Data Analytics

Learning from data for optimal decisions.

Our data analytics work addresses predictive modeling techniques, including system identification, anomaly detection, feature selection, and time series analysis, as well as methods to solve various decision optimization problems including continuous optimization, combinatorial optimization, and sequential decision making.

  • Researchers

  • Awards

    •  AWARD   Mitsubishi Electric US Receives a 2022 CES Innovation Award for Touchless Elevator Control Jointly Developed with MERL
      Date: November 17, 2021
      Awarded to: Elevators and Escalators Division of Mitsubishi Electric US, Inc.
      MERL Contacts: Daniel N. Nikovski; William S. Yerazunis
      Research Areas: Data Analytics, Machine Learning, Signal Processing
      Brief
      • The Elevators and Escalators Division of Mitsubishi Electric US, Inc. has been recognized as a 2022 CES® Innovation Awards honoree for its new PureRide™ Touchless Control for elevators, jointly developed with MERL. Sponsored by the Consumer Technology Association (CTA), the CES Innovation Awards is the largest and most influential technology event in the world. PureRide™ Touchless Control provides a simple, no-touch product that enables users to call an elevator and designate a destination floor by placing a hand or finger over a sensor. MERL initiated the development of PureRide™ in the first weeks of the COVID-19 pandemic by proposing the use of infra-red sensors for operating elevator call buttons, and participated actively in its rapid implementation and commercialization, resulting in a first customer installation in October of 2020.
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    •  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 N. 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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    •  AWARD   MERL Researcher Devesh Jha Wins the Rudolf Kalman Best Paper Award 2019
      Date: October 10, 2019
      Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
      MERL Contact: Devesh K. Jha
      Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, Robotics
      Brief
      • MERL researcher Devesh Jha has won the Rudolf Kalman Best Paper Award 2019 for the paper entitled "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models". This paper, published in a Special Commemorative Issue for Rudolf E. Kalman in the ASME JDSMC in March 2018, uses Bayesian filtering for imitation learning in Hidden Mode Hybrid Systems. This award is given annually by the Dynamic Systems and Control Division of ASME to the authors of the best paper published in the ASME Journal of Dynamic Systems Measurement and Control during the preceding year.
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  • News & Events

    •  EVENT   Prof. Melanie Zeilinger of ETH to give keynote at MERL's Virtual Open House
      Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
      Speaker: Prof. Melanie Zeilinger, ETH
      Location: Virtual Event
      Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information Security
      Brief
      • MERL is excited to announce the second keynote speaker for our Virtual Open House 2021:
        Prof. Melanie Zeilinger from ETH .

        Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).

        Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Zeilinger's talk is scheduled for 3:15pm - 3:45pm (EST).

        Registration: https://mailchi.mp/merl/merlvoh2021

        Keynote Title: Control Meets Learning - On Performance, Safety and User Interaction

        Abstract: With increasing sensing and communication capabilities, physical systems today are becoming one of the largest generators of data, making learning a central component of autonomous control systems. While this paradigm shift offers tremendous opportunities to address new levels of system complexity, variability and user interaction, it also raises fundamental questions of learning in a closed-loop dynamical control system. In this talk, I will present some of our recent results showing how even safety-critical systems can leverage the potential of data. I will first briefly present concepts for using learning for automatic controller design and for a new safety framework that can equip any learning-based controller with safety guarantees. The second part will then discuss how expert and user information can be utilized to optimize system performance, where I will particularly highlight an approach developed together with MERL for personalizing the motion planning in autonomous driving to the individual driving style of a passenger.
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    •  EVENT   Prof. Ashok Veeraraghavan of Rice University to give keynote at MERL's Virtual Open House
      Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
      Speaker: Prof. Ashok Veeraraghavan, Rice University
      Location: Virtual Event
      Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information Security
      Brief
      • MERL is excited to announce the first keynote speaker for our Virtual Open House 2021:
        Prof. Ashok Veeraraghavan from Rice University.

        Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).

        Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Veeraraghavan's talk is scheduled for 1:15pm - 1:45pm (EST).

        Registration: https://mailchi.mp/merl/merlvoh2021

        Keynote Title: Computational Imaging: Beyond the limits imposed by lenses.

