News & Events

1,453 News items, Awards, Events and Talks related to MERL and its staff.

Learn about the MERL Seminar Series.

  •  AWARD    Best Paper Award of 2022 IPSJ Transactions on Consumer Devices & Systems
    Date: March 27, 2023
    Awarded to: Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik, Hiroshi Mineno
    MERL Contacts: Jianlin Guo; Philip V. Orlik; Kieran Parsons
    Research Areas: Communications, Signal Processing
    • MELCO/MERL research paper “IEEE 802.19.3 Standardization for Coexistence of IEEE 802.11ah and IEEE 802.15.4g Systems in Sub-1GHz Frequency Bands” has won the Best Paper Award of the 2022 IPSJ Transactions on Consumer Devices and Systems. The Information Processing Society of Japan (IPSJ) award was established in 1970 and is conferred on the authors of particularly excellent papers, which are published in the IPSJ journals and transactions. Our paper was published by the IPSJ Transaction on Consumer Device and System Vol. 29 in 2021 and authors are Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik and Hiroshi Mineno.
  •  TALK    [MERL Seminar Series 2023] Dr. Suraj Srinivas presents talk titled Pitfalls and Opportunities in Interpretable Machine Learning
    Date & Time: Tuesday, March 14, 2023; 1:00 PM
    Speaker: Suraj Srinivas, Harvard University
    MERL Host: Suhas Lohit
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    • In this talk, I will discuss our recent research on understanding post-hoc interpretability. I will begin by introducing a characterization of post-hoc interpretability methods as local function approximators, and the implications of this viewpoint, including a no-free-lunch theorem for explanations. Next, we shall challenge the assumption that post-hoc explanations provide information about a model's discriminative capabilities p(y|x) and instead demonstrate that many common methods instead rely on a conditional generative model p(x|y). This observation underscores the importance of being cautious when using such methods in practice. Finally, I will propose to resolve this via regularization of model structure, specifically by training low curvature neural networks, resulting in improved model robustness and stable gradients.
  •  TALK    [MERL Seminar Series 2023] Prof. Shaowu Pan presents talk titled Neural Implicit Flow
    Date & Time: Wednesday, March 1, 2023; 1:00 PM
    Speaker: Shaowu Pan, Rensselaer Polytechnic Institute
    MERL Host: Saviz Mowlavi
    Research Areas: Computational Sensing, Data Analytics, Machine Learning
    • High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional subspace. Engineering applications for modeling, characterization, design, and control of such large-scale systems often rely on dimensionality reduction to make solutions computationally tractable in real-time. Common existing paradigms for dimensionality reduction include linear methods, such as the singular value decomposition (SVD), and nonlinear methods, such as variants of convolutional autoencoders (CAE). However, these encoding techniques lack the ability to efficiently represent the complexity associated with spatio-temporal data, which often requires variable geometry, non-uniform grid resolution, adaptive meshing, and/or parametric dependencies. To resolve these practical engineering challenges, we propose a general framework called Neural Implicit Flow (NIF) that enables a mesh-agnostic, low-rank representation of large-scale, parametric, spatial-temporal data. NIF consists of two modified multilayer perceptrons (MLPs): (i) ShapeNet, which isolates and represents the spatial complexity, and (ii) ParameterNet, which accounts for any other input complexity, including parametric dependencies, time, and sensor measurements. We demonstrate the utility of NIF for parametric surrogate modeling, enabling the interpretable representation and compression of complex spatio-temporal dynamics, efficient many-spatial-query tasks, and improved generalization performance for sparse reconstruction.
  •  TALK    Prof. Kevin Lynch presents talk titled Autonomous and Human-Collaborative Robotic Manipulation
    Date & Time: Tuesday, February 28, 2023; 12:00 PM
    Speaker: Prof. Kevin Lynch, Northwestern University
    MERL Host: Diego Romeres
    Research Areas: Machine Learning, Robotics
    • Research at the Center for Robotics and Biosystems at Northwestern University includes bio-inspiration, neuromechanics, human-machine systems, and swarm robotics, among other topics. In this talk I will focus on our work on manipulation, including autonomous in-hand robotic manipulation and safe, intuitive human-collaborative manipulation among one or more humans and a team of mobile manipulators.
  •  NEWS    Jonathan Le Roux gives invited talk at CMU's Language Technology Institute Colloquium
    Date: December 9, 2022
    Where: Pittsburg, PA
    MERL Contact: Jonathan Le Roux
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    • MERL Senior Principal Research Scientist and Speech and Audio Senior Team Leader, Jonathan Le Roux, was invited by Carnegie Mellon University's Language Technology Institute (LTI) to give an invited talk as part of the LTI Colloquium Series. The LTI Colloquium is a prestigious series of talks given by experts from across the country related to different areas of language technologies. Jonathan's talk, entitled "Towards general and flexible audio source separation", presented an overview of techniques developed at MERL towards the goal of robustly and flexibly decomposing and analyzing an acoustic scene, describing in particular the Speech and Audio Team's efforts to extend MERL's early speech separation and enhancement methods to more challenging environments, and to more general and less supervised scenarios.
  •  NEWS    Rien Quirynen Appointed IPC Vice-Chair for the 8th IFAC Conference on NMPC 2024
    Date: August 27, 2024 - August 30, 2024
    Where: Kyoto, Japan
    MERL Contact: Rien Quirynen
    Research Areas: Control, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
    • MERL researcher Rien Quirynen has been appointed as Vice-Chair from Industry of the International Program Committee of the 8th IFAC Conference on Nonlinear Model Predictive Control, which will be held in Kyoto, Japan, in August 2024.

