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.

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  • Researchers

  • Awards

    •  AWARD    Best Paper Award at SDEMPED 2023
      Date: August 30, 2023
      Awarded to: Bingnan Wang, Hiroshi Inoue, and Makoto Kanemaru
      MERL Contact: Bingnan Wang
      Research Areas: Applied Physics, Data Analytics, Multi-Physical Modeling
      Brief
      • MERL and Mitsubishi Electric's paper titled “Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches” was awarded one of three best paper awards at the 14th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED 2023). MERL Senior Principal Research Scientist Bingnan Wang presented the paper and received the award at the symposium. Co-authors of the paper include Mitsubishi Electric researchers Hiroshi Inoue and Makoto Kanemaru.

        SDEMPED was established as the only international symposium entirely devoted to the diagnostics of electrical machines, power electronics and drives. It is now a regular biennial event. The 14th version, SDEMPED 2023 was held in Chania, Greece from August 28th to 31st, 2023.
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    •  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: 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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  • News & Events

    •  TALK    [MERL Seminar Series 2025] Dick den Hertog presents talk titled Optimizing the Path Towards Plastic-Free Oceans
      Date & Time: Tuesday, March 11, 2025; 12:00 PM
      Speaker: Dick den Hertog, University of Amsterdam
      MERL Host: Arvind Raghunathan
      Research Areas: Data Analytics, Optimization
      Abstract
      • Increasing ocean plastic pollution is irreversibly harming ecosystems and human economic activities. We partner with a nonprofit organization and use optimization to help clean up oceans from plastic faster. Specifically, we optimize the route of their plastic collection system in the ocean to maximize the quantity of plastic collected over time. We formulate the problem as a longest path problem in a well-structured graph. However, because collection directly impacts future plastic density, the corresponding edge lengths are nonlinear polynomials. After analyzing the structural properties of the edge lengths, we propose a search-and-bound method, which leverages a relaxation of the problem solvable via dynamic programming and clustering, to efficiently find high-quality solutions (within 6% optimal in practice) and develop a tailored branch-and-bound strategy to solve it to provable optimality. On one year of ocean data, our optimization-based routing approach increases the quantity of plastic collected by more than 60% compared with the current routing strategy, hence speeding up the progress toward plastic-free oceans.
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    •  NEWS    MERL Researchers to Present 2 Conference and 11 Workshop Papers at NeurIPS 2024
      Date: December 10, 2024 - December 15, 2024
      Where: Advances in Neural Processing Systems (NeurIPS)
      MERL Contacts: Petros T. Boufounos; Matthew Brand; Ankush Chakrabarty; Anoop Cherian; François Germain; Toshiaki Koike-Akino; Christopher R. Laughman; Jonathan Le Roux; Jing Liu; Suhas Lohit; Tim K. Marks; Yoshiki Masuyama; Kieran Parsons; Kuan-Chuan Peng; Diego Romeres; Pu (Perry) Wang; Ye Wang; Gordon Wichern
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Human-Computer Interaction, Information Security
      Brief
      • MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.

        1. "RETR: Multi-View Radar Detection Transformer for Indoor Perception" by Ryoma Yataka (Mitsubishi Electric), Adriano Cardace (Bologna University), Perry Wang (Mitsubishi Electric Research Laboratories), Petros Boufounos (Mitsubishi Electric Research Laboratories), Ryuhei Takahashi (Mitsubishi Electric). Main Conference. https://neurips.cc/virtual/2024/poster/95530

        2. "Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads" by Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Joanna Matthiesen (Math Kangaroo USA), Kevin Smith (Massachusetts Institute of Technology), Josh Tenenbaum (Massachusetts Institute of Technology). Main Conference, Datasets and Benchmarks track. https://neurips.cc/virtual/2024/poster/97639

        3. "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?" by Young-Jin Park (Massachusetts Institute of Technology), Jing Liu (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Gordon Wichern (Mitsubishi Electric Research Laboratories), Navid Azizan (Massachusetts Institute of Technology), Christopher R. Laughman (Mitsubishi Electric Research Laboratories), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories). Time Series in the Age of Large Models Workshop.

        4. "Forget to Flourish: Leveraging Model-Unlearning on Pretrained Language Models for Privacy Leakage" by Md Rafi Ur Rashid (Penn State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Shagufta Mehnaz (Penn State University), Ye Wang (Mitsubishi Electric Research Laboratories). Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?

