News & Events

104 were found.




  •  NEWS   Computer vision and robotics researcher Siddarth Jain appointed as an Associate Editor for the IEEE Robotics and Automation Letters (RA-L)
    Date: October 13, 2020
    MERL Contact: Siddarth Jain
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Robotics
    Brief
    • Computer vision and robotics researcher, Siddarth Jain, has been appointed to the editorial board of the IEEE Robotics and Automation Letters (RA-L) as an Associate Editor. Siddarth joined MERL in September 2019 after obtaining his Ph.D. in robotics from Northwestern University, where he developed novel robotics systems to help people with motor-impairments in performing activities of daily living tasks.

      RA-L publishes peer-reviewed articles in areas of robotics and automation. RA-L also provides a unique feature to the authors with the opportunity to publish a paper in a peer-reviewed journal and present the same paper at the annual flagship robotics conferences of IEEE RAS, including ICRA, IROS, and CASE.
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  •  AWARD   Best Poster Award and Best Video Award at the International Society for Music Information Retrieval Conference (ISMIR) 2020
    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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  •  NEWS   Diego Romeres serves on the Programme Committee for the Conference on Innovative Applications of Artificial Intelligence, 2021.
    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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  •  NEWS   Anoop Cherian gave an invited talk at the Multi-modal Video Analysis Workshop, ECCV 2020
    Date: August 23, 2020
    Where: European Conference on Computer Vision (ECCV), online, 2020
    MERL Contact: Anoop Cherian
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
    Brief
    • MERL Principal Research Scientist Anoop Cherian gave an invited talk titled "Sound2Sight: Audio-Conditioned Visual Imagination" at the Multi-modal Video Analysis workshop held in conjunction with the European Conference on Computer Vision (ECCV), 2020. The talk was based on a recent ECCV paper that describes a new multimodal reasoning task called Sound2Sight and a generative adversarial machine learning algorithm for producing plausible video sequences conditioned on sound and visual context.
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  •  NEWS   MERL Researcher Ankush Chakrabarty organized a special session on data-driven control at IEEE CCTA 2020
    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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  •  NEWS   MERL's Scene-Aware Interaction Technology Featured in Mitsubishi Electric Corporation Press Release
    Date: July 22, 2020
    Where: Tokyo, Japan
    MERL Contacts: Siheng Chen; Anoop Cherian; Bret Harsham; Chiori Hori; Takaaki Hori; Jonathan Le Roux; Tim Marks; Alan Sullivan; Anthony Vetro
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
    Brief
    • Mitsubishi Electric Corporation announced that the company has developed what it believes to be the world’s first technology capable of highly natural and intuitive interaction with humans based on a scene-aware capability to translate multimodal sensing information into natural language.

      The novel technology, Scene-Aware Interaction, incorporates Mitsubishi Electric’s proprietary Maisart® compact AI technology to analyze multimodal sensing information for highly natural and intuitive interaction with humans through context-dependent generation of natural language. The technology recognizes contextual objects and events based on multimodal sensing information, such as images and video captured with cameras, audio information recorded with microphones, and localization information measured with LiDAR.

      Scene-Aware Interaction for car navigation, one target application, will provide drivers with intuitive route guidance. The technology is also expected to have applicability to human-machine interfaces for in-vehicle infotainment, interaction with service robots in building and factory automation systems, systems that monitor the health and well-being of people, surveillance systems that interpret complex scenes for humans and encourage social distancing, support for touchless operation of equipment in public areas, and much more. The technology is based on recent research by MERL's Speech & Audio and Computer Vision groups.


      Demonstration Video:



      Link:

