News & Events

139 Events and Talks were found.




  •  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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  •  EVENT   MERL hosts Workshops for 2018 American Modelica Conference
    Date & Time: Monday, October 8, 2018 - Thursday, October 11, 2018; 8am-5pm
    MERL Contact: Christopher Laughman
    Location: MIT Samberg Conference Center, Cambridge, MA
    Research Areas: Control, Multi-Physical Modeling
    Brief
    • The 2018 American Modelica Conference, the first North American conference focused on the Modelica multiphysics modeling language, will be held on Tuesday and Wednesday, October 9-10, 2018 at the Samberg Conference Center at MIT in Cambridge, MA. Chris Laughman, a team leader in the Multiphysical Systems and Devices group, is the local chair for the conference.

      This conference will feature over 40 papers and user presentations on the Modelica language and its application to a wide variety of problem domains, including thermofluid, aerospace, automotive, and energy systems. There will also be 2 keynote addresses by John McKibben (Proctor & Gamble) and Hilding Elmqvist (Mogram AB). Nearly 100 attendees from 11 different countries have already registered for the conference, and it promises to be a very educational experience.

      MERL is also hosting two free workshops on October 8 to provide opportunities to engineers looking to increase their familiarity with the language and its applications. An introductory workshop will be led by engineers from Modelon during that morning, and then a second workshop on the application of Modelica to building systems will be led by Michael Wetter from Lawrence Berkeley National Labs in the afternoon. MERL will also host a Modelica user meeting on October 11 that will provide more details and discussion about trends in the use and development of Modelica in the larger engineering community.
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  •  EVENT   SANE 2018 - Speech and Audio in the Northeast
    Date: Thursday, October 18, 2018
    MERL Contacts: Takaaki Hori; Jonathan Le Roux
    Location: Google, Cambridge, MA
    Research Area: Speech & Audio
    Brief
    • SANE 2018, a one-day event gathering researchers and students in speech and audio from the Northeast of the American continent, will be held on Thursday October 18, 2018 at Google, in Cambridge, MA. MERL is one of the organizers and sponsors of the workshop.

      It is the 7th edition in the SANE series of workshops, which started at MERL in 2012. Since the first edition, the audience has steadily grown, with a record 180 participants in 2017.

      SANE 2018 will feature invited talks by leading researchers from the Northeast, as well as from the international community. It will also feature a lively poster session, open to both students and researchers.
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  •  EVENT   Fourth Annual Celebrating "Women in Science" Luncheon
    Date: Thursday, July 19, 2018
    MERL Contacts: Marissa Deegan; Kerry McKeon; Elizabeth Phillips; Jinyun Zhang
    Location: MERL
    Brief
    • We hosted the 4th Annual "Women in Science at MERL," event on July 19th. This year we celebrated the contributions of the eleven female interns, three female researchers, and some female members of HQ staff. MERL executives, managers and researchers participated in the event. MERL's interns and researchers were asked probing questions about how they are fulfilled in their work and how they facilitate innovation. This resulted in every participant feeling as though they were moving their field of science forward. Everyone left feeling inspired.
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  •  TALK   Theory and Applications of Sparse Model-Based Recurrent Neural Networks
    Date & Time: Tuesday, March 6, 2018; 12:00 PM
    Speaker: Scott Wisdom, Affectiva
    MERL Host: Jonathan Le Roux
    Research Area: Speech & Audio
    Brief
    • Recurrent neural networks (RNNs) are effective, data-driven models for sequential data, such as audio and speech signals. However, like many deep networks, RNNs are essentially black boxes; though they are effective, their weights and architecture are not directly interpretable by practitioners. A major component of my dissertation research is explaining the success of RNNs and constructing new RNN architectures through the process of "deep unfolding," which can construct and explain deep network architectures using an equivalence to inference in statistical models. Deep unfolding yields principled initializations for training deep networks, provides insight into their effectiveness, and assists with interpretation of what these networks learn.

