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 2024] Tom Griffiths presents talk titled Tools from cognitive science to understand the behavior of large language models
      Date & Time: Wednesday, September 18, 2024; 1:00 PM
      Speaker: Tom Griffiths, Princeton University
      Research Areas: Artificial Intelligence, Data Analytics, Machine Learning, Human-Computer Interaction
      Abstract
      • Large language models have been found to have surprising capabilities, even what have been called “sparks of artificial general intelligence.” However, understanding these models involves some significant challenges: their internal structure is extremely complicated, their training data is often opaque, and getting access to the underlying mechanisms is becoming increasingly difficult. As a consequence, researchers often have to resort to studying these systems based on their behavior. This situation is, of course, one that cognitive scientists are very familiar with — human brains are complicated systems trained on opaque data and typically difficult to study mechanistically. In this talk I will summarize some of the tools of cognitive science that are useful for understanding the behavior of large language models. Specifically, I will talk about how thinking about different levels of analysis (and Bayesian inference) can help us understand some behaviors that don’t seem particularly intelligent, how tasks like similarity judgment can be used to probe internal representations, how axiom violations can reveal interesting mechanisms, and how associations can reveal biases in systems that have been trained to be unbiased.
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    •  NEWS    Ankush Chakrabarty gave a lecture at UT-Austin's Seminar Series on Occupant-Centric Grid-Interactive Buildings
      Date: March 20, 2024
      Where: Austin, TX
      MERL Contact: Ankush Chakrabarty
      Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization
      Brief
      • Ankush Chakrabarty, Principal Research Scientist in the Multiphysical Systems Team, was invited to speak as a guest lecturer in the seminar series on "Occupant-Centric Grid Interactive Buildings" in the Department of Civil, Architectural and Environmental Engineering (CAEE) at the University of Texas at Austin.

        The talk, entitled "Deep Generative Networks and Fine-Tuning for Net-Zero Energy Buildings" described lessons learned from MERL's recent research on generative models for building simulation and control, along with meta-learning for on-the-fly fine-tuning to adapt and optimize energy expenditure.
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  • Internships

    • SA0040: Internship - Sound event and anomaly detection

      We are seeking graduate students interested in helping advance the fields of sound event detection/localization, anomaly detection, and physics informed deep learning for machine sounds. The interns will collaborate with MERL researchers to derive and implement novel algorithms, record data, conduct experiments, integrate audio signals with other sensors (electrical, vision, vibration, etc.), and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern's doctoral work.

      The ideal candidates are senior Ph.D. students with experience in some of the following: audio signal processing, microphone array processing, physics informed machine learning, outlier detection, and unsupervised learning.

      Multiple positions are available with flexible start dates (not just Spring/Summer but throughout 2025) and duration (typically 3-6 months).

    • 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.

    • MS0109: Internship - Time-Series Forecasting for Energy Systems

      MERL seeks graduate students passionate about deep learning and energy systems to contribute to the development of deep time-series forecasting models for real building energy data. The work will involve multi-domain research including deep learning model development, time-series analysis, and possibly integration with energy management systems. The methods will be implemented and evaluated using real-world datasets. The results of the internship are expected to be published in top-tier machine learning and energy systems conferences and/or journals.

      Exact start date is flexible (most likely Summer 2025), with an expected duration of 3-6 months, depending on agreed scope and intermediate progress.

      Required Specific Experience:

      • Current or past enrollment in a PhD program in Electrical Engineering, Computer Science, or a related field with a focus on Machine Learning or Energy Systems.
      • 2+ years of research experience in at least some of the following areas: deep learning, time-series analysis, probabilistic machine learning, energy systems modeling.
      • PyTorch fluency.
      • Familiarity with real-world data wrangling.
      • Experience with time-series data visualization and analysis tools.

      Strong Pluses:

      • Familiarity with transformer-based time-series forecasting methodologies e.g. TFT or time-series foundation models.
      • Familiarity with adaptation mechanisms e.g. fine-tuning, meta-learning.


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  • 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}
      • }
    •  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.
      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,
      • 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}
      • }
    •  Sharma, A., Zhang, J., Nikovski, D., Doshi-Velez, F., "Travel-time prediction using neural-network-based mixture models", International Workshop on Statistical Methods and Artificial Intelligence, DOI: 10.1016/​j.procs.2023.03.144, March 2023.
      BibTeX TR2023-012 PDF
      • @inproceedings{Sharma2023mar,
      • author = {Sharma, Abhishek and Zhang, Jing and Nikovski, Daniel and Doshi-Velez, Finale},
      • title = {Travel-time prediction using neural-network-based mixture models},
      • booktitle = {International Workshop on Statistical Methods and Artificial Intelligence},
      • year = 2023,
      • month = mar,
      • doi = {10.1016/j.procs.2023.03.144},
      • url = {https://www.merl.com/publications/TR2023-012}
      • }
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