2, March 2022, pp. 1274 – 1285. DOI: 10.1109/TPWRS.2021.3101220.

10. N. Nikmehr, P. Zhang, and M. A. Bragin, “Quantum Distributed Unit Commitment: An Application in Microgrids,” IEEE Transactions on Power Systems, Volume 37, Issue 5, September 2022, pp. 3592 – 3603. DOI: 10.1109/TPWRS.2022.3141794.

11. J. Wu, P. B. Luh, Y. Chen, M. A. Bragin, and B. Yan, “A Novel Optimization Approach for Sub-Hourly Unit Commitment with Large Numbers of Generators and Virtual Transactions,” IEEE Transactions on Power Systems, Volume 37, Issue 5, September 2022, pp. 3716 – 3725. DOI: 10.1109/TPWRS.2021.3137842.

Environmental Science:

1. X. Wang, S. Sahoo, J. Gascon, M. A. Bragin, F. Liu, J. Olchowski, S. Rothfarb, Y. Huang, W. Xiang, P. Gao, P. Alpay, and B. Li, “Deciphering Electrochemical Interactions in Metal-Polymer Catalysts for CO2 Reduction,” accepted to Energy and Environmental Science

 

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