      In particular, I will show how RNNs with rectified linear units and residual connections are a particular deep unfolding of a sequential version of the iterative shrinkage-thresholding algorithm (ISTA), a simple and classic algorithm for solving L1-regularized least-squares. This equivalence allows interpretation of state-of-the-art unitary RNNs (uRNNs) as an unfolded sparse coding algorithm. I will also describe a new type of RNN architecture called deep recurrent nonnegative matrix factorization (DR-NMF). DR-NMF is an unfolding of a sparse NMF model of nonnegative spectrograms for audio source separation. Both of these networks outperform conventional LSTM networks while also providing interpretability for practitioners.
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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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  •  EVENT   MERL leads organization of dialog technology challenges and associated workshop
    Date: Sunday, December 10, 2017
    MERL Contacts: Bret Harsham; Chiori Hori; Takaaki Hori
    Location: Hyatt Regency, Long Beach, CA
    Research Area: Speech & Audio
    Brief
    • MERL researcher Chiori Hori led the organization of the 6th edition of the Dialog System Technology Challenges (DSTC6). This year's edition of DSTC is split into three tracks: End-to-End Goal Oriented Dialog Learning, End-to-End Conversation Modeling, and Dialogue Breakdown Detection. A total of 23 teams from all over the world competed in the various tracks, and will meet at the Hyatt Regency in Long Beach, CA, USA on December 10 to present their results at a dedicated workshop colocated with NIPS 2017.

      MERL's Speech and Audio Team and Mitsubishi Electric Corporation jointly submitted a set of systems to the End-to-End Conversation Modeling Track, obtaining the best rank among 19 submissions in terms of objective metrics.
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  •  EVENT   MERL's Petros Boufounos is co-organizing symposium on The Future Of Signal Processing
    Date & Time: Monday, October 23, 2017; 8:00am-4:00pm
    MERL Contact: Petros Boufounos
    Location: MIT Samberg Conference Center Floor 7, 50 Memorial Drive, Cambridge, MA 02142
    Research Areas: Computational Sensing, Communications, Signal Processing
    Brief
    • Dr. Petros Boufounos is co-organizing the symposium on "The Future of Signal Processing," held in honor of the 80th birthday of Prof. Alan V. Oppenheim.

      Details at: https://futureofsp.eecs.mit.edu

      Organizing committee:
      Dr. Tom Baran, Lumii
      Dr. Petros Boufounos, MERL
      Prof. Anantha Chandrakasan, MIT
      Prof. Yonina Eldar, Technion

      Program:
      8:00-8:45 Coffee
      8:45-9:00 Opening remarks
      Prof. Martin Schmidt, Provost, MIT
      9:00-9:35 The ever-expanding physical boundaries of Signal Processing
      Prof. Martin Vetterli, President of EPFL, Lausanne
      9:35-10:10 Signal Processors and the U.S. Navy: Enduring Partners
      Admiral John Richardson, Chief of Naval Operations, US Navy

      10:10-10:30 Short break

      10:30-11:05 Signals and Signal Processing: The Invisibles and The Everlastings
      Prof. Min Wu, Professor of Electrical and Computer Engineering, University of Maryland
      11:05-11:40 Signal processing with quantum computers
      Prof. Isaac Chuang, Professor of Physics and Electrical Engineering; Senior Associate Dean of Digital Learning, MIT

      11:40-12:30 A box lunch will be provided. In your lunchbox, you'll find an envelope with four cards in it. Bring these cards back to your seats promptly after lunch for a magical surprise!

