- Date: October 18, 2021
Awarded to: Daniel Nikovski
MERL Contact: Daniel N. Nikovski
Research Areas: Artificial Intelligence, Machine Learning
Brief - Daniel Nikovski, Group Manager of MERL's Data Analytics group, has received an Outstanding Reviewer Award from the 2021 conference on Neural Information Processing Systems (NeurIPS'21). NeurIPS is the world's premier conference on neural networks and related technologies.
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- Date: January 25, 2021
Awarded to: Takenori Sumi, Yukimasa Nagai, Jianlin Guo, Philip Orlik, Tatsuya Yokoyama, Hiroshi Mineno
MERL Contacts: Jianlin Guo; Philip V. Orlik
Research Areas: Communications, Machine Learning, Signal Processing
Brief - MELCO and MERL researchers have won "Excellent Presentation Award" at the IPSJ/CDS30 (Information Processing Society of Japan/Consumer Devices and Systems 30th conferences) held on January 25, 2021. The paper titled "Sub-1 GHz Coexistence Using Reinforcement Learning Based IEEE 802.11ah RAW Scheduling" addresses coexistence between IEEE 802.11ah and IEEE 802.15.4g systems in the Sub-1 GHz frequency bands. This paper proposes a novel method to allocate IEEE 802.11 RAW time slots using a Q-Learning technique. MERL and MELCO have been leading IEEE 802.19.3 coexistence standard development and this paper is a good candidate for future standard enhancement. The authors are Takenori Sumi, Yukimasa Nagai, Jianlin Guo, Philip Orlik, Tatsuya Yokoyama and Hiroshi Mineno.
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- Date: January 6, 2021
Awarded to: Rushil Anirudh, Suhas Lohit, Pavan Turaga
MERL Contact: Suhas Lohit
Research Areas: Computational Sensing, Computer Vision, Machine Learning
Brief - A team of researchers from Mitsubishi Electric Research Laboratories (MERL), Lawrence Livermore National Laboratory (LLNL) and Arizona State University (ASU) received the Best Paper Honorable Mention Award at WACV 2021 for their paper "Generative Patch Priors for Practical Compressive Image Recovery".
The paper proposes a novel model of natural images as a composition of small patches which are obtained from a deep generative network. This is unlike prior approaches where the networks attempt to model image-level distributions and are unable to generalize outside training distributions. The key idea in this paper is that learning patch-level statistics is far easier. As the authors demonstrate, this model can then be used to efficiently solve challenging inverse problems in imaging such as compressive image recovery and inpainting even from very few measurements for diverse natural scenes.
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- Date: October 20, 2020
Awarded to: Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik, Hiroshi Mineno
MERL Contacts: Jianlin Guo; Philip V. Orlik
Research Areas: Communications, Optimization, Signal Processing
Brief - MELCO and MERL researchers have won "Outstanding Presentation Award" at 28th Conference of Information Processing Society of Japan (IPSJ)/Consumer Device & Systems held on September 29-30, 2020. The paper titled "IEEE 802.19.3 Standardization for Coexistence of IEEE 802.11ah and IEEE 802.15.4g Systems in Sub-1 GHz Frequency Bands" reports IEEE 802.19.3 standard development on coexistence between IEEE 802.11ah and IEEE 802.15.4g systems in the Sub-1 GHz frequency bands. MERL and MELCO have been leading this standard development and made major technical contributions, which propose methods to mitigate interference in smart meter systems. The authors are Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik and Hiroshi Mineno.
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- Date: October 15, 2020
Awarded to: Ethan Manilow, Gordon Wichern, Jonathan Le Roux
MERL Contacts: Jonathan Le Roux; Gordon Wichern
Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
Brief - Former MERL intern Ethan Manilow and MERL researchers Gordon Wichern and Jonathan Le Roux won Best Poster Award and Best Video Award at the 2020 International Society for Music Information Retrieval Conference (ISMIR 2020) for the paper "Hierarchical Musical Source Separation". The conference was held October 11-14 in a virtual format. The Best Poster Awards and Best Video Awards were awarded by popular vote among the conference attendees.
