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
Daniel N.
Nikovski
Christopher R.
Laughman
Arvind
Raghunathan
Matthew
Brand
Hongbo
Sun
Diego
Romeres
William S.
Yerazunis
Devesh K.
Jha
Hongtao
Qiao
Bingnan
Wang
Scott A.
Bortoff
Chungwei
Lin
Jinyun
Zhang
Michael J.
Jones
Shingo
Kobori
Stefano
Di Cairano
Jianlin
Guo
Frederick J.
Igo Jr.
Suhas
Lohit
Siddarth
Jain
Jing
Liu
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Awards
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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 ModelingBrief- 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.
- 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.
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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 ProcessingBrief- 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, OptimizationBrief- 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
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NEWS Toshiaki Koike-Akino to give a tutorial talk at ISIT 2025 Quantum Hackathon Date: June 22, 2025
Where: IEEE International Symposium on Information Theory (ISIT)
MERL Contact: Toshiaki Koike-Akino
Research Areas: Artificial Intelligence, Communications, Data Analytics, Machine Learning, Optimization, Signal Processing, Human-Computer Interaction, Information SecurityBrief- Toshiaki Koike-Akino is invited to present a tutorial talk at IEEE ISIT 2025 Quantum Hackathon, to be held at Ann Arbor, Michigan, USA. The talk, entitled "Emerging Quantum AI Technology", will discuss the recent trends, challenges, and applications of quantum artificial intelligence (QAI) technologies.
The ISIT 2025 Quantum Hackathon invites participants to explore the intersection of quantum computing and information theory. Participants will work with quantum simulators, available quantum hardware, and state-of-the-art development kits to create innovative solutions that connect quantum advancements with challenges in communication and signal processing.
The IEEE International Symposium on Information Theory (ISIT) is the flagship conference of the IEEE Information Theory Society. The symposium centers around the presentation in all of the areas of information theory, including source and channel coding, communication theory and systems, cryptography and security, detection and estimation, networks, pattern recognition and learning, statistics, stochastic processes and complexity, and signal processing.
- Toshiaki Koike-Akino is invited to present a tutorial talk at IEEE ISIT 2025 Quantum Hackathon, to be held at Ann Arbor, Michigan, USA. The talk, entitled "Emerging Quantum AI Technology", will discuss the recent trends, challenges, and applications of quantum artificial intelligence (QAI) technologies.
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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, OptimizationAbstract
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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Openings
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Recent Publications
- , "Induction Motor Fault Classification with Topological Data Analysis", IEEE Energy Conversion Congress and Exposition (ECCE), DOI: 10.1109/ECCE55643.2024.10860892, October 2024.BibTeX TR2024-145 PDF
- @inproceedings{Wang2024oct,
- author = {Wang, Bingnan},
- title = {{Induction Motor Fault Classification with Topological Data Analysis}},
- booktitle = {2024 IEEE Energy Conversion Congress and Exposition (ECCE)},
- year = 2024,
- month = oct,
- doi = {10.1109/ECCE55643.2024.10860892},
- url = {https://www.merl.com/publications/TR2024-145}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "Induction Motor Fault Classification with Topological Data Analysis", IEEE Energy Conversion Congress and Exposition (ECCE), DOI: 10.1109/ECCE55643.2024.10860892, October 2024.
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