News & Events

169 News items, Awards, Events or Talks found.


  •  NEWS    MERL researchers presenting three papers at ICML 2020
    Date: July 12, 2020 - July 18, 2020
    Where: Vienna, Austria (virtual this year)
    MERL Contacts: Mouhacine Benosman; Anoop Cherian; Devesh K. Jha; Daniel N. Nikovski
    Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
    Brief
    • MERL researchers are presenting three papers at the International Conference on Machine Learning (ICML 2020), which is virtually held this year from 12-18th July. ICML is one of the top-tier conferences in machine learning with an acceptance rate of 22%. The MERL papers are:

      1) "Finite-time convergence in Continuous-Time Optimization" by Orlando Romero and Mouhacine Benosman.

      2) "Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?" by Kei Ota, Tomoaki Oiki, Devesh Jha, Toshisada Mariyama, and Daniel Nikovski.

      3) "Representation Learning Using Adversarially-Contrastive Optimal Transport" by Anoop Cherian and Shuchin Aeron.
  •  
  •  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: 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.
  •  
  •  NEWS    Diego Romeres gave an invited talk on modeling and control of physical systems at the MIT workshop "ICRAxMIT"
    Date: June 9, 2020
    Where: ICRAxMIT
    MERL Contact: Diego Romeres
    Research Areas: Artificial Intelligence, Data Analytics, Dynamical Systems, Machine Learning, Robotics
    Brief
    • Diego Romeres, a Principal Research Scientist in MERL's Data Analytics group, gave an invited talk at the workshop ICRAxMIT organized at MIT. The talk briefly described a derivative-free framework that doesn't take in consideration velocities and accelerations to model and control robotic systems. The proposed approach is validated in two real robotic systems.
  •  
  •  NEWS    Diego Romeres appointed as an Associate Editor for IROS 2020
    Date: February 14, 2020
    Where: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems
    MERL Contact: Diego Romeres
    Research Areas: Artificial Intelligence, Data Analytics, Robotics
    Brief
    • Diego Romeres, a Research Scientist in MERL's Data Analytics group, will be serving as an Associate Editor (AE) for the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020).
  •  
  •  NEWS    Dr. Benosman joins the editorial board of the IEEE Control Systems Letters (L-CSS)
    Date: February 10, 2020
    MERL Contact: Mouhacine Benosman
    Research Areas: Control, Data Analytics, Dynamical Systems
    Brief
    • Dr. Benosman has been nominated as an associate editor at the IEEE Control Systems Letters (L-CSS).

      The L-CSS publishes peer-reviewed brief articles that provide a rapid and concise account of innovative ideas regarding the theory, design, and applications of all aspects of control engineering.
  •  
  •  NEWS    MERL researcher Diego Romeres gave an invited talk at University of Connecticut on Reinforcement Learning for Robotics
    Date: November 20, 2019
    MERL Contact: Diego Romeres
    Research Areas: Artificial Intelligence, Data Analytics, Machine Learning, Robotics
    Brief
    • Diego Romeres, a Research Scientist in MERL's Data Analytics group, gave a seminar lecture at the Electrical and Computer Engineering Colloquium of the University of Connecticut. The talk described novel reinforcement algorithms based on combining physical models with non-parametric models of robotic systems derived from data.
  •  
  •  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 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.
  •  
  •  NEWS    Mouhacine Benosman to deliver keynote at the mini-symposium 'Data assimilation in Model Order Techniques for Computational Mechanics'
    Date & Time: July 29, 2019; 10 AM
    Where: US National Congress on Computational Mechanics 2019, in Austin Texas
    MERL Contact: Mouhacine Benosman
    Research Areas: Control, Data Analytics, Dynamical Systems
    Brief
    • MERL researcher Mouhacine Benosman will present his work on 'Learning-based Robust Stabilization for Reduced-Order Models of 3D Boussinesq Equations' as a keynote speaker at the mini-symposium 'Data assimilation in Model Order Techniques for Computational Mechanics', during the next US National Congress on Computational Mechanics 2019, in Austin Texas.
  •  
  •  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.
  •  
  •  NEWS    Mouhacine Benosman co-edited a special issue on Learning-based Adaptive Control: Theory and Applications
    Date: February 4, 2019
    MERL Contact: Mouhacine Benosman
    Research Areas: Control, Data Analytics, Dynamical Systems
    Brief
    • Mouhacine Benosman is a guest editor of a special issue on Learning-based Adaptive Control: Theory and Application, recently published by the International Journal of Adaptive Control and Signal Processing. Other guest editors included Professor F.L. Lewis (University of Texas at Arlington Research Institute), Professor M. Guay (Queen's University), and Professor D. Owens (The University of Sheffield).

      The special issue presents results of current research on learning-based adaptive methods, merging together model-based and data-driven machine learning approaches.

