News & Events

77 were found.




  •  NEWS   MERL Speech & Audio Researchers Presenting 7 Papers and a Tutorial at Interspeech 2019
    Date: September 15, 2019 - September 19, 2019
    Where: Graz, Austria
    MERL Contacts: Chiori Hori; Takaaki Hori; Jonathan Le Roux; Niko Moritz; Gordon Wichern
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio, Computer Vision
    Brief
    • MERL Speech & Audio Team researchers will be presenting 7 papers at the 20th Annual Conference of the International Speech Communication Association INTERSPEECH 2019, which is being held in Graz, Austria from September 15-19, 2019. Topics to be presented include recent advances in end-to-end speech recognition, speech separation, and audio-visual scene-aware dialog. Takaaki Hori is also co-presenting a tutorial on end-to-end speech processing.

      Interspeech is the world's largest and most comprehensive conference on the science and technology of spoken language processing. It gathers around 2000 participants from all over the world.
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  •  NEWS   Ankush Chakrabarty gave an invited talk on machine learning for constrained control at AI for Engineering in Toronto
    Date: August 19, 2019 - August 23, 2019
    Where: AI for Engineering Summer School 2019
    MERL Contact: Ankush Chakrabarty
    Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning
    Brief
    • Ankush Chakrabarty, a Visiting Research Scientist in MERL's Control and Dynamical Systems group, gave an invited talk at the AI for Engineering Summer School 2019 hosted by Autodesk. The talk briefly described MERL's research areas, and focused on Dr. Chakrabarty's work at MERL (with collaborators from the CD and DA group) on the use of supervised learning for verification of control systems with simulators/neural nets in the loop, and on constraint-enforcing reinforcement learning. Other speakers at the event included researchers from various academic and industrial research facilities including U Toronto, UW-Seattle, Carnegie Mellon U, the Vector Institute, and the Montreal Institute for Learning Algorithms.
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  •  NEWS   MERL researchers presented 8 papers at American Control Conference
    Date: July 10, 2019 - July 12, 2019
    Where: Philadelphia
    MERL Contacts: Mouhacine Benosman; Karl Berntorp; Ankush Chakrabarty; Claus Danielson; Stefano Di Cairano; Devesh Jha; Rien Quirynen; Yebin Wang; Avishai Weiss
    Research Areas: Control, Machine Learning, Optimization, Robotics, Applied Physics, Dynamical Systems, Computational Sensing
    Brief
    • At the American Control Conference, MERL presented 8 papers on subjects including model predictive control applications, estimation and motion planning for vehicles, modular control architectures, and adaptation and learning.
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  •  TALK   Perspectives on Integer Programming in Sparse Optimization
    Date & Time: Tuesday, July 16, 2019; 12:00 PM
    Speaker: Prof. Jeff Linderoth, University of Wisconsin-Madison
    MERL Host: Arvind Raghunathan
    Research Areas: Machine Learning, Optimization
    Brief
    • Algorithms to solve mixed integer linear programs have made incredible progress in the past 20 years. Key to these advances has been a mathematical analysis of the structure of the set of feasible solutions. We argue that a similar analysis is required in the case of mixed integer quadratic programs, like those that arise in sparse optimization in machine learning. One such analysis leads to the so-called perspective relaxation, which significantly improves solution performance on separable instances. Extensions of the perspective reformulation can lead to algorithms that are equivalent to some of the most popular, modern, sparsity-inducing non-convex regularizations in variable selection. Based on joint work with Hongbo Dong (Washington State Univ. ), Oktay Gunluk (IBM), and Kun Chen (Univ. Connecticut)
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  •  NEWS   MERL researchers presented more than 8 papers in European Control Conference, ECC 2019
    Date: June 25, 2019 - June 28, 2019
    Where: Naples, Italy
    MERL Contacts: Karl Berntorp; Scott Bortoff; Ankush Chakrabarty; Claus Danielson; Stefano Di Cairano; Devesh Jha; Christopher Laughman; Daniel Nikovski; Rien Quirynen; Diego Romeres; William Yerazunis
    Research Areas: Control, Machine Learning, Optimization, Artificial Intelligence, Data Analytics, Robotics, Multi-Physical Modeling
    Brief
    • The European Control Conference is the premier control conference in Europe. This year MERL was well represented with papers on control for HVAC, machine learning for estimation and control, robot assembly, and optimization methods for control.
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  •  AWARD   MERL Researchers Won IEEE ICC Best Paper Award.
    Date: May 22, 2019
    Awarded to: Siriramya Bhamidipati, Kyeong Jin Kim, Hongbo Sun, Philip Orlik
    MERL Contacts: Kyeong Jin (K.J.) Kim; Hongbo Sun
    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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  •  AWARD   MERL researcher wins Best Visualization Note Award at PacificVis2019 Conference
    Date: April 23, 2019
    Awarded to: Teng-yok Lee
    MERL Contact: 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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  •  NEWS   MERL presenting 16 papers at ICASSP 2019
    Date: May 12, 2019 - May 17, 2019
    Where: Brighton, UK
    MERL Contacts: Petros Boufounos; Anoop Cherian; Chiori Hori; Takaaki Hori; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Tim Marks; Niko Moritz; Philip Orlik; Milutin Pajovic; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
    Research Areas: Computational Sensing, Computer Vision, Machine Learning, Signal Processing, Speech & Audio, Artificial Intelligence, Communications
    Brief
    • MERL researchers will be presenting 16 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held in Brighton, UK from May 12-17, 2019. Topics to be presented include recent advances in speech recognition, audio processing, scene understanding, computational sensing, and parameter estimation. MERL is also a sponsor of the conference and will be participating in the student career luncheon; please join us at the lunch to learn about our internship program and career opportunities.

      ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year.
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  •  NEWS   Deep Learning-Based Photonic Circuit Design in Scientific Reports
    Date: February 4, 2019
    Where: Scientific Reports, open-access journal from Nature Research
    MERL Contacts: Devesh Jha; Toshiaki Koike-Akino; Keisuke Kojima; Chungwei Lin; Kieran Parsons; Bingnan Wang
    Research Areas: Artificial Intelligence, Electronic and Photonic Devices, Machine Learning, Communications
    Brief
    • MERL researchers developed a novel design method enhanced by modern deep learning techniques for optimizing photonic integrated circuits (PIC). The developed technique employs residual deep neural networks (DNNs) to understand physics underlaying complicated lightwave propagations through nano-structured photonic devices. It was demonstrated that the trained DNN achieves excellent prediction to design power splitting nanostructures having various target power ratios. The work was published in Scientific Reports, which is an online open access journal from Nature Research, having high-impact articles in the research community.
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  •  NEWS   MERL presenting 4 papers at OFC, including an invited talk
    Date: March 3, 2019 - March 7, 2019
    Where: San Diego, CA
    MERL Contacts: Devesh Jha; Toshiaki Koike-Akino; Keisuke Kojima; Chungwei Lin; David Millar; Kieran Parsons; Bingnan Wang; Ye Wang
    Research Areas: Communications, Machine Learning, Optimization, Signal Processing, Electronic and Photonic Devices
    Brief
    • MERL researchers are presenting 4 papers at the OSA Optical Fiber Conference (OFC), which is being held in San Diego from March 3-7, 2019. Topics to be presented include recent advances in nonbinary polar codes, joint polar-coded shaping, and deep learning-based photonics circuit design. Additionally, recent work on multiset-partition distribution matching is presented as an invited talk.