      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.
  •  NEWS    Chris Laughman delivered two seminar talks for at the School of Engineering at Penn State
    Date: February 16, 2023 - February 17, 2023
    Where: Pennsylvania State University
    MERL Contact: Christopher R. Laughman
    Research Areas: Control, Machine Learning, Multi-Physical Modeling
    • On February 16 and 17, Chris Laughman, Senior Team Leader of the Multiphysical Systems Team, presented lectures for the Systems, Robotics, and Controls Seminar Series in the School of Engineering, and for the Distinguished Speaker Series in Architectural Engineering. His talk was titled "Architectural Thermofluid Systems: Next-Generation Challenges and Opportunities," and described characteristics of these systems that require specific attention in model-based system engineering processes, as well as MERL research to address these challenges.
  •  TALK    [MERL Seminar Series 2023] Prof. Stefanie Tellex presents talk titled Towards Complex Language in Partially Observed Environments
    Date & Time: Tuesday, February 14, 2023; 12:00 PM
    Speaker: Stefanie Tellex, Brown University
    MERL Host: Daniel N. Nikovski
    Research Area: Robotics
    • Robots can act as a force multiplier for people, whether a robot assisting an astronaut with a repair on the International Space station, a UAV taking flight over our cities, or an autonomous vehicle driving through our streets. Existing approaches use action-based representations that do not capture the goal-based meaning of a language expression and do not generalize to partially observed environments. The aim of my research program is to create autonomous robots that can understand complex goal-based commands and execute those commands in partially observed, dynamic environments. I will describe demonstrations of object-search in a POMDP setting with information about object locations provided by language, and mapping between English and Linear Temporal Logic, enabling a robot to understand complex natural language commands in city-scale environments. These advances represent steps towards robots that interpret complex natural language commands in partially observed environments using a decision theoretic framework.
  •  TALK    [MERL Seminar Series 2023] Dr. Rupert Way presents talk titled Empirically Grounded Technology Forecasts and the Energy Transition
    Date & Time: Tuesday, January 31, 2023; 11:00 AM
    Speaker: Rupert way, University of Oxford
    MERL Host: Ye Wang
    • Rapidly decarbonising the global energy system is critical for addressing climate change, but concerns about costs have been a barrier to implementation. Historically, most energy-economy models have overestimated the future costs of renewable energy technologies and underestimated their deployment, thereby overestimating total energy transition costs. These issues have driven calls for alternative approaches and more reliable technology forecasting methods. We use an approach based on probabilistic cost forecasting methods to estimate future energy system costs in a variety of scenarios. Our findings suggest that, compared to continuing with a fossil fuel-based system, a rapid green energy transition will likely result in net savings of many trillions of dollars - even without accounting for climate damages or co-benefits of climate policy.
  •  NEWS    Anthony Vetro participates in CES panel on renewable energy
    Date: January 7, 2023
    Where: Las Vegas, NV
    MERL Contact: Anthony Vetro
    • Sustainability took center stage at the 2023 Consumer Electronics Show held in Las Vegas from Jan 5-8. Anthony Vetro, VP & Director at MERL, participated in a panel on "Renewable Energy, Renewable World" at CES 2023, where he spoke on renewable energy solutions including electric vehicles, energy resource management, and energy-efficient heat pumps.