      Mitsubishi Electric Corporation Press Release
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  •  TALK   GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning
    Date & Time: Tuesday, July 14, 2020; 11:00 AM
    Speaker: Hanrui Wang, MIT
    MERL Host: Rui Ma
    Research Areas: Electronic and Photonic Devices, Machine Learning
    Brief
    • Automatic transistor sizing is a challenging problem in circuit design due to the large design space, complex performance trade-offs, and fast technological advancements. Although there has been plenty of work on transistor sizing targeting on one circuit, limited research has been done on transferring the knowledge from one circuit to another to reduce the re-design overhead. In this work, we present GCN-RL Circuit Designer, leveraging reinforcement learning (RL) to transfer the knowledge between different technology nodes and topologies. Moreover, inspired by the simple fact that circuit is a graph, we learn on the circuit topology representation with graph convolutional neural networks (GCN). The GCN-RL agent extracts features of the topology graph whose vertices are transistors, edges are wires. Our learning-based optimization consistently achieves the highest Figures of Merit (FoM) on four different circuits compared with conventional black-box optimization methods (Bayesian Optimization, Evolutionary Algorithms), random search, and human expert designs. Experiments on transfer learning between five technology nodes and two circuit topologies demonstrate that RL with transfer learning can achieve much higher FoMs than methods without knowledge transfer. Our transferable optimization method makes transistor sizing and design porting more effective and efficient. The work is accepted to DAC 2020.
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  •  NEWS   Jonathan Le Roux gives Plenary Lecture at the JSALT 2020 Summer Workshop
    Date: July 10, 2020
    Where: Virtual Baltimore, MD
    MERL Contact: Jonathan Le Roux
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • MERL Senior Principal Research Scientist and Speech and Audio Senior Team Leader Jonathan Le Roux was invited by the Center for Language and Speech Processing at Johns Hopkins University to give a plenary lecture at the 2020 Frederick Jelinek Memorial Summer Workshop on Speech and Language Technology (JSALT). The talk, entitled "Deep Learning for Multifarious Speech Processing: Tackling Multiple Speakers, Microphones, and Languages", presented an overview of deep learning techniques developed at MERL towards the goal of cracking the Tower of Babel version of the cocktail party problem, that is, separating and/or recognizing the speech of multiple unknown speakers speaking simultaneously in multiple languages, in both single-channel and multi-channel scenarios: from deep clustering to chimera networks, phasebook and friends, and from seamless ASR to MIMO-Speech and Transformer-based multi-speaker ASR.

      JSALT 2020 is the seventh in a series of six-week-long research workshops on Machine Learning for Speech Language and Computer Vision Technology. A continuation of the well known Johns Hopkins University summer workshops, these workshops bring together diverse "dream teams" of leading professionals, graduate students, and undergraduates, in a truly cooperative, intensive, and substantive effort to advance the state of the science. MERL researchers led such teams in the JSALT 2015 workshop, on "Far-Field Speech Enhancement and Recognition in Mismatched Settings", and the JSALT 2018 workshop, on "Multi-lingual End-to-End Speech Recognition for Incomplete Data".
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  •  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 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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  •  NEWS   MERL researchers presented 10 papers at American Control Conference (ACC)
    Date: July 1, 2020 - July 3, 2020
    Where: Denver, Colorado (virtual)
    MERL Contacts: Mouhacine Benosman; Karl Berntorp; Ankush Chakrabarty; Stefano Di Cairano; Saleh Nabi; Rien Quirynen; Yebin Wang; Avishai Weiss
    Research Areas: Control, Machine Learning, Optimization
    Brief
    • At the American Control Conference, MERL presented 10 papers on subjects including autonomous-vehicle decision making and motion planning, nonlinear estimation for thermal-fluid models and GNSS positioning, learning-based reference governors and reference governors for railway vehicles, and fail-safe rendezvous control.
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  •  NEWS   Zhong-Qiu Wang joins MERL's Speech and Audio Team
    Date: June 22, 2020
    MERL Contact: Zhong-Qiu Wang
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • We are excited to announce that Dr. Zhong-Qiu Wang, who recently obtained his Ph.D. from The Ohio State University, has joined MERL's Speech and Audio Team as a Visiting Research Scientist. Zhong-Qiu brings strong expertise in microphone array processing, speech enhancement, blind source/speaker separation, and robust automatic speech recognition, for which he has developed some of the most advanced machine learning and deep learning methods.

