- Date & Time: Tuesday, September 14, 2021; 1:00 PM EST
Speaker: Prof. David Bergman, University of Connecticut
MERL Host: Arvind Raghunathan
Research Areas: Data Analytics, Machine Learning, Optimization
Brief 
The integration of machine learning and optimization opens the door to new modeling paradigms that have already proven successful across a broad range of industries. Sports betting is a particularly exciting application area, where recent advances in both analytics and optimization can provide a lucrative edge. In this talk we will discuss three algorithmic sports betting games where combinations of machine learning and optimization have netted me significant winnings.
The video of the talk is at YouTube Merl_news.
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- Date: March 14, 2021 - April 20, 2021
Where: IROS
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, Robotics
Brief - 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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- 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 & Audio
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- Date: February 4, 2021
Where: N/A
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, Machine Learning
Brief - Dr. Diego Romeres, Principal Research Scientist in the Data Analytics group, will serve on the Programme Committee for the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI), 2021.
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- Date: August 25, 2020
MERL Contact: Ankush Chakrabarty
Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
Brief - Ankush Chakrabarty co-organized an invited session on “Data-Driven Control For Industrial Applications” at the IEEE Conference on Control Technology and Applications with Shahin Shahrampour (Asst. Prof., Texas A&M). Talks covered topics including reinforcement learning for aerospace systems, constrained reinforcement learning for motors, deep Q learning for traffic systems and participants included speakers from Stanford University, North Carolina State University, Texas A&M, Oklahoma State University, University of Science and Technology at Beijing, and TU Delft.
MERL presented research (Chakrabarty, Danielson, Wang) on constraint-enforcing output-tracking with approximate dynamic programming for servomotor systems.
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- Date: July 12, 2020 - July 18, 2020
Where: Vienna, Austria (virtual this year)
MERL Contacts: Mouhacine Benosman; Anoop Cherian; Devesh Jha; Daniel 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.
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- 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, 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: 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.
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- 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).
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- 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.
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- 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.
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- 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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- 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.
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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: 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.
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- 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.
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- Date: October 15, 2018 - October 19, 2018
Where: CEATEC'18, Makuhari Messe, Tokyo
MERL Contacts: Devesh Jha; Daniel Nikovski; Diego Romeres; Alan Sullivan; Jeroen van Baar; William 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.
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- 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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- 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).
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- 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.
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- 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.
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- Date: November 30, 2017
Awarded to: Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn Keogh
MERL Contact: Daniel 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: February 14, 2018
Where: Tokyo, Japan
MERL Contacts: Devesh Jha; Daniel Nikovski; Diego Romeres; William Yerazunis; Jeroen van Baar; Alan Sullivan
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.
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- 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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- 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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