      The panel was moderated by Hayden Fields, Senior Reporter at Morning Brew. Other panelists included Andrea Murphy (Director of Environmental Affairs and Sustainability, Panasonic) Enass Abo-Hamed (CEO, H2GO Power), and Giovanni Fili (Founder and CEO, Exeger).

      The video recording of the panel is available online:
      CES 2023 Panel on Renewable Energy, Renewable World

      Related article on sustainability panels at CES:
  •  AWARD    MERL paper wins major award from IEEE Computer Society
    Date: January 12, 2023
    Awarded to: William T. Freeman, Thouis R. Jones, and Egon C. Pasztor
    Awarded by: IEEE Computer Society
    Research Areas: Computer Vision, Machine Learning
    • The MERL paper entitled, "Example-Based Super-Resolution" by William T. Freeman, Thouis R. Jones, and Egon C. Pasztor, published in a 2002 issue of IEEE Computer Graphics and Applications, has been awarded a 2021 Test of Time Award by the IEEE Computer Society. This work was done while the principal investigator, Prof. Freeman, was a research scientist at MERL; he is now a Professor of Electrical Engineering and Computer Science at MIT.

      This best paper award recognizes regular or special issue papers published by the magazine that have made profound and long-lasting research impacts in bridging the theory and practice of computer graphics. "This paper is an early example of using learning for a low-level vision task and we are very proud of the pioneering work that MERL has done in this area prior to the deep learning revolution," says Anthony Vetro, VP & Director at MERL.
  •  TALK    [MERL Seminar Series 2022] William M. Sisson presents talk titled Sustainability, Innovation and Technology
    Date & Time: Tuesday, December 20, 2022; 1:00 PM
    Speaker: William M. Sisson, WBCSD North America
    MERL Host: Scott A. Bortoff
    • Sustainability today encompasses three interconnected imperatives that all businesses must face and help to address: the increasing impact of climate change, the degradation of natural systems, and the growth of inequality. Business leaders today are increasingly understanding, particularly with the engagement of capital markets, that investors, consumers, and other business stakeholders are setting expectations on how companies are responding to these challenges and preparing for their business impact. More and more companies have shifted from sustainability as a single function in the company to one the is integrated across the firm. This translates directly into how companies are rethinking their product design and innovation efforts for sustainability and the technologies they will require. Some product categories, like heating and air conditioning systems for buildings, are both a part of the problem as well as potentially offering real solutions.
  •  NEWS    Jianlin Guo recently delivered an invited talk at 2022 6th International Conference on Intelligent Manufacturing and Automation Engineering
    Date: December 15, 2022 - December 17, 2022
    MERL Contacts: Jianlin Guo; Philip V. Orlik; Kieran Parsons
    Research Areas: Artificial Intelligence, Data Analytics, Machine Learning
    • The performance of manufacturing systems is heavily affected by downtime – the time period that the system halts production due to system failure, anomalous operation, or intrusion. Therefore, it is crucial to detect and diagnose anomalies to allow predictive maintenance or intrusion detection to reduce downtime. This talk, titled "Anomaly detection and diagnosis in manufacturing systems using autoencoder", focuses on tackling the challenges arising from predictive maintenance in manufacturing systems. It presents a structured autoencoder and a pre-processed autoencoder for accurate anomaly detection, as well as a statistical-based algorithm and an autoencoder-based algorithm for anomaly diagnosis.
  •  NEWS    Yebin Wang delivered an invited industry talk at the 1st IEEE Industrial Electronics Society Annual On-Line Conference
    Date: December 9, 2022 - December 11, 2022
    MERL Contact: Yebin Wang
    Research Areas: Communications, Control, Optimization
    • Future factory, in the era of industry 4.0, is characterized by autonomy, digital twin, and mass customization. This talk, titled "Future factory automation and cyber-physical system: an industrial perspective," focuses on tackling the challenges arising from mass customization, for example reconfigurable machine controller and material flow.
  •  NEWS    MERL Researchers gave a Tutorial Talk on Quantum Machine Learning for Sensing and Communications at IEEE GLOBECOM
    Date: December 8, 2022
    MERL Contacts: Toshiaki Koike-Akino; Pu (Perry) Wang
    Research Areas: Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal Processing
    • On December 8, 2022, MERL researchers Toshiaki Koike-Akino and Pu (Perry) Wang gave a 3.5-hour tutorial presentation at the IEEE Global Communications Conference (GLOBECOM). The talk, titled "Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications," addressed recent trends, challenges, and advances in sensing and communications. P. Wang presented on use cases, industry trends, signal processing, and deep learning for Wi-Fi integrated sensing and communications (ISAC), while T. Koike-Akino discussed the future of deep learning, giving a comprehensive overview of artificial intelligence (AI) technologies, natural computing, emerging quantum AI, and their diverse applications. The tutorial was conducted remotely. MERL's quantum AI technology was partly reported in the recent press release (

