MERL - Mitsubishi Electric Research Laboratories

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Optimization

Efficient solutions to large-scale problems.

Much of MERL's research activity involves formulating scientific and engineering problems as optimizations, which can be solved in an efficient way. We have developed fundamental algorithms to better solve classic problems, such as quadratic programs and minimum-cost paths. Our work also involves developing theoretical bounds to understand performance limits.

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  • Researchers

  • Awards

    •  AWARD   Best conference paper of IEEE PES-GM 2020
      Date: June 18, 2020
      Awarded to: Tong Huang, Hongbo Sun, K.J. Kim, Daniel Nikovski, Le Xie
      MERL Contacts: Kyeong Jin (K.J.) Kim; Daniel Nikovski; Hongbo Sun
      Research Areas: Data Analytics, Electric Systems, Optimization
      Brief
      • A paper on A Holistic Framework for Parameter Coordination of Interconnected Microgrids Against Natural Disasters, written by Tong Huang, a former MERL intern from Texas A&M University, has been selected as one of the Best Conference Papers at the 2020 Power and Energy Society General Meeting (PES-GM). IEEE PES-GM is the flagship conference for the IEEE Power and Energy Society. The work was done in collaboration with Hongbo Sun, K. J. Kim, and Daniel Nikovski from MERL, and Tong's advisor, Prof. Le Xie from Texas A&M University.
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  • News & Events

    •  NEWS   MERL Researcher Ankush Chakrabarty organized a special session on data-driven control at IEEE CCTA 2020
      Date: August 25, 2020
      MERL Contact: Ankush Chakrabarty
      Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
      Brief
      • Ankush Chakrabarty co-organized an invited session on “Data-Driven Control For Industrial Applications” at the IEEE Conference on Control Technology and Applications with Shahin Shahrampour (Asst. Prof., Texas A&M). Talks covered topics including reinforcement learning for aerospace systems, constrained reinforcement learning for motors, deep Q learning for traffic systems and participants included speakers from Stanford University, North Carolina State University, Texas A&M, Oklahoma State University, University of Science and Technology at Beijing, and TU Delft.

        MERL presented research (Chakrabarty, Danielson, Wang) on constraint-enforcing output-tracking with approximate dynamic programming for servomotor systems.
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    •  NEWS   Dr. Abraham P. Vinod joins the Research Staff of Mitsubishi Electric Research Laboratories
      Date: August 3, 2020
      Where: Cambridge, MA
      MERL Contact: Abraham P. Vinod
      Research Areas: Artificial Intelligence, Control, Optimization, Robotics
      Brief
      • Mitsubishi Electric Research Laboratories is excited to welcome Abraham P. Vinod as the newest member of its research staff, in the Control for Autonomy Team. Abraham joins MERL from the University of Texas, Austin, where he was a Postdoctoral Research Fellow. He obtained his Ph.D. from the University of New Mexico. His PhD research produced scalable algorithms for providing safety guarantees for stochastic, control-constrained, dynamical systems, with applications to motion planning. In his postdoctoral research, Abraham studied theory and algorithms for on-the-fly, data-driven control of unknown systems under severely limited data. His current research interests lie in the intersection of optimization, control, and learning. Abraham won the Best Student Paper Award at the 2017 ACM Hybrid Systems: Computation and Control Conference, was a finalist for the Best Paper Award in the 2018 ACM Hybrid Systems: Computation and Control Conference, and won the best undergraduate student research project award at the Indian Institute of Technology, Madras.
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  • Internships

    • MS1461: Online Bayesian Optimization

      The Multiphysical Systems (MS) team at MERL is seeking a highly motivated intern to conduct research on model-free optimization of HVAC systems, with special emphasis on online and scalable Bayesian optimization. The ideal candidate is enrolled in a PhD program and is pursuing research in machine learning for optimization/control. The ideal candidate will have experience in (one or more of) Bayesian optimization, Bayesian neural nets, Gaussian processes, and must be fluent in Python and standard ML toolkits e.g. PyTorch/Tensorflow. The expected duration of the (virtual) internship is 3-6 months.

    • MD1406: Numerical Analysis of Electric Machines

      MERL is seeking a motivated and qualified intern to conduct research in the design, modeling and optimization of electrical machines. The ideal candidate should have solid backgrounds in electromagnetic theory, electric machine design, and numerical modeling techniques (including model reduction), research experiences in electric, magnetic, and thermal modeling and analysis of electrical machines, and demonstrated capability to publish results in leading conferences/journals. Experience with ANSYS, COMSOL, and optimization techniques is a strong plus. Senior Ph.D. students in electrical or mechanical engineering with related expertise are encouraged to apply. Start date for this internship is flexible and the duration is 3-6 months.

    • CA1399: Optimization Algorithms for Stochastic Predictive Control

      MERL is looking for a highly motivated individual to work on tailored numerical optimization algorithms and applications of stochastic learning-based model predictive control (MPC) methods. The research will involve the study and development of novel optimization techniques and/or the implementation and validation of algorithms for industrial applications, e.g., related to autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: stochastic MPC (e.g., scenario trees or tube MPC), convex and non-convex optimization, machine learning, numerical optimization and (inverse) optimal control. PhD students in engineering or mathematics with a focus on stochastic (learning-based) MPC or numerical optimization are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in Matlab is expected; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3-6 months and the start date is flexible.