The paper proposes a new method for isolating individual sounds in an audio mixture that accounts for the hierarchical relationship between sound sources. Many sounds we are interested in analyzing are hierarchical in nature, e.g., during a music performance, a hi-hat note is one of many such hi-hat notes, which is one of several parts of a drumkit, itself one of many instruments in a band, which might be playing in a bar with other sounds occurring. Inspired by this, the paper re-frames the audio source separation problem as hierarchical, combining similar sounds together at certain levels while separating them at other levels, and shows on a musical instrument separation task that a hierarchical approach outperforms non-hierarchical models while also requiring less training data. The paper, poster, and video can be seen on the paper page on the ISMIR website.
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- Date: September 11, 2020
Awarded to: Yukimasa Nagai, Jianlin Guo, Takenori Sumi, Philip Orlik, Hiroshi Mineno
MERL Contacts: Jianlin Guo; Philip V. Orlik
Research Areas: Communications, Signal Processing
Brief - MELCO and MERL researchers have won one of two Best Paper Awards at International Workshop on Informatics (IWIN) 2020. The paper titled 'Hybrid CSMA/CA for Sub-1 GHz Frequency Band Coexistence of IEEE 802.11ah and IEEE 802.15.4g', reports research on the severity of interference between IEEE 802.11ah and IEEE 802.15.4g based networks and also proposes methods to mitigate this interference in smart meter systems. This research reported in this paper has also informed several of MELCO/MERL's contributions to the IEEE P802.19.3 task group which is developing standards to allow for improved coexistence in outdoor metering systems. Authors are Yukimasa Nagai, Jianlin Guo, Takenori Sumi, Philip Orlik and Hiroshi Mineno.
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- Date: August 26, 2020
Awarded to: Marcus Greiff, Anders Robertsson, Karl Berntorp
Research Areas: Control, Signal Processing
Brief - Marcus Greiff, a former MERL intern from the Department of Automatic Control, Lund University, Sweden, won one of three 2020 CCTA Outstanding Student Paper Awards and the Best Student Paper Award at the 2020 IEEE Conference on Control Technology and Applications. The research leading up to the awarded paper titled 'MSE-Optimal Measurement Dimension Reduction in Gaussian Filtering', concerned how to select a reduced set of measurements in estimation applications while minimally degrading performance, was done in collaboration with Karl Berntorp at MERL.
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- 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, Optimization
Brief - 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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- Date: December 18, 2019
Awarded to: Xuankai Chang, Wangyou Zhang, Yanmin Qian, Jonathan Le Roux, Shinji Watanabe
MERL Contact: Jonathan Le Roux
Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
Brief - MERL researcher Jonathan Le Roux and co-authors Xuankai Chang, Shinji Watanabe (Johns Hopkins University), Wangyou Zhang, and Yanmin Qian (Shanghai Jiao Tong University) won the Best Paper Award at the 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2019), for the paper "MIMO-Speech: End-to-End Multi-Channel Multi-Speaker Speech Recognition". MIMO-Speech is a fully neural end-to-end framework that can transcribe the text of multiple speakers speaking simultaneously from multi-channel input. The system is comprised of a monaural masking network, a multi-source neural beamformer, and a multi-output speech recognition model, which are jointly optimized only via an automatic speech recognition (ASR) criterion. The award was received by lead author Xuankai Chang during the conference, which was held in Sentosa, Singapore from December 14-18, 2019.
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- Date: October 17, 2019
Awarded to: Mitsubishi Electric Research Labs
MERL Contact: Elizabeth Phillips Brief - MERL received Cultural Vista's Global Partnership Award at the 2019 Cultural Vistas Awards Gala (#CVGala)in NYC in October. This event brought together more than 250 leaders from across the business, education, government, and diplomatic communities for a special evening recognizing leadership in advancing global skills and understanding.