      The IEEE GLOBECOM is a highly anticipated event for researchers and industry professionals in the field of communications. Organized by the IEEE Communications Society, the flagship conference is known for its focus on driving innovation in all aspects of the field. Each year, over 3,000 scientific researchers submit proposals for program sessions at the annual conference. The theme of this year's conference was "Accelerating the Digital Transformation through Smart Communications," and featured a comprehensive technical program with 13 symposia, various tutorials and workshops.
  •  NEWS    MERL's Quantum Machine Learning Technology Featured in Mitsubishi Electric Corporation Press Release
    Date: December 2, 2022
    MERL Contacts: Toshiaki Koike-Akino; Kieran Parsons; Pu (Perry) Wang; Ye Wang
    Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Signal Processing, Human-Computer Interaction
    • Mitsubishi Electric Corporation announced its development of a quantum artificial intelligence (AI) technology that automatically optimizes inference models to downsize the scale of computation with quantum neural networks. The new quantum AI technology can be integrated with classical machine learning frameworks for diverse solutions.

      Mitsubishi Electric has confirmed that the technology can be incorporated in the world's first applications for terahertz (THz) imaging, Wi-Fi indoor monitoring, compressed sensing, and brain-computer interfaces. The technology is based on recent research by MERL's Connectivity & Information Processing team and Computational Sensing team.

      Mitsubishi Electric's new quantum machine learning (QML) technology realizes compact inference models by fully exploiting the enormous capacity of quantum computers to express exponentially larger-state space with the number of quantum bits (qubits). In a hybrid combination of both quantum and classical AI, the technology can compensate for limitations of classical AI to achieve superior performance while significantly downsizing the scale of AI models, even when using limited data.
  •  NEWS    MERL researchers presenting workshop papers at NeurIPS 2022
    Date: December 2, 2022 - December 8, 2022
    MERL Contacts: Matthew Brand; Toshiaki Koike-Akino; Jing Liu; Saviz Mowlavi; Kieran Parsons; Ye Wang
    Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Signal Processing
    • In addition to 5 papers in recent news (, MERL researchers presented 2 papers at the NeurIPS Conference Workshop, which was held Dec. 2-8. NeurIPS is one of the most prestigious and competitive international conferences in machine learning.

      - “Optimal control of PDEs using physics-informed neural networks” by Saviz Mowlavi and Saleh Nabi

      Physics-informed neural networks (PINNs) have recently become a popular method for solving forward and inverse problems governed by partial differential equations (PDEs). By incorporating the residual of the PDE into the loss function of a neural network-based surrogate model for the unknown state, PINNs can seamlessly blend measurement data with physical constraints. Here, we extend this framework to PDE-constrained optimal control problems, for which the governing PDE is fully known and the goal is to find a control variable that minimizes a desired cost objective. We validate the performance of the PINN framework by comparing it to state-of-the-art adjoint-based optimization, which performs gradient descent on the discretized control variable while satisfying the discretized PDE.