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  • Recent Publications

    •  Kalabic, U., Chiu, M., "Cap-and-trade scheme for ridesharing", Intelligent Transportation Systems Conference, September 2020.
      BibTeX TR2020-129 PDF
      • @inproceedings{Kalabic2020sep,
      • author = {Kalabic, Uros and Chiu, Michael},
      • title = {Cap-and-trade scheme for ridesharing},
      • booktitle = {Intelligent Transportation Systems Conference},
      • year = 2020,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2020-129}
      • }
    •  Han, M., Ozdenizci, O., Wang, Y., Koike-Akino, T., Erdogmus, D., "Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction", IEEE Signal Processing Letters, DOI: 10.1109/LSP.2020.3020215, Vol. 27, pp. 1565-1569, September 2020.
      BibTeX TR2020-128 PDF
      • @article{Han2020sep,
      • author = {Han, Mo and Ozdenizci, Ozan and Wang, Ye and Koike-Akino, Toshiaki and Erdogmus, Deniz},
      • title = {Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction},
      • journal = {IEEE Signal Processing Letters},
      • year = 2020,
      • volume = 27,
      • pages = {1565--1569},
      • month = sep,
      • doi = {10.1109/LSP.2020.3020215},
      • issn = {1558-2361},
      • url = {https://www.merl.com/publications/TR2020-128}
      • }
    •  Guo, J., Nagai, Y., Sumi, T., Orlik, P.V., Yamauchi, T., "Hybrid CSMA/CA for Sub-1 GHz Frequency Band Coexistence of IEEE 802.11ah and IEEE 802.15.4g", International Workshop on Informatics (IWIN), September 2020.
      BibTeX TR2020-127 PDF
      • @inproceedings{Guo2020sep,
      • author = {Guo, Jianlin and Nagai, Yukimasa and Sumi, Takenori and Orlik, Philip V. and Yamauchi, Takahisa},
      • title = {Hybrid CSMA/CA for Sub-1 GHz Frequency Band Coexistence of IEEE 802.11ah and IEEE 802.15.4g},
      • booktitle = {International Workshop on Informatics (IWIN)},
      • year = 2020,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2020-127}
      • }
    •  Chakrabarty, A., Danielson, C., Wang, Y., "Data-Driven Optimal Tracking with Constrained Approximate Dynamic Programming for Servomotor Systems", IEEE Conference on Control Technology and Applications, August 2020.
      BibTeX TR2020-116 PDF
      • @inproceedings{Chakrabarty2020aug,
      • author = {Chakrabarty, Ankush and Danielson, Claus and Wang, Yebin},
      • title = {Data-Driven Optimal Tracking with Constrained Approximate Dynamic Programming for Servomotor Systems},
      • booktitle = {IEEE Conference on Control Technology and Applications},
      • year = 2020,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2020-116}
      • }
    •  Greiff, M., Robertsson, A., Berntorp, K., "MSE-optimal measurement dimension reduction in Gaussian filtering*", Conference on Control Technology and Applications (CCTA), August 2020.
      BibTeX TR2020-124 PDF
      • @inproceedings{Greiff2020aug,
      • author = {Greiff, Marcus and Robertsson, Anders and Berntorp, Karl},
      • title = {MSE-optimal measurement dimension reduction in Gaussian filtering*},
      • booktitle = {Conference on Control Technology and Applications (CCTA)},
      • year = 2020,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2020-124}
      • }
    •  Romero, O., Benosman, M., "Robust Time-Varying Continuous-Time Optimization with Pre-Defined Finite-Time Stability", World Congress of the International Federation of Automatic Control (IFAC), August 2020.
      BibTeX TR2020-120 PDF
      • @inproceedings{Romero2020aug,
      • author = {Romero, Orlando and Benosman, Mouhacine},
      • title = {Robust Time-Varying Continuous-Time Optimization with Pre-Defined Finite-Time Stability},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2020,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2020-120}
      • }
    •  Di Cairano, S., Danielson, C., "Indirect Adaptive Model Predictive Control and its Application to Uncertain Linear Systems", International Journal of Robust and Nonlinear Control, July 2020.
      BibTeX TR2020-115 PDF
      • @article{DiCairano2020jul,
      • author = {Di Cairano, Stefano and Danielson, Claus},
      • title = {Indirect Adaptive Model Predictive Control and its Application to Uncertain Linear Systems},
      • journal = {International Journal of Robust and Nonlinear Control},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-115}
      • }
    •  Liu, D., Chen, S., Boufounos, P.T., "Graph-Based Array Signal Denoising for Perturbed Synthetic Aperture Radar", IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 2020.
      BibTeX TR2020-114 PDF Video
      • @inproceedings{Liu2020jul,
      • author = {Liu, Dehong and Chen, Siheng and Boufounos, Petros T.},
      • title = {Graph-Based Array Signal Denoising for Perturbed Synthetic Aperture Radar},
      • booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-114}
      • }
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