      OFC is the flagship conference of the OSA, and the world's most comprehensive technical conference focused on the research advances and latest technological development in optics and photonics. The event attracts more than 10000 participants each year.
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  •  NEWS   Mitsubishi Electric Corporation and MERL Press Release Describes Future Digitally Controlled Power Amplifier
    Date: January 10, 2019
    Where: Tokyo, Japan
    MERL Contacts: Mouhacine Benosman; Rui Ma; Philip Orlik; Koon Hoo Teo
    Research Areas: Communications, Electronic and Photonic Devices, Machine Learning, Signal Processing, Electric Systems
    Brief
    • Mitsubishi Electric Corporation announced today its development of the world's first ultra-wideband digitally controlled gallium nitride (GaN) amplifier, which is compatible with a world-leading range of sub-6GHz bands focused on fifth-generation (5G) mobile communication systems. With a power efficiency rating of above 40%, the amplifier is expected to contribute to large-capacity communication and reduce the power consumption of mobile base stations.

      MERL and Mitsubishi Electric researchers collaborated to develop digital control methods for amplifiers achieving high-efficiency of 40% and above, with 110% of the fractional bandwidth over frequency range 1.4-4.8 GHz. The digital control signals are designed using a learning-function based on Maisart®.

      Please see the link below for the full Mitsubishi Electric press release text.
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  •  AWARD   R&D100 award for Deep Learning-based Water Detector
    Date: November 16, 2018
    Awarded to: Ziming Zhang, Alan Sullivan, Hideaki Maehara, Kenji Taira, Kazuo Sugimoto
    MERL Contacts: Alan Sullivan; Ziming Zhang
    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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  •  EVENT   MERL is a Proud Sponsor of the Grace Hopper Celebration 2018!
    Date: Wednesday, September 26, 2018 - Friday, September 28, 2018
    MERL Contacts: Chiori Hori; Elizabeth Phillips
    Location: Houston, Texas
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    Brief
    • "MERL, in partnership with Mitsubishi Electric was a Gold Sponsor of the Grace Hopper Celebration 2018 (GHC18) held in Houston, TX on September 26-28th. Presented by AnitaB.org and the Association for Computing Machinery, this is world's largest gathering of women technologists. Chiori Hori and Elizabeth Phillips from MERL, and Yoshiyuki Umei, Jared Baker and Lien Randle from MEUS, proudly represented Mitsubishi Electric at the recruiting expo, that drew over 20,000 female technologists this year.
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  •  EVENT   MERL 3rd Annual Open House
    Date & Time: Thursday, November 29, 2018; 4-6pm
    MERL Contacts: Marissa Deegan; Elizabeth Phillips; Jeroen van Baar; Anthony Vetro
    Location: 201 Broadway, 8th floor, Cambridge, MA
    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 & Audio
    Brief
    • Snacks, demos, science: On Thursday 11/29, Mitsubishi Electric Research Labs (MERL) will host an open house for graduate+ students interested in internships, post-docs, and research scientist positions. The event will be held from 4-6pm and will feature demos & short presentations in our main areas of research including artificial intelligence, robotics, computer vision, speech processing, optimization, machine learning, data analytics, signal processing, communications, sensing, control and dynamical systems, as well as multi-physyical modeling and electronic devices. MERL is a high impact publication-oriented research lab with very extensive internship and university collaboration programs. Most internships lead to publication; many of our interns and staff have gone on to notable careers at MERL and in academia. Come mix with our researchers, see our state of the art technologies, and learn about our research opportunities. Dress code: casual, with resumes.

      Pre-registration for the event is strongly encouraged:
      merlopenhouse.eventbrite.com

      Current internship and employment openings:
      www.merl.com/internship/openings
      www.merl.com/employment/employment