      - “Learning with noisy labels using low-dimensional model trajectory” by Vasu Singla, Shuchin Aeron, Toshiaki Koike-Akino, Matthew E. Brand, Kieran Parsons, Ye Wang

      Noisy annotations in real-world datasets pose a challenge for training deep neural networks (DNNs), detrimentally impacting generalization performance as incorrect labels may be memorized. In this work, we probe the observations that early stopping and low-dimensional subspace learning can help address this issue. First, we show that a prior method is sensitive to the early stopping hyper-parameter. Second, we investigate the effectiveness of PCA, for approximating the optimization trajectory under noisy label information. We propose to estimate the low-rank subspace through robust and structured variants of PCA, namely Robust PCA, and Sparse PCA. We find that the subspace estimated through these variants can be less sensitive to early stopping, and can outperform PCA to achieve better test error when trained on noisy labels.

      - In addition, new MERL researcher, Jing Liu, also presented a paper entitled “CoPur: Certifiably Robust Collaborative Inference via Feature Purification" based on his previous work before joining MERL. His paper was elected as a spotlight paper to be highlighted in lightening talks and featured paper panel.
  •  AWARD    Arvind Raghunathan receives Roberto Tempo Best CDC Paper Award at 2022 IEEE Conference on Decision & Control (CDC)
    Date: December 8, 2022
    Awarded to: Arvind Raghunathan
    MERL Contact: Arvind Raghunathan
    Research Areas: Control, Optimization
    • Arvind Raghunathan, Senior Principal Research Scientist in the Data Analytics group, received the IEEE Control Systems Society Roberto Tempo Best CDC Paper Award. The award was presented at the 2022 IEEE Conference on Decision & Control (CDC).

      The award is given annually in honor of Roberto Tempo, the 44th President of the IEEE Control Systems Society (CSS). The Tempo Award Committee selects the best paper from the previous year's CDC based on originality, potential impact on any aspect of control theory, technology, or implementation, and for the clarity of writing. This year's award committee was headed by Prof. Patrizio Colaneri, Politecnico di Milano. Arvind's paper was nominated for the award by Prof. Lorenz Biegler, Carnegie Mellon University, with supporting letters from Prof. Andreas Waechter, Northwestern University, and Prof. Victor Zavala, University of Wisconsin-Madison.
  •  NEWS    MERL Researchers Presented Six Papers at the 2022 IEEE Conference on Decision and Control (CDC’22)
    Date: December 6, 2022 - December 9, 2022
    Where: Cancún, Mexico
    MERL Contacts: Mouhacine Benosman; Karl Berntorp; Ankush Chakrabarty; Marcus Greiff; Devesh K. Jha; Arvind Raghunathan; Diego Romeres; Yebin Wang
    Research Areas: Control, Optimization
    • MERL researchers presented six papers at the Conference on Decision and Control that was held in Cancún, Mexico from December 6-9, 2022. The papers covered a broad range of topics in the areas of decision making and control, including Bayesian optimization, quadratic programming, solution of differential equations, distributed Kalman filtering, thermal monitoring of batteries, and closed-loop control optimization.
  •  EVENT    Prof. Paris Smaragdis of UIUC to give keynote at MERL's Virtual Open House
    Date & Time: Monday, December 12, 2022; 1:00pm - 5:30pm
    Location: MERL, Virtual
    Speaker: Prof. Paris Smaragdis, University of Illinois at Urbana-Champaign
    • MERL is excited to announce the featured keynote speaker for our Virtual Open House 2022:
      Prof. Paris Smaragdis from University of Illinois at Urbana-Champaign.

      Our virtual open house will take place on December 12, 2022, 1:00pm - 5:30pm (EST).

      Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Smaragdis' talk is scheduled for 3:15pm - 3:45pm (EST).


