News & Events

151 Events and Talks were found.




  •  TALK   Prof. Pere Gilabert gave an invited talk at MERL on Machine Learning for Digital Predistortion Linearization of High Efficient Power Amplifier
    Date & Time: Tuesday, February 16, 2021; 11:00-12:00
    Speaker: Prof. Pere Gilabert, Universitat Politecnica de Catalunya, Barcelona, Spain
    MERL Host: Rui Ma
    Research Areas: Communications, Electronic and Photonic Devices, Machine Learning, Signal Processing
    Brief
    • Digital predistortion (DPD) linearization is the most common and spread solution to cope with power amplifiers (PA) inherent linearity versus efficiency trade-off. The use of new radio 5G spectrally efficient signals with high peak-to-average power ratios (PAPR) occupying wider bandwidths only aggravates such compromise. When considering wide bandwidth signals, carrier aggregation or multi-band configurations in high efficient transmitter architectures, such as Doherty PAs, load-modulated balanced amplifiers, envelope tracking PAs or outphasing transmitters, the number of parameters required in the DPD model to compensate for both nonlinearities and memory effects can be unacceptably high. This has a negative impact in the DPD model extraction/adaptation, because it increases the computational complexity and drives to over-fitting and uncertainty.
      This talk will discuss the use of machine learning techniques for DPD linearization. The use of artificial neural networks (ANNs) for adaptive DPD linearization and approaches to reduce the coefficients adaptation time will be discussed. In addition, an overview on several feature-extraction techniques used to reduce the number of parameters of the DPD linearization system as well as to ensure proper, well-conditioned estimation for related variables will be presented.
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  •  EVENT   MERL Virtual Open House 2020
    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
    Brief
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  •  EVENT   Saleh Nabi gave an invited talk at the Department of Mechanical Engineering at Rice University
    Date: Wednesday, September 30, 2020
    MERL Contact: Saleh Nabi
    Location: Rice University
    Research Areas: Dynamical Systems, Optimization
    Brief
    • MERL researcher Dr. S. Nabi was invited to give a talk on the state-of-the-art methods for airflow optimization and control at Rice University. Several industrial applications to buoyancy-driven flows in the built environment, atmospheric flows, and prevention of transmission of COVID-19 were discussed. Furthermore, some novel advances on data-driven fluid mechanics for industrial applications were covered.
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  •  TALK   Microwaving a Biological Cell Alive ‒ Broadband Label-Free Noninvasive Electrical Characterization of a Live Cell
    Date & Time: Tuesday, August 25, 2020; 11:00 AM
    Speaker: Prof. James Hwang, Cornell University
    MERL Host: Rui Ma
    Research Areas: Applied Physics, Electronic and Photonic Devices
    Brief
    • Microwave is not just for cooking, smart cars, or mobile phones. We can take advantage of the wide electromagnetic spectrum to do wonderful things that are more vital to our lives. For example, microwave ablation of cancer tumor is already in wide use, and microwave remote monitoring of vital signs is becoming more important as the population ages. This talk will focus on a biomedical use of microwave at the single-cell level. At low power, microwave can readily penetrate a cell membrane to interrogate what is inside a cell, without cooking it or otherwise hurting it. It is currently the fastest, most compact, and least costly way to tell whether a cell is alive or dead. On the other hand, at higher power but lower frequency, the electromagnetic signal can interact strongly with the cell membrane to drill temporary holes of nanometer size. The nanopores allow drugs to diffuse into the cell and, based on the reaction of the cell, individualized medicine can be developed and drug development can be sped up in general. Conversely, the nanopores allow strands of DNA molecules to be pulled out of the cell without killing it, which can speed up genetic engineering. Lastly, by changing both the power and frequency of the signal, we can have either positive or negative dielectrophoresis effects, which we have used to coerce a live cell to the examination table of Dr. Microwave, then usher it out after examination. These interesting uses of microwave and the resulted fundamental knowledge about biological cells will be explored in the talk.
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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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  •  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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  •  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   Perspectives on Integer Programming in Sparse Optimization
    Date & Time: Tuesday, July 16, 2019; 12:00 PM
    Speaker: Prof. Jeff Linderoth, University of Wisconsin-Madison
    MERL Host: Arvind Raghunathan
    Research Areas: Machine Learning, Optimization
    Brief
    • Algorithms to solve mixed integer linear programs have made incredible progress in the past 20 years. Key to these advances has been a mathematical analysis of the structure of the set of feasible solutions. We argue that a similar analysis is required in the case of mixed integer quadratic programs, like those that arise in sparse optimization in machine learning. One such analysis leads to the so-called perspective relaxation, which significantly improves solution performance on separable instances. Extensions of the perspective reformulation can lead to algorithms that are equivalent to some of the most popular, modern, sparsity-inducing non-convex regularizations in variable selection. Based on joint work with Hongbo Dong (Washington State Univ. ), Oktay Gunluk (IBM), and Kun Chen (Univ. Connecticut).
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  •  EVENT   MERL Hosts Annual Women in Science Luncheon
    Date & Time: Tuesday, June 18, 2019; 12:00PM
    Speaker: Beverly Shultz
    MERL Contacts: Marissa Deegan; Elizabeth Phillips
    Location: 201 Broadway, Cambridge, MA
    Brief
    • MERL hosted its annual "Women In Science Luncheon" to celebrate and inspire the Lab's team of female researchers, PhD student interns and members of the HQ staff. Beverly Shultz, author of "Skip the Typing Test, I’ll Manage the Software-A Woman’s Pioneering Journey in High Tech” joined the event to share her insights as a successful female engineer, who brought passion and technology business acumen to the male-dominated computer revolution.

