News & Events

7 Awards were found.




  •  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, Robotics
    Brief
    • 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   MERL researcher wins Best Visualization Note Award at PacificVis2019 Conference
    Date: April 23, 2019
    Awarded to: Teng-yok Lee
    Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Machine Learning
    Brief
    • MERL researcher Teng-yok Lee has won the Best Visualization Note Award at the PacificVis 2019 conference held in Bangkok Thailand, from April 23-26, 2019. The paper entitled "Space-Time Slicing: Visualizing Object Detector Performance in Driving Video Sequences" presents a visualization method called Space-Time Slicing to assist a human developer in the development of object detectors for driving applications without requiring labeled data. Space-Time Slicing reveals patterns in the detection data that can suggest the presence of false positives and false negatives.
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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 Areas: Data Analytics
    Brief
    • 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   2017 Graph Challenge Student Innovation Award
    Date: August 4, 2017
    Awarded to: David Zhuzhunashvili and Andrew Knyazev
    Research Area: Machine Learning
    Brief
    • David Zhuzhunashvili, an undergraduate student at UC Boulder, Colorado, and Andrew Knyazev, Distinguished Research Scientist at MERL, received the 2017 Graph Challenge Student Innovation Award. Their poster "Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge" was accepted to the 2017 IEEE High Performance Extreme Computing Conference (HPEC '17), taking place 12-14 September 2017 (http://www.ieee-hpec.org/), and the paper was accepted to the IEEE Xplore HPEC proceedings.

      HPEC is the premier conference in the world on the convergence of High Performance and Embedded Computing. DARPA/Amazon/IEEE Graph Challenge is a special HPEC event. Graph Challenge encourages community approaches to developing new solutions for analyzing graphs derived from social media, sensor feeds, and scientific data to enable relationships between events to be discovered as they unfold in the field. The 2017 Streaming Graph Challenge is Stochastic Block Partition. This challenge seeks to identify optimal blocks (or clusters) in a larger graph with known ground-truth clusters, while performance is evaluated compared to baseline Python and C codes, provided by the Graph Challenge organizers.

      The proposed approach is spectral clustering that performs block partition of graphs using eigenvectors of a matrix representing the graph. Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) method iteratively approximates a few leading eigenvectors of the symmetric graph Laplacian for multi-way graph partitioning. Preliminary tests for all static cases for the Graph Challenge demonstrate 100% correctness of partition using any of the IEEE HPEC Graph Challenge metrics, while at the same time also being approximately 500-1000 times faster compared to the provided baseline code, e.g., 2M static graph is 100% correctly partitioned in ~2,100 sec. Warm-starts of LOBPCG further cut the execution time 2-3x for the streaming graphs.
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  •  AWARD   Fellow of the Society for Industrial and Applied Mathematics (SIAM)
    Date: March 31, 2016
    Awarded to: Andrew Knyazev
    Research Areas: Control, Optimization, Dynamical Systems, Machine Learning, Data Analytics, Communications, Signal Processing
    Brief
    • Andrew Knyazev selected as a Fellow of the Society for Industrial and Applied Mathematics (SIAM) for contributions to computational mathematics and development of numerical methods for eigenvalue problems.

      Fellowship honors SIAM members who have made outstanding contributions to the fields served by the SIAM. Andrew Knyazev was among a distinguished group of members nominated by peers and selected for the 2016 Class of Fellows.
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  •  AWARD   DEIM 2010 Best Paper Award
    Date: February 1, 2010
    Awarded to: Hideya Shibata, Mamoru Kato, Mitsunori Kori and William Yerazunis
    Awarded for: "An Automatic Training Data Collection Method for Confidential E-mail Detection"
    Awarded by: The Forum on Data Engineering and Information Management (DEIM)
    MERL Contact: William Yerazunis
    Research Area: Data Analytics
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  •  AWARD   SICE 2008 Best Paper Award
    Date: July 1, 2008
    Awarded to: Nishiuma, N.; Goto, Y.; Kumazawa, H.; Komaya K.; Nikovski, D. and Brand, M.
    Awarded for: "Travel Time Prediction using Singular Value Decomposition"
    Awarded by: Journal of the Society of Instrument and Control Engineers of Japan
    MERL Contact: Daniel Nikovski
    Research Area: Data Analytics
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