{"id":5,"date":"2020-06-23T13:03:17","date_gmt":"2020-06-23T13:03:17","guid":{"rendered":"http:\/\/www.ee.uconn.edu\/mikhail-a-bragin\/?page_id=5"},"modified":"2023-09-01T19:59:03","modified_gmt":"2023-09-01T19:59:03","slug":"home","status":"publish","type":"page","link":"https:\/\/www.ee.uconn.edu\/mikhail-a-bragin\/","title":{"rendered":"Mikhail A. Bragin"},"content":{"rendered":"
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 <\/p>\n

Mikhail A. Bragin<\/span><\/strong><\/p>\n

Assistant Research Professor<\/span><\/p>\n

Ph.D., University of Connecticut (2016)<\/span><\/p>\n

Phone: (860) 486-1801<\/span><\/p>\n

Email: mikhail.bragin@uconn.edu<\/span><\/p>\n

\"\"<\/a>\u00a0 \u00a0\"\"<\/a>\u00a0 \u00a0\"\"<\/a> \"\"<\/a> \"\"<\/a><\/p>\n

Overview<\/strong><\/p>\n

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\u2019s, 3 Master\u2019s, 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.<\/p>\n

Update: I am currently a full-time employee at Southern California Edison.<\/p>\n

Updated: 08\/31\/2023<\/p>\n

Selected Publications by Discipline<\/strong><\/p>\n

Artificial Intelligence and Machine Learning:<\/strong>
\n1. A.-B. Liu, P. B. Luh, K. Sun, M. A. Bragin, and B. Yan, \u201cIntegrating Machine Learning and Mathematical Optimization for Job Shop Scheduling,\u201d accepted to IEEE Transactions on Automation Science and Engineering.<\/p>\n

2. S. Zhou, M. A. Bragin, L. Pepin, D. Gurevin, C. Ding, and F. Miao, \u201cSurrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning,\u201d accepted to Transactions on Design Automation of Electronic Systems<\/p>\n

3. J. Wu, P. B. Luh, Y. Chen, M. A. Bragin, and B. Yan, \u201cSynergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment,\u201d accepted to IEEE Transactions on Power Systems.<\/p>\n

4. W. Wan, P. Zhang, M. A. Bragin, and P. B. Luh, \u201cSafety-Assured, Real-Time Neural Active Fault Management for Resilient Microgrids Integration,\u201d accepted to iEnergy. DOI: 10.23919\/IEN.2022.0048<\/p>\n

5. S. Zhou, X. Xu, M. A. Bragin, and J. Bai, \u201cCombining Multi-view Ensemble and Surrogate Lagrangian Relaxation for Real-time 3D Biomedical Image Segmentation on the Edge,\u201d Neurocomputing, Volume 512, November 2022, pp. 466 \u2013 481. DOI: 10.1016\/j.neucom.2022.09.039<\/p>\n

6. D. Zhdanov, S. Bhattacharjee, and M. A. Bragin, \u201cIncorporating FAT and Privacy-Aware AI Modeling Approaches into Business Decision Making Frameworks,\u201d Decision Support Systems, Volume 155, April 2022, 113715. DOI: 10.1016\/j.dss.2021.113715.<\/p>\n

7. Z. Wang, B. Li, X. Xiao, T. Zhang, M. A. Bragin, B. Yan, C. Ding and S. Rajasekaran, \u201cAutomatic Subnetwork Search Through Dynamic Differentiable Neuron Pruning,\u201d accepted to ISQED 2023<\/p>\n

8. D. Gurevin\u2021, M. A. Bragin\u2021, C. Ding\u2021, S. Zhou, L. Pepin, B. Li, and F. Miao, \u201cEnabling Retrain-Free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation,\u201d 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%)<\/p>\n

Quantum Computing:<\/strong>
\n1. F. Feng, P. Zhang, M. A. Bragin, and Y. Zhou, \u201cNovel Resolution of Unit Commitment Problems through Quantum Surrogate Lagrangian Relaxation,\u201d accepted to IEEE Transactions on Power Systems. DOI: 10.1109\/TPWRS.2022.3181221.<\/p>\n

2. N. Nikmehr, P. Zhang, and M. A. Bragin, \u201cQuantum Distributed Unit Commitment: An Application in Microgrids,\u201d IEEE Transactions on Power Systems, Volume 37, Issue 5, September 2022, pp. 3592 \u2013 3603. DOI: 10.1109\/TPWRS.2022.3141794.<\/p>\n

3. N. Nikmehr, P. Zhang, and M. A. Bragin, \u201cQuantum-Enabled Distributed Unit Commitment,\u201d to appear in Proceedings of the IEEE PES 2022 General Meeting (Best Paper Session)<\/p>\n

Mathematical Optimization\/Operations Research:<\/strong><\/p>\n

1. M. A. Bragin, \u201cSurvey on Lagrangian Relaxation for MILP: Importance, Challenges, Historical Review, Recent Advancements, and Opportunities,\u201d accepted to Annals of Operations Research<\/p>\n

