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
Arvind
Raghunathan
Matthew
Brand
Hongbo
Sun
Hongtao
Qiao
William
Yerazunis
Devesh
Jha
Saleh
Nabi
Diego
Romeres
Scott
Bortoff
Jinyun
Zhang
Mouhacine
Benosman
Michael
Jones
Norihiro
Nishiuma
Jing
Zhang
Frederick
Igo Jr.
Jeroen
van Baar
Bingnan
Wang
Siddarth
Jain
Carlos J.
Nohra
Hongyu
Wang
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Awards
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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: Kyeong Jin (K.J.) Kim; Daniel 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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AWARD MERL Researcher Devesh Jha Wins the Rudolf Kalman Best Paper Award 2019 Date: October 10, 2019
Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
MERL Contact: Devesh Jha
Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, RoboticsBrief- MERL researcher Devesh Jha has won the Rudolf Kalman Best Paper Award 2019 for the paper entitled "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models". This paper, published in a Special Commemorative Issue for Rudolf E. Kalman in the ASME JDSMC in March 2018, uses Bayesian filtering for imitation learning in Hidden Mode Hybrid Systems. This award is given annually by the Dynamic Systems and Control Division of ASME to the authors of the best paper published in the ASME Journal of Dynamic Systems Measurement and Control during the preceding year.
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AWARD Best Student Paper Award at the International Conference on Data Mining Date: November 30, 2017
Awarded to: Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn Keogh
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.
See All Awards for Data Analytics -
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News & Events
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NEWS Diego Romeres appointed as an Associate Editor for IROS 2021 Date: March 14, 2021 - April 20, 2021
Where: IROS
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, RoboticsBrief- Diego Romeres, a Principal Research Scientist in MERL's Data Analytics group, is serving as an Associate Editor (AE) for the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021).
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EVENT MERL Virtual Open House 2020 Date & Time: Wednesday, December 9, 2020; 1:00-5:00PM EST
MERL Contacts: Elizabeth Phillips; Jeroen van Baar; Anthony Vetro
Location: Virtual
Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & AudioBrief- MERL will host a virtual open house on December 9, 2020. Live sessions will be held from 1-5pm EST, including an overview of recent activities by our research groups and a talk by Prof. Pierre Moulin of University of Illinois at Urbana-Champaign on adversarial machine learning. Registered attendees will also be able to browse our virtual booths at their convenience and connect with our research staff on engagement opportunities including internship, post-doc and research scientist openings, as well as visiting faculty positions.
Registration: https://mailchi.mp/merl/merl-virtual-open-house-2020
Schedule: https://www.merl.com/events/voh20
Current internship and employment openings:
https://www.merl.com/internship/openings
https://www.merl.com/employment/employment
Information about working at MERL:
https://www.merl.com/employment
- MERL will host a virtual open house on December 9, 2020. Live sessions will be held from 1-5pm EST, including an overview of recent activities by our research groups and a talk by Prof. Pierre Moulin of University of Illinois at Urbana-Champaign on adversarial machine learning. Registered attendees will also be able to browse our virtual booths at their convenience and connect with our research staff on engagement opportunities including internship, post-doc and research scientist openings, as well as visiting faculty positions.