      12:30-12:40 Your Role in the Future of Signal Processing
      Magician Joel Acevedo

      12:40-1:05 Future of Low-power Embedded Signal Processing
      Prof. Anantha Chandrakasan, Dean, School of Engineering, MIT
      1:05-1:30 Synthetic biology and signal processing in living cells
      Prof. Ron Weiss, MIT, Professor of Biological Engineering and Director of the Synthetic Biology Center
      1:30-1:55 Physics 101 for Data Scientists
      Prof. Richard Baraniuk, Professor of Electrical and Computer Engineering at Rice University, Founder and Director of OpenStax College

      1:55-2:15 Short break

      2:15-2:40 Signals: Representation and Information
      Prof. Meir Feder, Professor of Electrical Engineering, Tel Aviv University

      2:40-3:05 Exposing and Removing Information: Some new Mathematics for Signal Processing
      Dr. Petros Boufounos, Senior Principal Research Scientist, Sensing Team Leader, Mitsubishi Electric Research Labs

      3:05-4:00 Panel discussion: The Venn diagram between "Data Science," "Machine Learning" and "Signal Processing"
      Moderator:
      Prof. Alan Oppenheim, Ford Professor of Engineering, MIT
      Panelists:
      Prof. Asu Ozdaglar, Associate Department Head, Electrical Engineering and Computer Science, MIT
      Prof. Ron Schafer, Georgia Tech (Emeritus) and Stanford Univ.
      Prof. Yonina Eldar, Professor of Electrical Engineering, Technion
      Prof. Victor Zue, Professor of Electrical and Computer Engineering, MIT
      Prof. Alexander Rakhlin, Associate Professor of Statistics, University of Pennsylvania
      4:00 Closing remarks
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  •  EVENT   MERL 2nd Annual Open House
    Date & Time: Thursday, November 30, 2017; 4-6pm
    MERL Contacts: Marissa Deegan; Elizabeth Phillips; Jeroen van Baar; Anthony Vetro
    Location: 201 Broadway, 8th floor, Cambridge, MA
    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
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  •  EVENT   Tim Marks to give lunch talk at Face and Gesture 2017 conference
    Date: Thursday, June 1, 2017
    Speaker: Tim K. Marks
    MERL Contact: Tim Marks
    Location: IEEE Conference on Automatic Face and Gesture Recognition (FG 2017), Washington, DC
    Research Areas: Machine Learning, Computer Vision
    Brief
    • MERL Senior Principal Research Scientist Tim K. Marks will give the invited lunch talk on Thursday, June 1, at the IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017). The talk is entitled "Robust Real-Time 3D Head Pose and 2D Face Alignment."
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  •  EVENT   Society for Industrial and Applied Mathematics panel for students on careers in industry
    Date & Time: Monday, July 10, 2017; 6:15 PM - 7:15 PM
    Speaker: Andrew Knyazev and other panelists, MERL
    MERL Contact: Joseph Katz
    Location: David Lawrence Convention Center, Pittsburgh PA
    Brief
    • Andrew Knyazev accepted an invitation to represent MERL at the panel on Student Careers in Business, Industry and Government at the annual meeting of the Society for Industrial and Applied Mathematics (SIAM).

      The format consists of a five minute introduction by each of the panelists covering their background and an overview of the mathematical and computational challenges at their organization. The introductions will be followed by questions from the students.
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  •  EVENT   MERL to participate in Xconomy Forum on AI & Robotics
    Date & Time: Tuesday, March 28, 2017; 1:30 - 5:30PM
    MERL Contacts: Joseph Katz; Daniel Nikovski; Alan Sullivan; Jay Thornton; Anthony Vetro; Richard (Dick) Waters; Jinyun Zhang
    Location: Google (355 Main St., 5th Floor, Cambridge MA)
    Brief
    • How will AI and robotics reshape the economy and create new opportunities (and challenges) across industries? Who are the hottest companies that will compete with the likes of Google, Amazon, and Uber to create the future? And what are New England innovators doing to strengthen the local cluster and help lead the national discussion?

      MERL will be participating in Xconomy's third annual conference on AI and robotics in Boston to address these questions. MERL President & CEO, Dick Waters, will be on a panel discussing the status and future of self-driving vehicles. Lab members will also be on hand demonstrate and discuss recent advances AI and robotics technology.