2. M. A. Bragin, and E. L. Tucker, \u201cSurrogate \u201cLevel-Based\u201d Lagrangian Relaxation for Mixed-Integer Linear Programming,\u201d Scientific Reports, Volume 12, Issue 1, 22417. DOI: 10.1038\/s41598-022-26264-1<\/p>\n

3. M. A. Bragin, P. B. Luh, and B. Yan, \u201cDistributed and Asynchronous Coordination of a Mixed-Integer Linear System via Surrogate Lagrangian Relaxation,\u201d IEEE Transaction on Automation Science and Engineering, Volume 18, Issue 4, June 2020, pp. 1191 \u2013 1205. DOI: 10.1109\/TASE.2020.2998048.<\/p>\n

4. M. A. Bragin, P. B. Luh, B. Yan, and X. Sun, \u201cA Scalable Solution Methodology for Mixed-Integer Linear Programming Problems Arising in Automation,\u201d IEEE Transaction on Automation Science and Engineering, Volume 16, Issue 2, April 2019, pp. 531 \u2013 541. DOI: 10.1109\/TASE.2018.2835298. (2020 Best Transactions Paper Honorable Mention)<\/p>\n

5. M. A. Bragin, P. B. Luh, J. H. Yan, and G. A. Stern, \u201cAn Efficient Approach for Solving Mixed-Integer Programming Problems under the Monotonic Condition,\u201d Journal of Control and Decision, Volume 3, Issue 1, January 2016, pp. 44 \u2013 67. DOI: 10.1080\/23307706.2015.1129916<\/p>\n

6. M. A. Bragin, P. B. Luh, J. H. Yan, N. Yu, and G. A. Stern, \u201cConvergence of the Surrogate Lagrangian Relaxation Method,\u201d Journal of Optimization Theory and Applications, Volume 164, Issue 1, 2015, pp. 173 \u2013 201. DOI: 10.1007\/s10957-014-0561-3.<\/p>\n

Manufacturing:<\/strong>
\n1. A.-B. Liu, P. B. Luh, K. Sun, M. A. Bragin, and B. Yan, \u201cIntegrating Machine Learning and Mathematical Optimization for Job Shop Scheduling,\u201d accepted to IEEE Transactions on Automation Science and Engineering.<\/p>\n

2. T.-H. Tsai, B. Yan, P. B. Luh, H.-C. Yang, M. A. Bragin, and F.-T. Cheng, \u201cNear-Optimal Scheduling of IC Packaging Operations Considering Processing-Time Variations and Factory Practice,\u201d accepted to IEEE Robotics and Automation Letters.<\/p>\n

3. Y. Sun, J. Tu, M. A. Bragin, and L. Zhang, \u201cA Simulation-based Integrated Virtual Testbed for Dynamic Optimization in Smart Manufacturing Systems,\u201d Journal of Advanced Manufacturing and Processing, 2022, e10141. DOI: 10.1002\/amp2.10141.<\/p>\n

4. A.-B. Liu, P. B. Luh, B. Yan, and M. A. Bragin, \u201cA Novel Integer Linear Formulation for Job-shop Scheduling Problems,\u201d IEEE Robotics and Automation Letters, Volume 6, Issue 3, June 2021, pp. 5937 \u2013 5944. DOI: 10.1109\/LRA.2021.3086422.<\/p>\n

5. B. Yan, M. A. Bragin, and P. B. Luh, \u201cAn Innovative and Systematic Formulation Tightening Method for Job-Shop Scheduling,\u201d IEEE Transactions on Automation Science and Engineering, Volume 19, Issue 3, June 2021, pp. 2526 \u2013 2539. DOI: 10.1109\/TASE.2021.3088047.<\/p>\n

6. A.-B. Liu, P. B. Luh, M. A. Bragin, and B. Yan, \u201cOrdinal-Optimization Concept Enabled Decomposition and Coordination of Mixed-Integer Linear Programming Problems,\u201d IEEE Robotics and Automation Letters, Volume 5, Issue 4, October 2020, pp. 5051 \u2013 5058. DOI: 10.1109\/LRA.2020.3005125.<\/p>\n

7. B. Yan\u2021, M. A. Bragin\u2021, and P. B. Luh, \u201cNovel Formulation and Resolution of Job-Shop Scheduling Problems,\u201d IEEE Robotics and Automation Letters, Volume 3, Issue 4, October 2018, pp. 3387 \u2013 3393. DOI: 10.1109\/LRA.2018.2850056<\/p>\n

Power Systems:<\/strong>
\n1. J. Wu, P. B. Luh, Y. Chen, M. A. Bragin, and B. Yan, \u201cSynergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment,\u201d accepted to IEEE Transactions on Power Systems.<\/p>\n

2. W. Wan, P. Zhang, M. A. Bragin, and P. B. Luh, \u201cSafety-Assured, Real-Time Neural Active Fault Management for Resilient Microgrids Integration,\u201d accepted to iEnergy. DOI: 10.23919\/IEN.2022.0048<\/p>\n