See All News & Events for Data Analytics -
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Recent Publications
- , "Dynamic Thermal Comfort Optimization for Groups", American Control Conference (ACC), May 2021.BibTeX TR2021-057 PDF
- @inproceedings{Laftchiev2021may,
- author = {Laftchiev, Emil and Romeres, Diego and Nikovski, Daniel N.},
- title = {Dynamic Thermal Comfort Optimization for Groups},
- booktitle = {American Control Conference (ACC)},
- year = 2021,
- month = may,
- url = {https://www.merl.com/publications/TR2021-057}
- }
- , "The Missing Input Problem", IEEE Big Data, DOI: 10.1109/BigData50022.2020.9378144, December 2020, pp. 1565-1573.BibTeX TR2020-172 PDF
- @inproceedings{Laftchiev2020dec,
- author = {Laftchiev, Emil and Yan, Qing and Nikovski, Daniel N.},
- title = {The Missing Input Problem},
- booktitle = {IEEE Big Data},
- year = 2020,
- pages = {1565--1573},
- month = dec,
- publisher = {IEEE},
- doi = {10.1109/BigData50022.2020.9378144},
- url = {https://www.merl.com/publications/TR2020-172}
- }
- , "Personalized Destination Prediction Using Transformers in a Contextless Data Setting", IEEE World Congress on Computational Intelligence (WCCI), DOI: 10.1109/IJCNN48605.2020.9207514, July 2020.BibTeX TR2020-112 PDF
- @inproceedings{Tsiligkaridis2020jul,
- author = {Tsiligkaridis, Athanasios and Zhang, Jing and Taguchi, Hiroshi and Nikovski, Daniel N.},
- title = {Personalized Destination Prediction Using Transformers in a Contextless Data Setting},
- booktitle = {2020 International Joint Conference on Neural Networks (IJCNN)},
- year = 2020,
- month = jul,
- doi = {10.1109/IJCNN48605.2020.9207514},
- url = {https://www.merl.com/publications/TR2020-112}
- }
- , "Data-Driven Joint Optimization of Pricing and Seat Allocation in Trains", International Conference on Railway Engineering Design and Operation (COMPRAIL), DOI: 10.2495/CR200351, July 2020, pp. 379-392.BibTeX TR2020-094 PDF
- @inproceedings{Konno2020jul2,
- author = {Konno, Naoto and Raghunathan, Arvind},
- title = {Data-Driven Joint Optimization of Pricing and Seat Allocation in Trains},
- booktitle = {International Conference on Railway Engineering Design and Operation (COMPRAIL)},
- year = 2020,
- pages = {379--392},
- month = jul,
- publisher = {WIT Press},
- doi = {10.2495/CR200351},
- url = {https://www.merl.com/publications/TR2020-094}
- }
- , "Deep Learning Algorithms for Bearing Fault Diagnostics – A Comprehensive Review", IEEE Access, DOI: 10.1109/ACCESS.2020.2972859, Vol. 8, pp. 29857-29881, March 2020.BibTeX TR2020-034 PDF
- @article{Zhang2020mar,
- author = {Zhang, Shen and Zhang, Shibo and Wang, Bingnan and Habetler, Thomas},
- title = {Deep Learning Algorithms for Bearing Fault Diagnostics – A Comprehensive Review},
- journal = {IEEE Access},
- year = 2020,
- volume = 8,
- pages = {29857--29881},
- month = mar,
- doi = {10.1109/ACCESS.2020.2972859},
- issn = {2169-3536},
- url = {https://www.merl.com/publications/TR2020-034}
- }
- , "DynamicsExplorer: Visual Analytics for Robot Control Tasks involving Dynamics and LSTM-based Control Policies", IEEE Pacific Visualization Symposium (PacificVis), DOI: 10.1109/PacificVis48177.2020.7127, January 2020, pp. 36-45.BibTeX TR2020-011 PDF
- @inproceedings{He2020jan,
- author = {He, Wenbin and Lee, Teng-Yok and van Baar, Jeroen and Wittenburg, Kent B. and Shen, Han-Wei},
- title = {DynamicsExplorer: Visual Analytics for Robot Control Tasks involving Dynamics and LSTM-based Control Policies},
- booktitle = {IEEE Pacific Visualization Symposium (PacificVis)},
- year = 2020,
- pages = {36--45},
- month = jan,
- doi = {10.1109/PacificVis48177.2020.7127},
- url = {https://www.merl.com/publications/TR2020-011}
- }
- , "Deep Reinforcement Learning for Joint Bidding and Pricing of Load Serving Entity", IEEE Transactions on smart grids, DOI: 10.1109/TSG.2019.2903756, Vol. 10, No. 6, pp. 6366-6375, January 2020.BibTeX TR2020-003 PDF
- @article{Xu2020jan,
- author = {Xu, Hanchen and Sun, Hongbo and Nikovski, Daniel N. and Kitamura, Shoichi and Mori, Kazuyuki and Hashimoto, Hiroyuki},
- title = {Deep Reinforcement Learning for Joint Bidding and Pricing of Load Serving Entity},
- journal = {IEEE Transactions on smart grids},
- year = 2020,
- volume = 10,
- number = 6,
- pages = {6366--6375},
- month = jan,
- doi = {10.1109/TSG.2019.2903756},
- issn = {1949-3061},
- url = {https://www.merl.com/publications/TR2020-003}
- }
- , "Dynamic Thermal Comfort Optimization for Groups", American Control Conference (ACC), May 2021.
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