News & Events

207 News items, Awards, Events or Talks found.


  •  AWARD    MERL’s Paper on Wi-Fi Sensing Earns Top 3% Paper Recognition at ICASSP 2023, Selected as a Best Student Paper Award Finalist
    Date: June 9, 2023
    Awarded to: Cristian J. Vaca-Rubio, Pu Wang, Toshiaki Koike-Akino, Ye Wang, Petros Boufounos and Petar Popovski
    MERL Contacts: Petros T. Boufounos; Toshiaki Koike-Akino; Pu (Perry) Wang; Ye Wang
    Research Areas: Artificial Intelligence, Communications, Computational Sensing, Dynamical Systems, Machine Learning, Signal Processing
    Brief
    • A MERL Paper on Wi-Fi sensing was recognized as a Top 3% Paper among all 2709 accepted papers at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). Co-authored by Cristian Vaca-Rubio and Petar Popovski from Aalborg University, Denmark, and MERL researchers Pu Wang, Toshiaki Koike-Akino, Ye Wang, and Petros Boufounos, the paper "MmWave Wi-Fi Trajectory Estimation with Continous-Time Neural Dynamic Learning" was also a Best Student Paper Award finalist.

      Performed during Cristian’s stay at MERL first as a visiting Marie Skłodowska-Curie Fellow and then as a full-time intern in 2022, this work capitalizes on standards-compliant Wi-Fi signals to perform indoor localization and sensing. The paper uses a neural dynamic learning framework to address technical issues such as low sampling rate and irregular sampling intervals.

      ICASSP, a flagship conference of the IEEE Signal Processing Society (SPS), was hosted on the Greek island of Rhodes from June 04 to June 10, 2023. ICASSP 2023 marked the largest ICASSP in history, boasting over 4000 participants and 6128 submitted papers, out of which 2709 were accepted.
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  •  AWARD    Joint CMU-MERL team wins DCASE2023 Challenge on Automated Audio Captioning
    Date: June 1, 2023
    Awarded to: Shih-Lun Wu, Xuankai Chang, Gordon Wichern, Jee-weon Jung, Francois Germain, Jonathan Le Roux, Shinji Watanabe
    MERL Contacts: François Germain; Jonathan Le Roux; Gordon Wichern
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Brief
    • A joint team consisting of members of CMU Professor and MERL Alumn Shinji Watanabe's WavLab and members of MERL's Speech & Audio team ranked 1st out of 11 teams in the DCASE2023 Challenge's Task 6A "Automated Audio Captioning". The team was led by student Shih-Lun Wu and also featured Ph.D. candidate Xuankai Chang, Postdoctoral research associate Jee-weon Jung, Prof. Shinji Watanabe, and MERL researchers Gordon Wichern, Francois Germain, and Jonathan Le Roux.

      The IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE Challenge), started in 2013, has been organized yearly since 2016, and gathers challenges on multiple tasks related to the detection, analysis, and generation of sound events. This year, the DCASE2023 Challenge received over 428 submissions from 123 teams across seven tasks.

      The CMU-MERL team competed in the Task 6A track, Automated Audio Captioning, which aims at generating informative descriptions for various sounds from nature and/or human activities. The team's system made strong use of large pretrained models, namely a BEATs transformer as part of the audio encoder stack, an Instructor Transformer encoding ground-truth captions to derive an audio-text contrastive loss on the audio encoder, and ChatGPT to produce caption mix-ups (i.e., grammatical and compact combinations of two captions) which, together with the corresponding audio mixtures, increase not only the amount but also the complexity and diversity of the training data. The team's best submission obtained a SPIDEr-FL score of 0.327 on the hidden test set, largely outperforming the 2nd best team's 0.315.
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  •  NEWS    MERL researchers presenting four papers and co-organizing a workshop at CVPR 2023
    Date: June 18, 2023 - June 22, 2023
    Where: Vancouver/Canada
    MERL Contacts: Anoop Cherian; Michael J. Jones; Suhas Lohit; Kuan-Chuan Peng
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    Brief
    • MERL researchers are presenting 4 papers and co-organizing a workshop at the CVPR 2023 conference, which will be held in Vancouver, Canada June 18-22. CVPR is one of the most prestigious and competitive international conferences in computer vision. Details are provided below.