      Information about working at MERL:
      www.merl.com/employment
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  •  NEWS   Andrew Knyazev (MERL) presents at the Schlumberger-Tufts U. Computational and Applied Math Seminar
    Date: April 10, 2018
    Research Areas: Machine Learning, Signal Processing
    Brief
    • 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.
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  •  TALK   Advances in Accelerated Computing
    Date & Time: Friday, February 2, 2018; 12:00
    Speaker: Dr. David Kaeli, Northeastern University
    MERL Host: Abraham Goldsmith
    Research Areas: Control, Optimization, Machine Learning, Speech & Audio
    Brief
    • GPU computing is alive and well! The GPU has allowed researchers to overcome a number of computational barriers in important problem domains. But still, there remain challenges to use a GPU to target more general purpose applications. GPUs achieve impressive speedups when compared to CPUs, since GPUs have a large number of compute cores and high memory bandwidth. Recent GPU performance is approaching 10 teraflops of single precision performance on a single device. In this talk we will discuss current trends with GPUs, including some advanced features that allow them exploit multi-context grains of parallelism. Further, we consider how GPUs can be treated as cloud-based resources, enabling a GPU-enabled server to deliver HPC cloud services by leveraging virtualization and collaborative filtering. Finally, we argue for for new heterogeneous workloads and discuss the role of the Heterogeneous Systems Architecture (HSA), a standard that further supports integration of the CPU and GPU into a common framework. We present a new class of benchmarks specifically tailored to evaluate the benefits of features supported in the new HSA programming model.
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  •  NEWS   Andrew Knyazev (MERL) invited to 2018 MathWorks Research Summit
    Date: June 2, 2018 - June 4, 2018
    Where: Newton, Massachusetts (USA)
    Research Areas: Control, Computer Vision, Dynamical Systems, Machine Learning, Data Analytics
    Brief
    • Dr. Andrew Knyazev of MERL has accepted an invitation to participate at the 2018 MathWorks Research Summit. The objective of the Research Summit is to provide a forum for leading researchers in academia and industry to explore the latest research and technology results and directions in computation and its use in technology, engineering, and science. The event aims to foster discussion among scientists, engineers, and research faculty about challenges and research opportunities for the respective communities with a particular interest in exploring cross-disciplinary research avenues.
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  •  NEWS   Tim Marks to give invited Keynote talk at AMFG 2017 Workshop, at ICCV 2017.
    Date: October 28, 2017
    Where: Venice, Italy
    MERL Contact: Tim Marks
    Research Areas: Machine Learning, Artificial Intelligence, Computer Vision
    Brief
    • MERL Senior Principal Research Scientist Tim K. Marks will give an invited keynote talk at the 2017 IEEE Workshop on Analysis and Modeling of Faces and Gestures (AMFG 2017). The workshop will take place On October 28, 2017, at the International Conference on Computer Vision (ICCV 2017) in Venice, Italy.
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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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  •  NEWS   MERL researchers presented 11 papers at ACC 2017 (American Controls Conference)
    Date: May 24, 2017 - May 26, 2017
    MERL Contacts: Mouhacine Benosman; Claus Danielson; Stefano Di Cairano; Abraham Goldsmith; Uroš Kalabić; Saleh Nabi; Daniel Nikovski; Arvind Raghunathan; Yebin Wang
    Research Areas: Control, Dynamical Systems, Machine Learning
    Brief
    • Talks were presented by members of several groups at MERL and covered a wide range of topics:
      - Similarity-Based Vehicle-Motion Prediction
      - Transfer Operator Based Approach for Optimal Stabilization of Stochastic Systems
      - Extended command governors for constraint enforcement in dual stage processing machines
      - Cooperative Optimal Output Regulation of Multi-Agent Systems Using Adaptive Dynamic Programming
      - Deep Reinforcement Learning for Partial Differential Equation Control
      - Indirect Adaptive MPC for Output Tracking of Uncertain Linear Polytopic Systems
      - Constraint Satisfaction for Switched Linear Systems with Restricted Dwell-Time
      - Path Planning and Integrated Collision Avoidance for Autonomous Vehicles
      - Least Squares Dynamics in Newton-Krylov Model Predictive Control
      - A Neuro-Adaptive Architecture for Extremum Seeking Control Using Hybrid Learning Dynamics
      - Robust POD Model Stabilization for the 3D Boussinesq Equations Based on Lyapunov Theory and Extremum Seeking
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