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 \u201cSecurity-Constrained Unit Commitment for Electricity Market: Modeling, Solution Methods, and Future Challenges,\u201d accepted to IEEE Transactions on Power Systems. DOI: 10.1109\/TPWRS.2022.3213001.<\/p>\n

4. N. Nikmehr, M. A. Bragin, P. B. Luh, and P. Zhang, \u201cComputationally Distributed and Asynchronous Operational Optimization of Droop-Controlled Networked Microgrids,\u201d IEEE Open Access Journal of Power and Energy, Volume 9, July 2022, pp. 265 \u2013 277. DOI: 10.1109\/OAJPE.2022.3188733.<\/p>\n

5. F. Feng, P. Zhang, M. A. Bragin, and Y. Zhou, \u201cNovel Resolution of Unit Commitment Problems through Quantum Surrogate Lagrangian Relaxation,\u201d accepted to IEEE Transactions on Power Systems. DOI: 10.1109\/TPWRS.2022.3181221.<\/p>\n

6. W. Wan, P. Zhang, M. A. Bragin, and P. B. Luh, \u201cCooperative Fault Management for Resilient Integration of Renewable Energy,\u201d Electric Power Systems Research, Volume 211, October 2022, 108147. DOI: 10.1016\/j.epsr.2022.108147.<\/p>\n

7. M. A. Bragin, B. Yan, A. Kumar, N. Yu, and P. Zhang, \u201cEfficient Operations of Micro-Grids with Meshed Topology and Under Uncertainty through Exact Satisfaction of AC-PF, Droop Control and Tap-Changer Constraints,\u201d Energies, 2022, Volume 15, Issue 10, 3662. DOI: 10.3390\/en15103662.<\/p>\n

8. N. Raghunathan, M. A. Bragin, B. Yan, P. B. Luh, K. Moslehi, X. Feng, Y. Yu, C.-N. Yu, and C.-C. Tsai, \u201cExploiting Soft Constraints within Decomposition and Coordination Methods for Sub-Hourly Unit Commitment,\u201d International Journal of Electrical Power & Energy Systems, Volume 139, July 2022, 108023. DOI: 10.1016\/j.ijepes.2022.108023.<\/p>\n

9. M. A. Bragin, and Y. Dvorkin, \u201cTSO-DSO Operational Planning Coordination through \u201cl<\/em>1<\/sub>-Proximal\u201d Surrogate Lagrangian Relaxation,\u201d IEEE Transactions on Power Systems, Volume 37, Issue 2, March 2022, pp. 1274 \u2013 1285. DOI: 10.1109\/TPWRS.2021.3101220.<\/p>\n

10. N. Nikmehr, P. Zhang, and M. A. Bragin, \u201cQuantum Distributed Unit Commitment: An Application in Microgrids,\u201d IEEE Transactions on Power Systems, Volume 37, Issue 5, September 2022, pp. 3592 \u2013 3603. DOI: 10.1109\/TPWRS.2022.3141794.<\/p>\n

11. J. Wu, P. B. Luh, Y. Chen, M. A. Bragin, and B. Yan, \u201cA Novel Optimization Approach for Sub-Hourly Unit Commitment with Large Numbers of Generators and Virtual Transactions,\u201d IEEE Transactions on Power Systems, Volume 37, Issue 5, September 2022, pp. 3716 \u2013 3725. DOI: 10.1109\/TPWRS.2021.3137842.<\/p>\n

Environmental Science:<\/strong><\/p>\n

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, \u201cDeciphering Electrochemical Interactions in Metal-Polymer Catalysts for CO2 Reduction,\u201d accepted to Energy and Environmental Science<\/p>\n

 <\/p>\n

Publications<\/span><\/span><\/a><\/p>\n

Teaching<\/span><\/span><\/a><\/p>\n

\"Flag<\/a><\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"

  Mikhail A. Bragin Assistant Research Professor Ph.D., University of Connecticut (2016) Phone: (860) 486-1801 Email: mikhail.bragin@uconn.edu \u00a0 \u00a0\u00a0 \u00a0 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\u2019s, 3 Master\u2019s, and 1 […]<\/p>\n","protected":false},"author":71,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-5","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.ee.uconn.edu\/mikhail-a-bragin\/wp-json\/wp\/v2\/pages\/5","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ee.uconn.edu\/mikhail-a-bragin\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.ee.uconn.edu\/mikhail-a-bragin\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.ee.uconn.edu\/mikhail-a-bragin\/wp-json\/wp\/v2\/users\/71"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ee.uconn.edu\/mikhail-a-bragin\/wp-json\/wp\/v2\/comments?post=5"}],"version-history":[{"count":151,"href":"https:\/\/www.ee.uconn.edu\/mikhail-a-bragin\/wp-json\/wp\/v2\/pages\/5\/revisions"}],"predecessor-version":[{"id":250,"href":"https:\/\/www.ee.uconn.edu\/mikhail-a-bragin\/wp-json\/wp\/v2\/pages\/5\/revisions\/250"}],"wp:attachment":[{"href":"https:\/\/www.ee.uconn.edu\/mikhail-a-bragin\/wp-json\/wp\/v2\/media?parent=5"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}