      The agenda and registration for the event can be found online: https://xconomyforum85.eventbrite.com
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  •  EVENT   MERL hosts Boston Imaging and Vision Meetup
    Date & Time: Tuesday, January 17, 2017; 6:00 pm
    Speaker: Tim Marks, Esra Cansizoglu and Carl Vondrick, MERL and MIT
    MERL Contact: Alan Sullivan
    Location: 201 Broadway, Cambridge, MA
    Research Area: Computer Vision
    Brief
    • MERL was pleased to host the Boston Imaging and Vision Meetup held on January 17. The meetup is an informal gathering of people interested in the field of computer imaging and vision. According to the group's website "the meetup provides an opportunity for the image processing/computer vision community to network, socialize and learn". The event held at MERL featured three speakers, Tim Marks and Esra Cansizoglu from MERL, as well as Carl Vondrick, an MIT CS graduate student in the group of Prof. Antonio Torralba. Roughly 70 people attended to eat pizza, hear the speakers and network.
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  •  TALK   Generative Model-Based Text-to-Speech Synthesis
    Date & Time: Wednesday, February 1, 2017; 12:00-13:00
    Speaker: Dr. Heiga ZEN, Google
    MERL Host: Chiori Hori
    Research Area: Speech & Audio
    Brief
    • Recent progress in generative modeling has improved the naturalness of synthesized speech significantly. In this talk I will summarize these generative model-based approaches for speech synthesis such as WaveNet, a deep generative model of raw audio waveforms. We show that WaveNets are able to generate speech which mimics any human voice and which sounds more natural than the best existing Text-to-Speech systems.
      See https://deepmind.com/blog/wavenet-generative-model-raw-audio/ for further details.
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  •  TALK   High-Dimensional Analysis of Stochastic Optimization Algorithms for Estimation and Learning
    Date & Time: Tuesday, December 13, 2016; Noon
    Speaker: Yue M. Lu, John A. Paulson School of Engineering and Applied Sciences, Harvard University
    MERL Host: Petros Boufounos
    Research Areas: Computational Sensing, Machine Learning
    Brief
    • In this talk, we will present a framework for analyzing, in the high-dimensional limit, the exact dynamics of several stochastic optimization algorithms that arise in signal and information processing. For concreteness, we consider two prototypical problems: sparse principal component analysis and regularized linear regression (e.g. LASSO). For each case, we show that the time-varying estimates given by the algorithms will converge weakly to a deterministic "limiting process" in the high-dimensional limit. Moreover, this limiting process can be characterized as the unique solution of a nonlinear PDE, and it provides exact information regarding the asymptotic performance of the algorithms. For example, performance metrics such as the MSE, the cosine similarity and the misclassification rate in sparse support recovery can all be obtained by examining the deterministic limiting process. A steady-state analysis of the nonlinear PDE also reveals interesting phase transition phenomena related to the performance of the algorithms. Although our analysis is asymptotic in nature, numerical simulations show that the theoretical predictions are accurate for moderate signal dimensions.
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  •  TALK   Reduced basis methods and their application in data science and uncertainty quantification
    Date & Time: Monday, December 12, 2016; 12:00 PM
    Speaker: Yanlai Chen, Department of Mathematics at the University of Massachusetts Dartmouth
    Research Areas: Control, Dynamical Systems
    Brief
    • Models of reduced computational complexity is indispensable in scenarios where a large number of numerical solutions to a parametrized problem are desired in a fast/real-time fashion. These include simulation-based design, parameter optimization, optimal control, multi-model/scale analysis, uncertainty quantification. Thanks to an offline-online procedure and the recognition that the parameter-induced solution manifolds can be well approximated by finite-dimensional spaces, reduced basis method (RBM) and reduced collocation method (RCM) can improve efficiency by several orders of magnitudes. The accuracy of the RBM solution is maintained through a rigorous a posteriori error estimator whose efficient development is critical and involves fast eigensolves.