The Global Partnership award recognizes MERL's exemplary contributions to advancing friendship, understanding, and effective collaboration between the United States and Japan.
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- Date: October 27, 2019
Awarded to: Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Chen Feng, Xiaoming Liu
MERL Contact: Tim K. Marks
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief - MERL researcher Tim Marks, former MERL interns Abhinav Kumar and Wenxuan Mou, and MERL consultants Professor Chen Feng (NYU) and Professor Xiaoming Liu (MSU) received the Best Oral Paper Award at the IEEE/CVF International Conference on Computer Vision (ICCV) 2019 Workshop on Statistical Deep Learning in Computer Vision (SDL-CV) held in Seoul, Korea. Their paper, entitled "UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood Loss," describes a method which, given an image of a face, estimates not only the locations of facial landmarks but also the uncertainty of each landmark location estimate.
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- Date: October 10, 2019
Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
MERL Contact: Devesh K. 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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- Date: May 22, 2019
Awarded to: Siriramya Bhamidipati, Kyeong Jin Kim, Hongbo Sun, Philip Orlik
MERL Contacts: Hongbo Sun; Philip V. Orlik
Research Areas: Artificial Intelligence, Communications, Machine Learning, Signal Processing, Information Security
Brief - MERL researchers, Kyeong Jin Kim, Hongbo Sun, Philip Orlik, along with lead author and former MERL intern Siriramya Bhamidipati were awarded the Smart Grid Symposium Best Paper Award at this year's International Conference on Communications (ICC) held in Shanghai, China. There paper titled "GPS Spoofing Detection and Mitigation in PMUs Using Distributed Multiple Directional Antennas," described a technique to rapidly detect and mitigate GPS timing attacks/errors via hardware (antennas) and signal processing (Kalman Filtering).
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- Date: January 2, 2019
Awarded to: Siheng Chen
Research Area: Signal Processing
Brief - MERL researcher, Siheng Chen, has won an IEEE Young Author Best Paper award for his paper entitled "Discrete Signal Processing on Graphs: Sampling Theory". This paper, published in the December 2015 issue of IEEE Transactions on Signal Processing, proposes a sampling theory for signals that are supported on either directed or undirected graphs. The theory follows the same paradigm as classical sampling theory and shows that perfect recovery is possible for graph signals bandlimited under the graph Fourier transform. The award honors the authors of an especially meritorious paper dealing with a subject related to IEEE's technical scope and appearing in one if its journals within a three year window of eligibility.
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- 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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- Date: January 16, 2019
Awarded to: Daniel Dinis
Research Areas: Communications, Signal Processing
Brief - Former MERL intern Daniel Dinis from University of Aveiro (UA), Portugal has received the 2018 IBM Scientific Award with Honorable Mention referring to the contributions on "Real-time Tunable Delta-sigma modulators for All-Digital RF Transmitters" in his Ph.D. study.
The award-winning work includes research conducted under the supervision of Arnaldo Oliveira and José Neto Vieira, professors from the Department of Electronics and Information Technology (DETI) of the UA, as well as contributions made during Daniel's 7 month internship in 2017 at MERL.
The ceremony for the presentation of the 28th IBM Scientific Prize took on January 16th, at the Noble Hall of the Superior Technical Institute. It was chaired by Marcelo Rebelo de Sousa, President of the Portuguese Republic.
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- Date: November 16, 2018
Awarded to: Ziming Zhang, Alan Sullivan, Hideaki Maehara, Kenji Taira, Kazuo Sugimoto
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief - Researchers and developers from MERL, Mitsubishi Electric and Mitsubishi Electric Engineering (MEE) have been recognized with an R&D100 award for the development of a deep learning-based water detector. Automatic detection of water levels in rivers and streams is critical for early warning of flash flooding. Existing systems require a height gauge be placed in the river or stream, something that is costly and sometimes impossible. The new deep learning-based water detector uses only images from a video camera along with 3D measurements of the river valley to determine water levels and warn of potential flooding. The system is robust to lighting and weather conditions working well during the night as well as during fog or rain. Deep learning is a relatively new technique that uses neural networks and AI that are trained from real data to perform human-level recognition tasks. This work is powered by Mitsubishi Electric's Maisart AI technology.