      Keynote Title: Dragging Audio Processing Past the 1970s (and the 2010s!)
      Abstract: Audio processing has not changed appreciably in the last 50 years. However, novel tasks, new computational demands, attention to human-centered evaluation, and a strong influence from machine learning, all point towards new ways of thinking about sound. In this talk I will go over multiple examples of how one can modernize standard audio processing in order to serve ambitious project goals. I will specifically talk about the use of meta learning for adaptive filtering, and how we can outperform humans in the game of optimizer design; I will show new ways to represent and process time series based on graph networks that results in highly desirable scaling properties for audio and speech recognition; and I will also talk about how we can move towards unsupervised learning from real-world data in a way that (almost) matches curated data performance and allows highly-distributed learning from audio devices in the wild.

      Speaker Bio:
      Paris Smaragdis is a Professor and an Associate Department Head in the Computer Science department in the University of Illinois at Urbana-Champaign. He completer his graduate studies and postdoc at MIT in 2001. He has been a research scientist at Mitsubishi Electric Research Labs in Cambridge MA, a senior research scientist at Adobe Research, and an Amazon Scholar with AWS. His research lies in the intersection of signal processing and machine learning, where he has contributed multiple widely used methods for source separation and audio analysis throughout his 150+ publications and 60+ US and international patents. His research has been productized many times worldwide, has been widely used in personal computers and commercial systems, and has been used in award winning movies and music releases. He was recognized by the MIT Technology Review as one of the "world's top innovators under 35 years old" in 2006 (TR35 award) and he has received the IEEE Signal Processing Society (SPS) Best Paper Award twice (2017,2020). He was elected an IEEE Fellow (class of 2015), and selected as an IEEE SPS Distinguished Lecturer (2016-2017). Within IEEE SPS he has served as the chair the Machine Learning for Signal Processing Technical Committee, the Audio and Acoustic Signal Processing Technical Committee, and the Data Science Initiative. He has been elected to and served in the IEEE Signal Processing Society Board of Governors, and is currently the Editor in Chief of the ACM/IEEE Transactions on Audio, Speech, and Language Processing.
  •  NEWS    Karl Berntorp gave Spotlight Talk at CDC Workshop on Gaussian Process Learning-Based Control
    Date: December 5, 2022
    Where: Cancun, Mexico
    MERL Contact: Karl Berntorp
    Research Areas: Control, Machine Learning
    • Karl Berntorp was an invited speaker at the workshop on Gaussian Process Learning-Based Control organized at the Conference on Decision and Control (CDC) 2022 in Cancun, Mexico.

      The talk was part of a tutorial-style workshop aimed to provide insight into the fundamentals behind Gaussian processes for modeling and control and sketching some of the open challenges and opportunities using Gaussian processes for modeling and control. The talk titled ``Gaussian Processes for Learning and Control: Opportunities for Real-World Impact" described some of MERL's efforts in using Gaussian processes (GPs) for learning and control, with several application examples and discussing some of the key benefits and limitations with using GPs for learning-based control.
  •  EVENT    MERL's Virtual Open House 2022
    Date & Time: Monday, December 12, 2022; 1:00pm-5:30pm ET
    Location: Mitsubishi Electric Research Laboratories (MERL)/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, Digital Video
    • Join MERL's virtual open house on December 12th, 2022! Featuring a keynote, live sessions, research area booths, and opportunities to interact with our research team. Discover who we are and what we do, and learn about internship and employment opportunities.
  •  TALK    [MERL Seminar Series 2022] Dr Mathew Hampshire-Waugh presents talk titled Climate Change and the road to Net-Zero
    Date & Time: Tuesday, November 29, 2022; 1:00 PM
    Speaker: Mathew Hampshire-Waugh, Net-Zero Consulting Services LTD
    MERL Host: Ye Wang
    • A seminar based upon the Author’s bestselling book, CLIMATE CHANGE and the road to NET-ZERO. The session shall explore how humanity has broken free from the shackles of poverty, suffering, and war and for the first time in human history grown both population and prosperity. It will also delve into how a single species has reconfigured the natural world, repurposed the Earth’s resources, and begun to re-engineer the climate.