        5. "Spatially-Aware Losses for Enhanced Neural Acoustic Fields" by Christopher Ick (New York University), Gordon Wichern (Mitsubishi Electric Research Laboratories), Yoshiki Masuyama (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Jonathan Le Roux (Mitsubishi Electric Research Laboratories). Audio Imagination Workshop.

        6. "FV-NeRV: Neural Compression for Free Viewpoint Videos" by Sorachi Kato (Osaka University), Takuya Fujihashi (Osaka University), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Takashi Watanabe (Osaka University). Machine Learning and Compression Workshop.

        7. "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via VLM" by Keshav Bimbraw (Worcester Polytechnic Institute), Ye Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop.

        8. "Smoothed Embeddings for Robust Language Models" by Hase Ryo (Mitsubishi Electric), Md Rafi Ur Rashid (Penn State University), Ashley Lewis (Ohio State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kieran Parsons (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories). Safe Generative AI Workshop.

        9. "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation" by Xiangyu Chen (University of Kansas), Ye Wang (Mitsubishi Electric Research Laboratories), Matthew Brand (Mitsubishi Electric Research Laboratories), Pu Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). Workshop on Adaptive Foundation Models.

        10. "Preference-based Multi-Objective Bayesian Optimization with Gradients" by Joshua Hang Sai Ip (University of California Berkeley), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Ali Mesbah (University of California Berkeley), Diego Romeres (Mitsubishi Electric Research Laboratories). Workshop on Bayesian Decision-Making and Uncertainty. Lightning talk spotlight.

        11. "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensions with Trust-Region-based Bayesian Novelty Search" by Wei-Ting Tang (Ohio State University), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Joel A. Paulson (Ohio State University). Workshop on Bayesian Decision-Making and Uncertainty.

        12. "MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track" by Ye Wang (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Special Award for Practical Attack.

        13. "MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track" by Jing Liu (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Won 3rd Place Award.

        MERL members also contributed to the organization of the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips24/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce Research), Kevin Smith (Massachusetts Institute of Technology), Tim K. Marks (Mitsubishi Electric Research Laboratories), Juan Carlos Niebles (Salesforce AI Research), Petar Veličković (Google DeepMind).
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  • Internships

    • EA0070: Internship - Multi-modal sensor fusion

      MERL is looking for a self-motivated intern to work on multi-modal sensor fusion for health condition monitoring and predictive maintenance of motor drive systems. The ideal candidate would be a Ph.D. candidate in electrical engineering or computer science with solid research background in signal processing and machine learning. Experience in motor drive system is a plus. The intern is expected to collaborate with MERL researchers to collect data, explore multi-modal data relationship, and prepare manuscripts for publications. The total duration is anticipated to be 3 months and the start date is flexible.

      Required Specific Experience

      • Experience with multi-modal sensor fusion.