      Beverly was a former Vice President of Engineering at Mitsubishi Electric of America and responsible to produce several versions of an early volume rendering product. She was the first female recipient of the MELCO’s President’s Award for Technology, for this work.
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  •  TALK   MERL Low-Thrust GEO Satellite Control talk at Stanford University
    Date & Time: Thursday, February 14, 2019; 1:30 -3:00 PM
    Speaker: Avishai Weiss, MERL
    MERL Hosts: Stefano Di Cairano; Uroš Kalabić; Avishai Weiss
    Research Area: Control
    Brief
    • Avishai Weiss from MERL's Control and Dynamical Systems group will give a talk at Stanford's Aeronautics and Astronautics department titled: "Low-Thrust GEO Satellite Station Keeping, Attitude Control, and Momentum Management via Model Predictive Control". Electric propulsion for satellites is much more fuel efficient than conventional methods. The talk will describe MERL's solution to the satellite control problems deriving from the low thrust provided by electric propulsion.
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  •  EVENT   MERL is a Proud Sponsor of the Grace Hopper Celebration 2018!
    Date: Wednesday, September 26, 2018 - Friday, September 28, 2018
    MERL Contacts: Chiori Hori; Elizabeth Phillips
    Location: Houston, Texas
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    Brief
    • "MERL, in partnership with Mitsubishi Electric was a Gold Sponsor of the Grace Hopper Celebration 2018 (GHC18) held in Houston, TX on September 26-28th. Presented by AnitaB.org and the Association for Computing Machinery, this is world's largest gathering of women technologists. Chiori Hori and Elizabeth Phillips from MERL, and Yoshiyuki Umei, Jared Baker and Lien Randle from MEUS, proudly represented Mitsubishi Electric at the recruiting expo, that drew over 20,000 female technologists this year.
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  •  EVENT   Dr. Petros Boufounos is co-organizing workshop on the Intersection of Information Theory and Signal Processing
    Date: Sunday, October 28, 2018 - Friday, November 2, 2018
    MERL Contact: Petros Boufounos
    Location: Banff International Research Station (BIRS), Alberta, Canada
    Research Areas: Computational Sensing, Signal Processing
    Brief
    • Dr. Petros Boufounos, Prof. Stark Draper (U. of Toronto) and Prof. Yonina Eldar (Technion) are co-organizing a workshop on the intersection of Information Theory and Signal Processing. The 5-day workshop will take place Oct. 28 - Nov. 2 at the Banff International Research Station (BIRS) in Alberta, Canada. The workshop schedule includes invited talks from prominent researchers in the two fields, coming together from all over the world. Parts of the workshop will be streamed live through the BIRS website.
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  •  EVENT   MERL 3rd Annual Open House
    Date & Time: Thursday, November 29, 2018; 4-6pm
    MERL Contacts: Marissa Deegan; Elizabeth Phillips; Jeroen van Baar; Anthony Vetro
    Location: 201 Broadway, 8th floor, Cambridge, MA
    Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio
    Brief
    • Snacks, demos, science: On Thursday 11/29, Mitsubishi Electric Research Labs (MERL) will host an open house for graduate+ students interested in internships, post-docs, and research scientist positions. The event will be held from 4-6pm and will feature demos & short presentations in our main areas of research including artificial intelligence, robotics, computer vision, speech processing, optimization, machine learning, data analytics, signal processing, communications, sensing, control and dynamical systems, as well as multi-physyical modeling and electronic devices. MERL is a high impact publication-oriented research lab with very extensive internship and university collaboration programs. Most internships lead to publication; many of our interns and staff have gone on to notable careers at MERL and in academia. Come mix with our researchers, see our state of the art technologies, and learn about our research opportunities. Dress code: casual, with resumes.

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

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

      Information about working at MERL:
      www.merl.com/employment.
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  •  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; 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: 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 Area: Machine Learning
    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
    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: 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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