Data Analytics
Predictive analytics, decision analytics, modeling, simulation, and optimization.
Data Analytics technologies aim to improve the performance of devices, systems, and business processes by means of collecting data, constructing predictive models from that data, and making improved decisions based on the constructed models. The Data Analytics group at MERL has been working on both predictive and decision analytics, as well as supporting fields such as signal processing, numerical methods, and information systems infrastructure.
The focus of the group is on innovative high-performance algorithms that can be applied to various product lines of Mitsubishi Electric, including electrical power systems, various transportation systems (trains, elevators, car navigation), heating, ventilation, and air conditioning (HVAC) systems and solutions, and factory automation. The application of these algorithms minimizes costs, maximizes profits, increases reliability, improves energy efficiency, and reduces environmental impact of products.
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Research Areas
Predictive Modeling
Research on predictive analytics, supported by advances in the field of statistical machine learning, aims to create accurate data-driven models of electromechanical and thermodynamical systems, as well as models of complex natural and man-made phenomena, such as road traffic and demand for electrical power.
Decision Optimization
Numerical methods for fast solution of network problems find application in the analysis of electrical power systems and Smart Grids that include renewable power sources with intermittent output, as well as highly variable loads such as electrical vehicles. Many problems in transportation systems, such as train operation optimization, group elevator scheduling, and route guidance for car navigation, can be reduced to planning and optimization problems. Energy consumption in buildings can be minimized by solving sequential decision and optimization problems, both at the level of individual air conditioning devices, as well as at the level of entire buildings.
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Researchers
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Awards
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AWARD Best Student Paper Award at the International Conference on Data Mining Date: November 30, 2017
Awarded to:
MERL Contact: Daniel Nikovski
Research Areas: Data Analytics, Predictive ModelingBrief- Yan Zhu, a former MERL intern from the University of California at Riverside has won the Best Student Paper Award at the International Conference on Data Mining in 2017, for her work on time series chains, a novel primitive for time series analysis. The work was done in collaboration with Makoto Imamura, formerly at Information Technology Center/AI Department, and currently a professor at Tokai University in Tokyo, Japan, Daniel Nikovski from MERL, and Yan's advisor, Prof. Eamonn Keogh from UC Riverside, whose lab has had a long and fruitful collaboration with MERL and Mitsubishi Electric.
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AWARD DEIM 2010 Best Paper Award Date: February 1, 2010
Awarded to:
MERL Contact: William Yerazunis
Research Area: Data Analytics -
AWARD SICE 2008 Best Paper Award Date: July 1, 2008
Awarded to:
MERL Contact: Daniel Nikovski
Research Area: Data Analytics
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News & Events
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NEWS Mouhacine Benosman joins the Editorial Board of the International Journal of Adaptive Control and Signal Processing Date: March 19, 2018
MERL Contact: Mouhacine Benosman
Research Areas: Multimedia, Data AnalyticsBrief- MERL researcher Mouhacine Benosman has been appointed as a member of the Editorial Board of the International Journal of Adaptive Control and Signal Processing.
The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.
- MERL researcher Mouhacine Benosman has been appointed as a member of the Editorial Board of the International Journal of Adaptive Control and Signal Processing.
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NEWS Andrew Knyazev (MERL) presents at the Schlumberger-Tufts U. Computational and Applied Math Seminar Date: April 10, 2018
MERL Contact: Andrew Knyazev
Research Areas: Data Analytics, Algorithms, Machine LearningBrief- Andrew Knyazev, Distinguished Research Scientist of MERL, has accepted an invitation to speak about his work on Big Data and spectral graph partitioning at the Schlumberger-Tufts U. Computational and Applied Math Seminar. A primary focus of this seminar series is on mathematical and computational aspects of remote sensing. A partial list of the topics of interest includes: numerical solution of large scale PDEs (a.k.a. forward problems); theory and numerical methods of inverse and ill-posed problems; imaging; related problems in numerical linear algebra, approximation theory, optimization and model reduction. The seminar meets on average once a month, the location alternates between Schlumberger's office in Cambridge, MA and the Tufts Medford Campus.
Abstract: Data clustering via spectral graph partitioning requires constructing the graph Laplacian and solving the corresponding eigenvalue problem. We consider and motivate using negative edge weights in the graph Laplacian. Preconditioned iterative solvers for the Laplacian eigenvalue problem are discussed and preliminary numerical results are presented.
- Andrew Knyazev, Distinguished Research Scientist of MERL, has accepted an invitation to speak about his work on Big Data and spectral graph partitioning at the Schlumberger-Tufts U. Computational and Applied Math Seminar. A primary focus of this seminar series is on mathematical and computational aspects of remote sensing. A partial list of the topics of interest includes: numerical solution of large scale PDEs (a.k.a. forward problems); theory and numerical methods of inverse and ill-posed problems; imaging; related problems in numerical linear algebra, approximation theory, optimization and model reduction. The seminar meets on average once a month, the location alternates between Schlumberger's office in Cambridge, MA and the Tufts Medford Campus.
See All News & Events for Data Analytics -
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Recent Publications
- , "Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control," Tech. Rep. TR2018-028, arXiv, February 2018.BibTeX Download PDFRead TR2018-028
- @techreport{MERL_TR2018-028,
- author = {Pan, Y. and Farahmand, A.-M. and White, M. and Nabi, S. and Grover, P. and Nikovski, D.N.},
- title = {Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control},
- institution = {MERL - Mitsubishi Electric Research Laboratories},
- address = {Cambridge, MA 02139},
- number = {TR2018-028},
- month = feb,
- year = 2018,
- url = {http://www.merl.com/publications/TR2018-028/}
- }
- , Fast Market Clearing Algorithms, December 2017.
- , "Three-Level Co-optimization Model for Generation Scheduling of Integrated Energy and Regulation Market", IEEE Innovative Smart Grid Technologies Conference , December 2017.
- , "Random Projection Filter Bank for Time Series Data", Advances in Neural Information Processing Systems (NIPS), December 2017.
- , "Learning to Regulate Rolling Ball Motion", IEEE Symposium on Computational Intelligence in Engineering Solutions, November 2017.
- , "Matrix Profile VII: Time Series Chains: A New Primitive for Time Series Data Mining", International Conference on Data Mining 2017, November 2017.
- , "Optimization of circuitry arrangements for heat exchangers using derivative-free optimization", Chemical Engineering Research and Design, DOI: 10.1016/j.cherd.2017.05.015, October 2017.
- , "Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control," Tech. Rep. TR2018-028, arXiv, February 2018.
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