      In this talk, I will give a brief introduction of the RBM/RCM, and explain how they can be used for data compression, face recognition, and significantly delaying the curse of dimensionality for uncertainty quantification.
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  •  TALK   Collaborative dictionary learning from big, distributed data
    Date & Time: Friday, December 2, 2016; 11:00 AM
    Speaker: Prof. Waheed Bajwa, Rutgers University
    MERL Host: Petros Boufounos
    Research Area: Computational Sensing
    Brief
    • While distributed information processing has a rich history, relatively less attention has been paid to the problem of collaborative learning of nonlinear geometric structures underlying data distributed across sites that are connected to each other in an arbitrary topology. In this talk, we discuss this problem in the context of collaborative dictionary learning from big, distributed data. It is assumed that a number of geographically-distributed, interconnected sites have massive local data and they are interested in collaboratively learning a low-dimensional geometric structure underlying these data. In contrast to some of the previous works on subspace-based data representations, we focus on the geometric structure of a union of subspaces (UoS). In this regard, we propose a distributed algorithm, termed cloud K-SVD, for collaborative learning of a UoS structure underlying distributed data of interest. The goal of cloud K-SVD is to learn an overcomplete dictionary at each individual site such that every sample in the distributed data can be represented through a small number of atoms of the learned dictionary. Cloud K-SVD accomplishes this goal without requiring communication of individual data samples between different sites. In this talk, we also theoretically characterize deviations of the dictionaries learned at individual sites by cloud K-SVD from a centralized solution. Finally, we numerically illustrate the efficacy of cloud K-SVD in the context of supervised training of nonlinear classsifiers from distributed, labaled training data.
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  •  EVENT   MERL organizes Workshop on End-to-End Speech and Audio Processing at NIPS 2016
    Date: Saturday, December 10, 2016
    Location: Centre Convencions Internacional Barcelona, Barcelona SPAIN
    Research Areas: Machine Learning, Speech & Audio
    Brief
    • MERL researcher John Hershey, is organizing a Workshop on End-to-End Speech and Audio Processing, on behalf of MERL's Speech and Audio team, and in collaboration with Philemon Brakel of the University of Montreal. The workshop focuses on recent advances to end-to-end deep learning methods to address alignment and structured prediction problems that naturally arise in speech and audio processing. The all day workshop takes place on Saturday, December 10th at NIPS 2016, in Barcelona, Spain.
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  •  EVENT   John Hershey to present tutorial at the 2016 IEEE SLT Workshop
    Date: Tuesday, December 13, 2016
    Speaker: John Hershey, MERL
    MERL Contact: Jonathan Le Roux
    Location: 2016 IEEE Spoken Language Technology Workshop, San Diego, California
    Research Areas: Machine Learning, Speech & Audio
    Brief
    • MERL researcher John Hershey presents an invited tutorial at the 2016 IEEE Workshop on Spoken Language Technology, in San Diego, California. The topic, "developing novel deep neural network architectures from probabilistic models" stems from MERL work with collaborators Jonathan Le Roux and Shinji Watanabe, on a principled framework that seeks to improve our understanding of deep neural networks, and draws inspiration for new types of deep network from the arsenal of principles and tools developed over the years for conventional probabilistic models. The tutorial covers a range of parallel ideas in the literature that have formed a recent trend, as well as their application to speech and language.
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  •  EVENT   2016 IEEE Workshop on Spoken Language Technology: Sponsored by MERL
    Date: Tuesday, December 13, 2016 - Friday, December 16, 2016
    Location: San Diego, California
    Research Area: Speech & Audio
    Brief
    • The IEEE Workshop on Spoken Language Technology is a premier international showcase for advances in spoken language technology. The theme for 2016 is "machine learning: from signal to concepts," which reflects the current excitement about end-to-end learning in speech and language processing. This year, MERL is showing its support for SLT as one of its top sponsors, along with Amazon and Microsoft.
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