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  • Openings


    See All Openings at MERL

  • Recent Publications

    •  Wang, B., "Induction Motor Fault Classification with Topological Data Analysis", IEEE Energy Conversion Congress and Exposition (ECCE), October 2024.
      BibTeX TR2024-145 PDF
      • @inproceedings{Wang2024oct,
      • author = {Wang, Bingnan},
      • title = {{Induction Motor Fault Classification with Topological Data Analysis}},
      • booktitle = {IEEE Energy Conversion Congress and Exposition (ECCE)},
      • year = 2024,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2024-145}
      • }
    •  Giacomuzzo, G., Dalla Libera, A., Romeres, D., Carli, R., "A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification", IEEE Transaction on Robotics, DOI: 10.1109/​TRO.2024.3474851, pp. 4820-4836, August 2024.
      BibTeX TR2024-077 PDF Data Software
      • @article{Giacomuzzo2024aug2,
      • author = {Giacomuzzo, Giulio and Dalla Libera, Alberto and Romeres, Diego and Carli, Ruggero},
      • title = {{A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification}},
      • journal = {IEEE Transaction on Robotics},
      • year = 2024,
      • pages = {4820--4836},
      • month = aug,
      • doi = {10.1109/TRO.2024.3474851},
      • issn = {1941-0468},
      • url = {https://www.merl.com/publications/TR2024-077}
      • }
    •  Wang, B., Lin, C., Inoue, H., Kanemaru, M., "Induction Motor Eccentricity Fault Detection and Quantification using Topological Data Analysis", IEEE Access, DOI: 10.1109/​ACCESS.2024.3376249, Vol. 12, pp. 37891-37902, June 2024.
      BibTeX TR2024-063 PDF
      • @article{Wang2024jun,
      • author = {Wang, Bingnan and Lin, Chungwei and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {{Induction Motor Eccentricity Fault Detection and Quantification using Topological Data Analysis}},
      • journal = {IEEE Access},
      • year = 2024,
      • volume = 12,
      • pages = {37891--37902},
      • month = jun,
      • doi = {10.1109/ACCESS.2024.3376249},
      • url = {https://www.merl.com/publications/TR2024-063}
      • }
    •  Lin, C., Wang, B., "Analytical Green’s functions for two-dimensional electrostatics and Boundary-element based solver", Applied Computational Electromagnetics Society Symposium (ACES), May 2024, pp. 4.
      BibTeX TR2024-060 PDF
      • @inproceedings{Lin2024may3,
      • author = {Lin, Chungwei and Wang, Bingnan},
      • title = {{Analytical Green’s functions for two-dimensional electrostatics and Boundary-element based solver}},
      • booktitle = {Applied Computational Electromagnetics Society Symposium (ACES)},
      • year = 2024,
      • pages = 4,
      • month = may,
      • url = {https://www.merl.com/publications/TR2024-060}
      • }
    •  Wang, B., Inoue, H., Kanemaru, M., "Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches", IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), DOI: 10.1109/​SDEMPED54949.2023.10271414, August 2023, pp. 42-48.
      BibTeX TR2023-107 PDF
      • @inproceedings{Wang2023aug,
      • author = {Wang, Bingnan and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {{Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches}},
      • booktitle = {2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)},
      • year = 2023,
      • pages = {42--48},
      • month = aug,
      • publisher = {IEEE},
      • doi = {10.1109/SDEMPED54949.2023.10271414},
      • url = {https://www.merl.com/publications/TR2023-107}
      • }
    •  Zhang, J., Nikovski, D., Kojima, T., "3T-Net: Transformer Encoders for Destination Prediction", The Chinese Control Conference, DOI: 10.23919/​CCC58697.2023.10240616, July 2023.
      BibTeX TR2023-094 PDF Presentation
      • @inproceedings{Zhang2023jul3,
      • author = {Zhang, Jing and Nikovski, Daniel and Kojima, Takuro},
      • title = {{3T-Net: Transformer Encoders for Destination Prediction}},
      • booktitle = {The Chinese Control Conference},
      • year = 2023,
      • month = jul,
      • doi = {10.23919/CCC58697.2023.10240616},
      • url = {https://www.merl.com/publications/TR2023-094}
      • }
    •  Zhang, J., Nikovski, D., Nakamura, T., "GPU-APUMPEDI: A Parallel Algorithm for Computing Approximate Pan Matrix Profiles of Time Series", International conference on Time Series and Forecasting, July 2023.
      BibTeX TR2023-091 PDF
      • @inproceedings{Zhang2023jul2,
      • author = {Zhang, Jing and Nikovski, Daniel and Nakamura, Takaaki},
      • title = {{GPU-APUMPEDI: A Parallel Algorithm for Computing Approximate Pan Matrix Profiles of Time Series}},
      • booktitle = {International conference on Time Series and Forecasting},
      • year = 2023,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2023-091}
      • }
    •  Jeon, E.S., Lohit, S., Anirudh, R., Turaga, P., "Robust Time Series Recovery and Classification Using Test-time Noise Simulator Networks", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/​ICASSP49357.2023.10096888, May 2023.
      BibTeX TR2023-021 PDF Presentation
      • @inproceedings{Jeon2023may,
      • author = {Jeon, Eun Som and Lohit, Suhas and Anirudh, Rushil and Turaga, Pavan},
      • title = {{Robust Time Series Recovery and Classification Using Test-time Noise Simulator Networks}},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2023,
      • month = may,
      • publisher = {IEEE},
      • doi = {10.1109/ICASSP49357.2023.10096888},
      • url = {https://www.merl.com/publications/TR2023-021}
      • }
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