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- Date: April 17, 2018
Awarded to: Zhong-Qiu Wang
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - Former MERL intern Zhong-Qiu Wang (Ph.D. Candidate at Ohio State University) has received a Best Student Paper Award at the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018) for the paper "Multi-Channel Deep Clustering: Discriminative Spectral and Spatial Embeddings for Speaker-Independent Speech Separation" by Zhong-Qiu Wang, Jonathan Le Roux, and John Hershey. The paper presents work performed during Zhong-Qiu's internship at MERL in the summer 2017, extending MERL's pioneering Deep Clustering framework for speech separation to a multi-channel setup. The award was received on behalf on Zhong-Qiu by MERL researcher and co-author Jonathan Le Roux during the conference, held in Calgary April 15-20.
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- Date: November 30, 2017
Awarded to: Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn Keogh
MERL Contact: Daniel N. Nikovski
Research Area: 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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- 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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- Date: October 15, 2016
Awarded to: Anthony Vetro
MERL Contact: Anthony Vetro Brief - Anthony Vetro was recognized by APSIPA (Asia-Pacific Signal and Information Processing Association) as a 2016 Industrial Distinguished Leader. This distinction is reserved for selected APSIPA members with extraordinary accomplishments in any of the fields related to APSIPA scope. A list of past recipients can be found online: http://www.apsipa.org/industrial.htm.
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- Date: March 24, 2016
Awarded to: Toshiaki Koike-Akino, Keisuke Kojima, David S. Millar, Kieran Parsons, Tsuyoshi Yoshida, Takashi Sugihara
MERL Contacts: Toshiaki Koike-Akino; Kieran Parsons
Research Areas: Communications, Electronic and Photonic Devices, Signal Processing
Brief - Five papers from the Optical Comms team were presented at the 2016 Optical Fiber Conference (OFC) held in Anaheim, USA in March 2016. The papers relate to enhanced modulation formats, constellation shaping, chromatic dispersion estimation, low complexity adaptive equalization and coding for coherent optical links. The top-scored paper studied optimal selection of coding and modulation sets to jointly maximize nonlinear tolerance and spectral efficiency.
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- 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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- Date: January 6, 2016
Awarded to: Andrew Knyazev Brief - Andrew Knyazev is awarded the title of Professor Emeritus at the University of Colorado Denver effective 1/31/2016. The award letter from the Chancellor of the University of Colorado Denver provides examples of the record of excellence over 20 years of contributions to the university such as 2008 CU Denver Excellence in Research Award, 2000 Teaching Excellence Award for the college, supervision of Ph.D. students, and two decades of uninterrupted external research funding from the US National Science Foundation and Department of Energy.
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- Date: December 15, 2015
Awarded to: John R. Hershey, Takaaki Hori, Jonathan Le Roux and Shinji Watanabe
MERL Contact: Jonathan Le Roux
Research Area: Speech & Audio
Brief - The results of the third 'CHiME' Speech Separation and Recognition Challenge were publicly announced on December 15 at the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2015) held in Scottsdale, Arizona, USA. MERL's Speech and Audio Team, in collaboration with SRI, ranked 2nd out of 26 teams from Europe, Asia and the US. The task this year was to recognize speech recorded using a tablet in real environments such as cafes, buses, or busy streets. Due to the high levels of noise and the distance from the speaker's mouth to the microphones, this is very challenging task, where the baseline system only achieved 33.4% word error rate. The MERL/SRI system featured state-of-the-art techniques including multi-channel front-end, noise-robust feature extraction, and deep learning for speech enhancement, acoustic modeling, and language modeling, leading to a dramatic 73% reduction in word error rate, down to 9.1%. The core of the system has since been released as a new official challenge baseline for the community to use.
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