      Prior to joining MERL, Zhong-Qiu received the B.Eng. degree in 2013 from Harbin Institute of Technology, Harbin, China, and the M.Sc. and Ph.D. degree in 2017 and 2020 from The Ohio State University, Columbus, USA, all in Computer Science. He was a summer research intern at Microsoft Research, Mitsubishi Electric Research Laboratories, and Google AI. He received a Best Student Paper Award at ICASSP 2018 for his work as an intern at MERL, and a Graduate Research Award from OSU Department of Computer Science and Engineering in 2020.
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  •  NEWS   MERL Scientists Presenting 5 Papers at IEEE International Conference on Communications (ICC) 2020
    Date: June 7, 2020 - June 11, 2020
    Where: Dublin, Ireland
    MERL Contacts: Kyeong Jin (K.J.) Kim; Toshiaki Koike-Akino; Ye Wang
    Research Areas: Communications, Machine Learning, Signal Processing, Digital Video
    Brief
    • Due to COVID-19, MERL Network Intelligence Team scientists remotely presented 5 papers at the IEEE International Conference on Communications (ICC) 2020, that was scheduled to be held in Dublin Ireland from June 7-11, 2020. Topics presented include recent advances in deep learning methods for communications and new access systems. Presentation videos are also found on our YouTube channel. Our developed technologies can facilitate a great advancement in broadband virtual conferencing which is required in post-COVID-19 society.

      IEEE ICC is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 2,900 scientific researchers submit proposals for program sessions to be held at the annual conference. The high-quality proposals are selected for the conference program, which includes technical papers, tutorials, workshops and industry sessions designed specifically to advance technologies, systems and infrastructure that are continuing to reshape the world and provide all users with access to an unprecedented spectrum of high-speed, seamless and cost-effective global telecommunications services.
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  •  NEWS   MERL researchers presenting four papers and organizing two workshops at CVPR 2020 conference
    Date: June 14, 2020 - June 19, 2020
    MERL Contacts: Siheng Chen; Anoop Cherian; Michael Jones; Toshiaki Koike-Akino; Tim Marks; Kuan-Chuan Peng; Ye Wang
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    Brief
    • MERL researchers are presenting four papers (two oral papers and two posters) and organizing two workshops at the IEEE/CVF Computer Vision and Pattern Recognition (CVPR 2020) conference.

      CVPR 2020 Orals with MERL authors:
      1. "Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction," by Maosen Li, Siheng Chen, Yangheng Zhao, Ya Zhang, Yanfeng Wang, Qi Tian
      2. "Collaborative Motion Prediction via Neural Motion Message Passing," by Yue Hu, Siheng Chen, Ya Zhang, Xiao Gu

      CVPR 2020 Posters with MERL authors:
      3. "LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood," by Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Ye Wang, Michael Jones, Anoop Cherian, Toshiaki Koike-Akino, Xiaoming Liu, Chen Feng
      4. "MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird’s Eye View Maps," by Pengxiang Wu, Siheng Chen, Dimitris N. Metaxas

      CVPR 2020 Workshops co-organized by MERL researchers:
      1. Fair, Data-Efficient and Trusted Computer Vision
      2. Deep Declarative Networks.
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  •  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.
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  •  NEWS   MERL Researcher Pu (Perry) Wang organized a special session on automotive radar sensing at IEEE SAM Workshop 2020
    Date: June 8, 2020 - June 12, 2020
    Where: Virtual Hangzhou
    MERL Contact: Pu (Perry) Wang
    Research Areas: Artificial Intelligence, Computational Sensing, Dynamical Systems, Machine Learning, Signal Processing
    Brief
    • MERL researcher Pu (Perry) Wang organized a special session on June 10, 2020 titled Automotive Radar Sensing. Presentations included topics from deep waveform design, object tracking, mutual interference mitigation with their applications to high-resolution automotive imaging. The session's contributors come from both academia and industry.