        Abstract: The lens has long been a central element of cameras, since its early use in the mid-nineteenth century by Niepce, Talbot, and Daguerre. The role of the lens, from the Daguerrotype to modern digital cameras, is to refract light to achieve a one-to-one mapping between a point in the scene and a point on the sensor. This effect enables the sensor to compute a particular two-dimensional (2D) integral of the incident 4D light-field. We propose a radical departure from this practice and the many limitations it imposes. In the talk we focus on two inter-related research projects that attempt to go beyond lens-based imaging.

        First, we discuss our lab’s recent efforts to build flat, extremely thin imaging devices by replacing the lens in a conventional camera with an amplitude mask and computational reconstruction algorithms. These lensless cameras, called FlatCams can be less than a millimeter in thickness and enable applications where size, weight, thickness or cost are the driving factors. Second, we discuss high-resolution, long-distance imaging using Fourier Ptychography, where the need for a large aperture aberration corrected lens is replaced by a camera array and associated phase retrieval algorithms resulting again in order of magnitude reductions in size, weight and cost. Finally, I will spend a few minutes discussing how the wholistic computational imaging approach can be used to create ultra-high-resolution wavefront sensors.
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  • Internships

    • DA1713: Resilient Power Grid and Multi-Energy Systems

      MERL is seeking a highly motivated and qualified individual to join our summer internship program and conduct research in the area of resilient power grid and multi-energy systems. The ideal candidate should have a solid knowledge of power systems, gas network, water network, energy hubs, and optimization. Experience with MATLAB or C/C++/Python is required. The duration of the internship is expected to be 3-6 months, and the start date is flexible. Candidates in their senior or junior years of a Ph.D. program are encouraged to apply This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • CV1703: Software development in ROS for robotic manipulation

      MERL is offering an internship position for non-research software development for robotic manipulation. The scope of the internship is to develop robust ROS packages by refactoring existing experimental code. The position is open to prospective candidates with very strong programming skills in ROS (Robot Operating System) using C++ primarily and Python respectively. The selected intern will have a software engineering role rather than research oriented. The position is open to both senior undergraduate students and master students. Flexible start and end dates. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • MD1745: Electric machine operation analysis

      MERL is looking for a self-motivated intern to work on electric machine experiments and signal processing. The ideal candidate would be a Ph.D. candidate in electrical engineering with solid research background in electric machines, power electronics, and signal processing. Experience in dSPACE is required. Proficiency in MATLAB and simulink is necessary. The intern is expected to collaborate with MERL researchers to carry out experiments, analyze experimental data, and prepare manuscripts for scientific publications. The total duration is 3 months. This internship requires work that can only be done at MERL.


