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

    •  NEWS    Dr. Benosman is invited to give the mini-course in control theory at the 2022 edition of the Benelux Meeting on Systems and Control
      Date: July 5, 2022 - July 7, 2022
      MERL Contact: Mouhacine Benosman
      Research Areas: Control, Data Analytics, Dynamical Systems
      Brief
      • The Benelux meeting is an annual conference gathering of the scientific community of Belgium, the Netherlands, and Luxemburg around systems and control. It is especially intended for PhD researchers and a number of activities are dedicated to them, including plenary talks and a mini-course.

        Dr. Benosman has been invited to give the mini-course of the 2022 edition of the conference. This course, entitled 'A hybrid approach to control: classical control theory meets machine learning theory', will be centered around the topic of safe and robust machine learning-based control.
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    •  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
      Location: Virtual Event
      Speaker: Prof. Melanie Zeilinger, ETH
      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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  • Internships

    • 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.

    • DA1841: High-fidelity CFD for simulation and optimization

      The Data Analytics Group at MERL is seeking a highly motivated, qualified individual to join our internship program in the summer of 2022. The ideal candidate will be a Ph.D. student specializing in fluid dynamics, with solid background in turbulence modeling and computational fluid dynamics (CFD). Research exposure to one of the following is very desirable but not necessary: PDE-constrained optimization, model reduction techniques, and Physics-informed Neural Nets (PINNs). Ideal candidate is familiar with open-source CFD solvers such as OpenFOAM or SU2. Publication of results obtained during the internship is expected. The starting date is flexible and the internship will last about 12 weeks.

    • MS1838: Data-Driven Optimization for Building Energy Systems

      MERL is looking for a highly motivated and qualified candidate to work on data-driven, sample-efficient optimization with real-world applications in building energy systems. The ideal candidate will have a strong understanding machine learning or sampling-based optimization with expertise demonstrated via, e.g., publications, in at least one of: few-shot optimization, Bayesian methods, and/or learning for control/estimation of buildings and energy systems. Hands-on programming experience with standard ML toolkits such as PyTorch/Tensorflow is preferred; knowledge of additional, relevant tools (e.g., GPyTorch, Pyro) is a plus. PhD students are preferred, as an expected outcome of the internship is a publication in a high-tier venue. The minimum duration of the internship is 12 weeks; start time is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.


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  • Recent Publications

    •  Zhang, J., Nikovski, D.N., "Algorithms for Fast Computation of Pan Matrix Profiles of Time Series Under Unnormalized Euclidean Distances", International Conference on Applied Statistics and Data Analytics, April 2022.
      BibTeX TR2022-041 PDF
      • @inproceedings{Zhang2022apr3,
      • author = {Zhang, Jing and Nikovski, Daniel N.},
      • title = {Algorithms for Fast Computation of Pan Matrix Profiles of Time Series Under Unnormalized Euclidean Distances},
      • booktitle = {International Conference on Applied Statistics and Data Analytics},
      • year = 2022,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2022-041}
      • }
    •  Zhang, J., Nikovski, D.N., "Algorithms for Fast Computation of Matrix Profiles of Time Series Under Unnormalized Euclidean Distances", International Conference on Applied Statistics and Data Analytics, April 2022.
      BibTeX TR2022-040 PDF
      • @inproceedings{Zhang2022apr2,
      • author = {Zhang, Jing and Nikovski, Daniel N.},
      • title = {Algorithms for Fast Computation of Matrix Profiles of Time Series Under Unnormalized Euclidean Distances},
      • booktitle = {International Conference on Applied Statistics and Data Analytics},
      • year = 2022,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2022-040}
      • }
    •  Zhang, J., Nikovski, D.N., "Algorithms for Fast Computation of Matrix Profiles of Time Series Under Unnormalized Euclidean Distances", International Conference on Applied Statistics and Data Analytics, April 2022.
      BibTeX TR2022-040 PDF
      • @inproceedings{Zhang2022apr,
      • author = {Zhang, Jing and Nikovski, Daniel N.},
      • title = {Algorithms for Fast Computation of Matrix Profiles of Time Series Under Unnormalized Euclidean Distances},
      • booktitle = {International Conference on Applied Statistics and Data Analytics},
      • year = 2022,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2022-040}
      • }
    •  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}
      • }
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