      In this special session, our previous intern Yuxuan Xia (Chalmers Institute of Technology, Sweden) presented our work on extended object tracking using low-cost automotive radar sensors with a realistic measurement model. Yuxuan was also selected to be one of the six best student paper finalists at IEEE SAM 2020.
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  •  TALK   A Prospect in Wireless Connectivity Beyond 5G: Heterogeneity, Learning, Caution, and New Opportunities
    Date & Time: Thursday, May 7, 2020; 11:00 AM
    Speaker: Prof. Petar Popovski, Aalborg University, Denmark
    MERL Host: Toshiaki Koike-Akino
    Research Areas: Artificial Intelligence, Communications, Machine Learning, Signal Processing, Information Security
    Brief
    • The wireless landscape evolves towards supporting a large population of connections for humans and machines with very diverse features and requirements. Perhaps the main motivation of 5G wireless systems is its flexibility to support heterogeneous connectivity requirements: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). However, this classification is rather limited and is currently undergoing a revision within the research community. The first part of this talk will discuss how this heterogeneity can be revised and which opportunities it opens with respect to spectrum usage. The second part of the talk will deal with performance guarantees of wireless services and, specifically, ultra-reliable communication and outline the importance of machine learning in that context. The final part of the talk will provide a broader view on the evolution of wireless connectivity, including aspects that are implied by the resistance to the deployment of 5G, but also the new opportunities that can transform the way we build and utilize connected systems.
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  •  TALK   Universal Differential Equations for Scientific Machine Learning
    Date & Time: Thursday, May 7, 2020; 12:00 PM
    Speaker: Christopher Rackauckas, MIT
    MERL Host: Christopher Laughman
    Research Areas: Machine Learning, Multi-Physical Modeling, Optimization
    Brief
    • In the context of science, the well-known adage "a picture is worth a thousand words" might well be "a model is worth a thousand datasets." Scientific models, such as Newtonian physics or biological gene regulatory networks, are human-driven simplifications of complex phenomena that serve as surrogates for the countless experiments that validated the models. Recently, machine learning has been able to overcome the inaccuracies of approximate modeling by directly learning the entire set of nonlinear interactions from data. However, without any predetermined structure from the scientific basis behind the problem, machine learning approaches are flexible but data-expensive, requiring large databases of homogeneous labeled training data. A central challenge is reco nciling data that is at odds with simplified models without requiring "big data". In this talk we discuss a new methodology, universal differential equations (UDEs), which augment scientific models with machine-learnable structures for scientifically-based learning. We show how UDEs can be utilized to discover previously unknown governing equations, accurately extrapolate beyond the original data, and accelerate model simulation, all in a time and data-efficient manner. This advance is coupled with open-source software that allows for training UDEs which incorporate physical constraints, delayed interactions, implicitly-defined events, and intrinsic stochasticity in the model. Our examples show how a diverse set of computationally-difficult modeling issues across scientific disciplines, from automatically discovering biological mechanisms to accelerating climate simulations by 15,000x, can be handled by training UDEs.
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  •  NEWS   Stefano Di Cairano Appointed IPC Vice-Chair for the 7th IFAC Symposium on NMPC (2021)
    Date: July 7, 2021 - July 14, 2021
    Where: Bratislava, Slovakia
    MERL Contact: Stefano Di Cairano
    Research Areas: Control, Machine Learning, Optimization
    Brief
    • MERL researcher Stefano Di Cairano has been appointed as Vice-Chair for Industry of the International Program Committee of the 7th IFAC Symposium on Nonlinear Model Predictive Control, which will be held in Bratislava, Slovakia, in July 2021.
      IFAC NMPC is the main symposium focused on model predictive control, theory, methods and applications, includes contributions on control, optimization, and machine learning research, and is held every 3 years.
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  •  NEWS   MERL presenting 13 papers and an industry talk at ICASSP 2020
    Date: May 4, 2020 - May 8, 2020
    Where: Virtual Barcelona
    MERL Contacts: Karl Berntorp; Petros Boufounos; Chiori Hori; Takaaki Hori; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Yanting Ma; Hassan Mansour; Niko Moritz; Philip Orlik; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern; Siheng Chen
    Research Areas: Computational Sensing, Computer Vision, Machine Learning, Signal Processing, Speech & Audio
    Brief
    • MERL researchers are presenting 13 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held virtually from May 4-8, 2020. Petros Boufounos is also presenting a talk on the Computational Sensing Revolution in Array Processing (video) in ICASSP’s Industry Track, and Siheng Chen is co-organizing and chairing a special session on a Signal-Processing View of Graph Neural Networks.

      Topics to be presented include recent advances in speech recognition, audio processing, scene understanding, computational sensing, array processing, and parameter estimation. Videos for all talks are available on MERL's YouTube channel, with corresponding links in the references below.