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  • Openings


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  • Recent Publications

    •  Laftchiev, E., Romeres, D., Nikovski, D.N., "Dynamic Thermal Comfort Optimization for Groups", American Control Conference (ACC), DOI: 10.23919/​ACC50511.2021.9483191, May 2021.
      BibTeX TR2021-057 PDF
      • @inproceedings{Laftchiev2021may,
      • author = {Laftchiev, Emil and Romeres, Diego and Nikovski, Daniel N.},
      • title = {Dynamic Thermal Comfort Optimization for Groups},
      • booktitle = {American Control Conference (ACC)},
      • year = 2021,
      • month = may,
      • publisher = {IEEE},
      • doi = {10.23919/ACC50511.2021.9483191},
      • issn = {2378-5861},
      • isbn = {978-1-6654-4197-1},
      • url = {https://www.merl.com/publications/TR2021-057}
      • }
    •  Laftchiev, E., Yan, Q., Nikovski, D.N., "The Missing Input Problem", IEEE Big Data, DOI: 10.1109/​BigData50022.2020.9378144, December 2020, pp. 1565-1573.
      BibTeX TR2020-172 PDF
      • @inproceedings{Laftchiev2020dec,
      • author = {Laftchiev, Emil and Yan, Qing and Nikovski, Daniel N.},
      • title = {The Missing Input Problem},
      • booktitle = {IEEE Big Data},
      • year = 2020,
      • pages = {1565--1573},
      • month = dec,
      • publisher = {IEEE},
      • doi = {10.1109/BigData50022.2020.9378144},
      • url = {https://www.merl.com/publications/TR2020-172}
      • }
    •  Tsiligkaridis, A., Zhang, J., Taguchi, H., Nikovski, D.N., "Personalized Destination Prediction Using Transformers in a Contextless Data Setting", IEEE World Congress on Computational Intelligence (WCCI), DOI: 10.1109/​IJCNN48605.2020.9207514, July 2020.
      BibTeX TR2020-112 PDF
      • @inproceedings{Tsiligkaridis2020jul,
      • author = {Tsiligkaridis, Athanasios and Zhang, Jing and Taguchi, Hiroshi and Nikovski, Daniel N.},
      • title = {Personalized Destination Prediction Using Transformers in a Contextless Data Setting},
      • booktitle = {2020 International Joint Conference on Neural Networks (IJCNN)},
      • year = 2020,
      • month = jul,
      • doi = {10.1109/IJCNN48605.2020.9207514},
      • url = {https://www.merl.com/publications/TR2020-112}
      • }
    •  Konno, N., Raghunathan, A., "Data-Driven Joint Optimization of Pricing and Seat Allocation in Trains", International Conference on Railway Engineering Design and Operation (COMPRAIL), DOI: 10.2495/​CR200351, July 2020, pp. 379-392.
      BibTeX TR2020-094 PDF
      • @inproceedings{Konno2020jul2,
      • author = {Konno, Naoto and Raghunathan, Arvind},
      • title = {Data-Driven Joint Optimization of Pricing and Seat Allocation in Trains},
      • booktitle = {International Conference on Railway Engineering Design and Operation (COMPRAIL)},
      • year = 2020,
      • pages = {379--392},
      • month = jul,
      • publisher = {WIT Press},
      • doi = {10.2495/CR200351},
      • url = {https://www.merl.com/publications/TR2020-094}
      • }
    •  Zhang, S., Zhang, S., Wang, B., Habetler, T., "Deep Learning Algorithms for Bearing Fault Diagnostics – A Comprehensive Review", IEEE Access, DOI: 10.1109/​ACCESS.2020.2972859, Vol. 8, pp. 29857-29881, March 2020.
      BibTeX TR2020-034 PDF
      • @article{Zhang2020mar,
      • author = {Zhang, Shen and Zhang, Shibo and Wang, Bingnan and Habetler, Thomas},
      • title = {Deep Learning Algorithms for Bearing Fault Diagnostics – A Comprehensive Review},
      • journal = {IEEE Access},
      • year = 2020,
      • volume = 8,
      • pages = {29857--29881},
      • month = mar,
      • doi = {10.1109/ACCESS.2020.2972859},
      • issn = {2169-3536},
      • url = {https://www.merl.com/publications/TR2020-034}
      • }
    •  He, W., Lee, T.-Y., van Baar, J., Wittenburg, K.B., Shen, H.-W., "DynamicsExplorer: Visual Analytics for Robot Control Tasks involving Dynamics and LSTM-based Control Policies", IEEE Pacific Visualization Symposium (PacificVis), DOI: 10.1109/​PacificVis48177.2020.7127, January 2020, pp. 36-45.
      BibTeX TR2020-011 PDF
      • @inproceedings{He2020jan,
      • author = {He, Wenbin and Lee, Teng-Yok and van Baar, Jeroen and Wittenburg, Kent B. and Shen, Han-Wei},
      • title = {DynamicsExplorer: Visual Analytics for Robot Control Tasks involving Dynamics and LSTM-based Control Policies},
      • booktitle = {IEEE Pacific Visualization Symposium (PacificVis)},
      • year = 2020,
      • pages = {36--45},
      • month = jan,
      • doi = {10.1109/PacificVis48177.2020.7127},
      • url = {https://www.merl.com/publications/TR2020-011}
      • }
    •  Xu, H., Sun, H., Nikovski, D.N., Kitamura, S., Mori, K., Hashimoto, H., "Deep Reinforcement Learning for Joint Bidding and Pricing of Load Serving Entity", IEEE Transactions on smart grids, DOI: 10.1109/​TSG.2019.2903756, Vol. 10, No. 6, pp. 6366-6375, January 2020.
      BibTeX TR2020-003 PDF
      • @article{Xu2020jan,
      • author = {Xu, Hanchen and Sun, Hongbo and Nikovski, Daniel N. and Kitamura, Shoichi and Mori, Kazuyuki and Hashimoto, Hiroyuki},
      • title = {Deep Reinforcement Learning for Joint Bidding and Pricing of Load Serving Entity},
      • journal = {IEEE Transactions on smart grids},
      • year = 2020,
      • volume = 10,
      • number = 6,
      • pages = {6366--6375},
      • month = jan,
      • doi = {10.1109/TSG.2019.2903756},
      • issn = {1949-3061},
      • url = {https://www.merl.com/publications/TR2020-003}
      • }
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