      More information on the content of this special issue can be found at:
      https://onlinelibrary.wiley.com/toc/10991115/2019/33/2.
  •  
  •  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 Systems
    Brief
    • 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.
  •  
  •  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 K. Jha; Daniel N. Nikovski; Diego Romeres; William S. Yerazunis
    Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Robotics
    Brief
    • 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.
  •  
  •  EVENT    MERL 3rd Annual Open House
    Date & Time: Thursday, November 29, 2018; 4-6pm
    Location: 201 Broadway, 8th floor, Cambridge, MA
    MERL Contacts: Elizabeth Phillips; Anthony Vetro
    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.
  •  
  •  NEWS    Best doctoral dissertation award received by Visiting Research Scientist Thiago Serra
    Date: June 4, 2018
    Where: Pittsburgh, Pennsylvania
    MERL Contact: Arvind Raghunathan
    Research Area: Optimization
    Brief
    • Thiago Serra, currently a Visiting Research Scientist in the Data Analytics group, has been awarded the Gerald L. Thompson Doctoral Dissertation Award in Management Science from the Tepper School of Business, Carnegie Mellon University. This is awarded each year to honor an outstanding doctoral dissertation involving theoretical, computational and applied contributions in the area of Management Science. One of the thesis chapters, "The Integrated Last-Mile Transportation Problem" was work performed at MERL in conjunction with Arvind Raghunathan during a summer internship. This work resulted in a patent application and will be presented at the 2018 International Conference on Automated Planning and Scheduling (ICAPS).
  •  
  •  NEWS    Mouhacine Benosman joins the Editorial Board of the International Journal of Adaptive Control and Signal Processing
    Date: March 19, 2018
    MERL Contact: Mouhacine Benosman
    Brief
    • MERL researcher Mouhacine Benosman has been appointed as a member of the Editorial Board of the International Journal of Adaptive Control and Signal Processing.

      The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.
  •  
  •  NEWS    Andrew Knyazev (MERL) presents at the Schlumberger-Tufts U. Computational and Applied Math Seminar
    Date: April 10, 2018
    Research Area: Machine Learning
    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.
  •  
  •  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 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.
  •  
  •  NEWS    MERL Researchers Demonstrate New Model-Based AI Learning Technology for Equipment Control
    Date: February 14, 2018
    Where: Tokyo, Japan
    MERL Contacts: Devesh K. Jha; Daniel N. Nikovski; Diego Romeres; William S. Yerazunis
    Research Areas: Optimization, Computer Vision
    Brief
    • New technology for model-based AI learning for equipment control was demonstrated by MERL researchers at a recent press release event in Tokyo. The AI learning method constructs predictive models of the equipment through repeated trial and error, and then learns control rules based on these models. The new technology is expected to significantly reduce the cost and time needed to develop control programs in the future. Please see the link below for the full text of the Mitsubishi Electric press release.
  •  
  •  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
    Abstract
    • 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.
  •  
  •  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.
  •  
  •  NEWS    MERL invites applications for Visiting Faculty
    Date: February 15, 2018
    Brief
    • University faculty members are invited to spend part or all of their sabbaticals at MERL, pursuing projects of their own choosing in collaboration with MERL researchers.

      To apply, a candidate should identify and contact one or more MERL researchers with whom they would like to collaborate. The applicant and a MERL researcher will jointly prepare a proposal that the researcher will champion internally. Please visit the visiting faculty web page for further details: http://www.merl.com/employment/visiting-faculty.php.

      The application deadline for positions starting in Summer/Fall 2018 is February 15, 2018.
  •  
  •  EVENT    MERL 2nd Annual Open House
    Date & Time: Thursday, November 30, 2017; 4-6pm
    Location: 201 Broadway, 8th floor, Cambridge, MA
    MERL Contacts: Elizabeth Phillips; Anthony Vetro
    Brief
    • Snacks, demos, science: On Thursday 11/30, 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: algorithms, multimedia, electronics, communications, computer vision, speech processing, optimization, machine learning, data analytics, mechatronics, dynamics, control, and robotics. 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:
      https://merlopenhouse2.eventbrite.com/

      Current internship and employment openings:
      http://www.merl.com/internship/openings
      http://www.merl.com/employment/employment.
  •  
  •  NEWS    MERL attends The Grace Hopper Celebration of Women in Computing
    Date: October 4, 2017 - October 6, 2017
    Where: Orange County Convention Center, Orlando, FL
    MERL Contacts: Elizabeth Phillips; Jinyun Zhang
    Brief
    • Every year, women technologists and the best minds in computing convene to highlight the contributions of women to computing. The Anita Borg Institute co-presents GHC with the Association of Computing Machinery (ACM).

      The conference results in collaborative proposals, networking and mentoring for our attendees. Conference presenters are leaders in their respective fields, representing industry, academia and government.
  •  
  •  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.
  •  
  •  NEWS    MERL researchers presented 11 papers at ACC 2017 (American Controls Conference)
    Date: May 24, 2017 - May 26, 2017
    MERL Contacts: Mouhacine Benosman; Stefano Di Cairano; Abraham Goldsmith; Daniel N. 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.
  •