      This year again, MERL is a sponsor of the conference and will be participating in the Student Job Fair; please join us 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. Originally planned to be held in Barcelona, Spain, ICASSP has moved to a fully virtual setting due to the COVID-19 crisis, with free registration for participants not covering a paper.
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  •  NEWS   MERL Scientists Presented 5 Papers Including 2 Invited Talks at Optical Fiber Communications Conference (OFC) 2020
    Date: March 8, 2020 - March 13, 2020
    MERL Contacts: Devesh Jha; Toshiaki Koike-Akino; Keisuke Kojima; David Millar; Kieran Parsons; Ye Wang
    Research Areas: Communications, Electronic and Photonic Devices, Machine Learning, Signal Processing
    Brief
    • Due to COVID-19, MERL Optical Team scientists remotely presented 5 papers including 2 invited talks at the Optical Fiber Communications Conference (OFC) 2020, that was held in San Diego from March 8-13, 2020. Topics presented include recent advances in quantum signal processing, channel coding design, nano-optic power splitter, and deep learning-based integrated photonics. In addition, Dr. Kojima gave an invited workshop talk on deep learning-based nano-photonic device optimization.

      OFC is the largest global conference and exhibition for optical communications and networking professionals. The program is comprehensive from research to marketplace, from components to systems and networks and from technical sessions to the exhibition. For over 40 years, OFC has drawn attendees from all corners of the globe to meet and greet, teach and learn, make connections and move the industry forward. The five-day technical conference features peer reviewed presentations and more than 180 invited speakers, the thought leaders in the industry presenting the highlights of emerging technologies. Additional technical programming throughout the week includes special symposia, special sessions, in-depth tutorials, workshops, panels and the thought-provoking rump session.
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  •  AWARD   Best Paper Award at the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) 2019
    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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  •  NEWS   MERL researchers presented 8 papers at Conference on Decision and Control (CDC)
    Date: December 11, 2019 - December 13, 2019
    Where: Nice, France
    MERL Contacts: Mouhacine Benosman; Karl Berntorp; Scott Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Uroš Kalabić; Jing Zhang
    Research Areas: Control, Machine Learning, Optimization
    Brief
    • At the Conference on Decision and Control, MERL presented 8 papers on subjects including estimation for thermal-fluid models and transportation networks, analysis of HVAC systems, extremum seeking for multi-agent systems, reinforcement learning for vehicle platoons, and learning with applications to autonomous vehicles.
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  •  NEWS   MERL Scientists Presenting 11 Papers at IEEE Global Communications Conference (GLOBECOM) 2019
    Date: December 9, 2019 - December 13, 2019
    Where: Waikoloa, Hawaii, USA
    MERL Contacts: Jianlin Guo; Kyeong Jin (K.J.) Kim; Toshiaki Koike-Akino; Rui Ma; Philip Orlik; Pu (Perry) Wang
    Research Areas: Communications, Computer Vision, Machine Learning, Signal Processing, Information Security
    Brief
    • MERL Signal Processing scientists and collaborators will be presenting 11 papers at the IEEE Global Communications Conference (GLOBECOM) 2019, which is being held in Waikoloa, Hawaii from December 9-13, 2019. Topics to be presented include recent advances in power amplifier, MIMO algorithms, WiFi sensing, video casting, visible light communications, user authentication, vehicular communications, secrecy, and relay systems, including sophisticated machine learning applications. A number of these papers are a result of successful collaboration between MERL and world-leading Universities including: Osaka University, University of New South Wales, Oxford University, Princeton University, South China University of Technology, Massachusetts Institute of Technology and Aalborg University.

      GLOBECOM is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 3000 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. Themed “Revolutionizing Communications,” GLOBECOM2019 will feature a comprehensive high-quality technical program including 13 symposia and a variety of tutorials and workshops to share visions and ideas, obtain updates on latest technologies and expand professional and social networking.
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  •  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.
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  •  NEWS   Takaaki Hori elected to IEEE Technical Committee on Speech and Language Processing
    Date: November 9, 2019
    MERL Contact: Takaaki Hori
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • Takaaki Hori has been elected to serve on the Speech and Language Processing Technical Committee (SLTC) of the IEEE Signal Processing Society for a 3-year term.

      The SLTC promotes and influences all the technical areas of speech and language processing such as speech recognition, speech synthesis, spoken language understanding, speech to speech translation, spoken dialog management, speech indexing, information extraction from audio, and speaker and language recognition.
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