      Using these conflicting narratives, the talk will explore the science, economics, technology, and politics of climate change. Constructing an argument that demonstrates, under many energy transition pathways, solving global warming requires no trade-off between the economy and environment, present and future generations, or rich and poor. Ultimately concluding that a twenty-year transition to a zero-carbon system provides a win-win solution for all on planet Earth.
  •  NEWS    MERL researchers presenting five papers at NeurIPS 2022
    Date: November 29, 2022 - December 9, 2022
    Where: NeurIPS 2022
    MERL Contacts: Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Suhas Lohit
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
    • MERL researchers are presenting 5 papers at the NeurIPS Conference, which will be held in New Orleans from Nov 29-Dec 1st, with virtual presentations in the following week. NeurIPS is one of the most prestigious and competitive international conferences in machine learning.

      MERL papers in NeurIPS 2022:

      1. “AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments” by Sudipta Paul, Amit Roy-Chowdhary, and Anoop Cherian

      This work proposes a unified multimodal task for audio-visual embodied navigation where the navigating agent can also interact and seek help from a human/oracle in natural language when it is uncertain of its navigation actions. We propose a multimodal deep hierarchical reinforcement learning framework for solving this challenging task that allows the agent to learn when to seek help and how to use the language instructions. AVLEN agents can interact anywhere in the 3D navigation space and demonstrate state-of-the-art performances when the audio-goal is sporadic or when distractor sounds are present.

      2. “Learning Partial Equivariances From Data” by David W. Romero and Suhas Lohit

      Group equivariance serves as a good prior improving data efficiency and generalization for deep neural networks, especially in settings with data or memory constraints. However, if the symmetry groups are misspecified, equivariance can be overly restrictive and lead to bad performance. This paper shows how to build partial group convolutional neural networks that learn to adapt the equivariance levels at each layer that are suitable for the task at hand directly from data. This improves performance while retaining equivariance properties approximately.

      3. “Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation” by Moitreya Chatterjee, Narendra Ahuja, and Anoop Cherian

      There often exist strong correlations between the 3D motion dynamics of a sounding source and its sound being heard, especially when the source is moving towards or away from the microphone. In this paper, we propose an audio-visual scene-graph that learns and leverages such correlations for improved visually-guided audio separation from an audio mixture, while also allowing predicting the direction of motion of the sound source.

      4. “What Makes a "Good" Data Augmentation in Knowledge Distillation - A Statistical Perspective” by Huan Wang, Suhas Lohit, Michael Jones, and Yun Fu

      This paper presents theoretical and practical results for understanding what makes a particular data augmentation technique (DA) suitable for knowledge distillation (KD). We design a simple metric that works very well in practice to predict the effectiveness of DA for KD. Based on this metric, we also propose a new data augmentation technique that outperforms other methods for knowledge distillation in image recognition networks.

      5. “FeLMi : Few shot Learning with hard Mixup” by Aniket Roy, Anshul Shah, Ketul Shah, Prithviraj Dhar, Anoop Cherian, and Rama Chellappa

      Learning from only a few examples is a fundamental challenge in machine learning. Recent approaches show benefits by learning a feature extractor on the abundant and labeled base examples and transferring these to the fewer novel examples. However, the latter stage is often prone to overfitting due to the small size of few-shot datasets. In this paper, we propose a novel uncertainty-based criteria to synthetically produce “hard” and useful data by mixing up real data samples. Our approach leads to state-of-the-art results on various computer vision few-shot benchmarks.
  •  NEWS    Members of the Speech & Audio team elected to IEEE Technical Committee
    Date: November 28, 2022
    MERL Contacts: Francois Germain; Gordon Wichern
    Research Area: Speech & Audio
    • Gordon Wichern and François Germain have been elected for 3-year terms to the IEEE Audio and Acoustic Signal Processing Technical Committee (AASP TC) of the IEEE Signal Processing Society.

      The AASP TC's mission is to support, nourish, and lead scientific and technological development in all areas of audio and acoustic signal processing. It numbers 30 or so appointed volunteer members drawn roughly equally from leading academic and industrial organizations around the world, unified by the common aim to offer their expertise in the service of the scientific community.