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
Nikovski
Christopher
Laughman
Matthew
Brand
Arvind
Raghunathan
Hongtao
Qiao
Saleh
Nabi
Hongbo
Sun
Piyush
Grover
William
Yerazunis
Emil
Laftchiev
Scott
Bortoff
Daniel
Burns
Devesh
Jha
Jinyun
Zhang
Michael
Jones
Mouhacine
Benosman
Hiroyuki
Hashimoto
Frederick
Igo Jr.
Diego
Romeres
Naoto
Konno
Thiago
Serra
Jing
Zhang
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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 Area: Data AnalyticsBrief- 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 new Wiley Journal of Advanced Control for Applications Date: November 1, 2018
MERL Contact: Mouhacine Benosman
Research Areas: Control, Data Analytics, Dynamical SystemsBrief- Wiley has recently launched the Journal of Advanced Control for Applications: Engineering and Industrial Systems, which seeks original and high-quality contributions on the design of advanced control for applications. The aim is to stimulate the adoption of new and improved control design methods and provide a forum for the discussion of control application problems. Papers for the journal must include sufficient novelty in either the control design methods, the modelling and simulation techniques used, or the applications studied. MERL researcher, Mouhacine Benosman, has been invited to join the Editorial Board of this new journal.
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NEWS MERL Researchers Demonstrate Robot Learning Technology at CEATEC'18 Date: October 15, 2018 - October 19, 2018
Where: CEATEC'18, Makuhari Messe, Tokyo
MERL Contacts: Devesh Jha; Daniel Nikovski; Diego Romeres; Alan Sullivan; Jeroen van Baar; William Yerazunis
Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, RoboticsBrief- MERL's work on robot learning algorithms was demonstrated at CEATEC'18, Japan's largest IT and electronics exhibition and conference held annually at Makuhari Messe near Tokyo. A team of researchers from the Data Analytics Group at MERL and the Artificial Intelligence Department of the Information Technology Center (ITC) of MELCO presented an interactive demonstration of a model-based artificial intelligence algorithm that learns how to control equipment autonomously. The algorithm developed at MERL constructs models of mechanical equipment through repeated trial and error, and then learns control policies based on these models. The demonstration used a circular maze, where the objective is to drive a ball to the center of the maze by tipping and tilting the maze, a task that is difficult even for humans; approximately half of the CEATEC'18 visitors who tried to steer the ball by means of a joystick could not bring it to the center of the maze within one minute. In contrast, MERL's algorithm successfully learned how to drive the ball to the goal within ten seconds without the need for human programming. The demo was at the entrance of MELCO's booth at CEATEC'18, inviting visitors to learn more about MELCO's many other AI technologies on display, and was seen by an estimated more than 50,000 visitors over the five days of the expo.
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Recent Publications
- , "Derivative-Free Semiparametric Bayesian Models for Robot Learning", Advances in Neural Information Processing Systems (NIPS), December 2018.
- , "Algorithms for Task Allocation in Homogeneous Swarm of Robots", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2018.
- , "Anomaly Detection in Discrete Manufacturing Systems using Event Relationship Tables", International Workshop on Principle of Diagnosis, August 2018.
- , "Consensus-based Synchronization of Microgrids at Multiple Points of Interconnection", IEEE Power & Energy Society General Meeting, August 2018.
- , "On the Minimum Chordal Completion Polytope", Operations Research, July 12, 2018.
- , "Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control", International Conference on Machine Learning (ICML), July 12, 2018.
- , "The Integrated Last-Mile Transportation Problem (ILMTP)", International Conference on Automated Planning and Scheduling (ICAPS), July 12, 20128.
- , "Machine Learning Based State-Space Approximate Dynamic Programming Approach for Energy and Reserve Management of Power Plants", IEEE PES Innovative Smart Grid Technologies Asia (ISGT Asia), July 11,. 2018.
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