      1. “Are Deep Neural Networks SMARTer than Second Graders,” by Anoop Cherian, Kuan-Chuan Peng, Suhas Lohit, Kevin Smith, and Joshua B. Tenenbaum

      We present SMART: a Simple Multimodal Algorithmic Reasoning Task and the associated SMART-101 dataset for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed for children in the 6-8 age group. Our experiments using SMART-101 reveal that powerful deep models are not better than random accuracy when analyzed for generalization. We also evaluate large language models (including ChatGPT) on a subset of SMART-101 and find that while these models show convincing reasoning abilities, their answers are often incorrect.

      Paper: https://arxiv.org/abs/2212.09993

      2. “EVAL: Explainable Video Anomaly Localization,” by Ashish Singh, Michael J. Jones, and Erik Learned-Miller

      This work presents a method for detecting unusual activities in videos by building a high-level model of activities found in nominal videos of a scene. The high-level features used in the model are human understandable and include attributes such as the object class and the directions and speeds of motion. Such high-level features allow our method to not only detect anomalous activity but also to provide explanations for why it is anomalous.

      Paper: https://arxiv.org/abs/2212.07900

      3. "Aligning Step-by-Step Instructional Diagrams to Video Demonstrations," by Jiahao Zhang, Anoop Cherian, Yanbin Liu, Yizhak Ben-Shabat, Cristian Rodriguez, and Stephen Gould

      The rise of do-it-yourself (DIY) videos on the web has made it possible even for an unskilled person (or a skilled robot) to imitate and follow instructions to complete complex real world tasks. In this paper, we consider the novel problem of aligning instruction steps that are depicted as assembly diagrams (commonly seen in Ikea assembly manuals) with video segments from in-the-wild videos. We present a new dataset: Ikea Assembly in the Wild (IAW) and propose a contrastive learning framework for aligning instruction diagrams with video clips.

      Paper: https://arxiv.org/pdf/2303.13800.pdf

      4. "HaLP: Hallucinating Latent Positives for Skeleton-Based Self-Supervised Learning of Actions," by Anshul Shah, Aniket Roy, Ketul Shah, Shlok Kumar Mishra, David Jacobs, Anoop Cherian, and Rama Chellappa

      In this work, we propose a new contrastive learning approach to train models for skeleton-based action recognition without labels. Our key contribution is a simple module, HaLP: Hallucinating Latent Positives for contrastive learning. HaLP explores the latent space of poses in suitable directions to generate new positives. Our experiments using HaLP demonstrates strong empirical improvements.

      Paper: https://arxiv.org/abs/2304.00387

      The 4th Workshop on Fair, Data-Efficient, and Trusted Computer Vision

      MERL researcher Kuan-Chuan Peng is co-organizing the fourth Workshop on Fair, Data-Efficient, and Trusted Computer Vision (https://fadetrcv.github.io/2023/) in conjunction with CVPR 2023 on June 18, 2023. This workshop provides a focused venue for discussing and disseminating research in the areas of fairness, bias, and trust in computer vision, as well as adjacent domains such as computational social science and public policy.
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  •  NEWS    Mitsubishi Electric Corporation Press Release Announces Worlds First GaN Power Amplifier Capable of Wideband Operation for 4G, 5G and Beyond 5G/6G.
    Date: June 8, 2023
    MERL Contacts: Toshiaki Koike-Akino; Koon Hoo Teo
    Research Areas: Communications, Electronic and Photonic Devices, Machine Learning, Signal Processing
    Brief
    • Mitsubishi Electric Corporation announced today it has developed what is believed to be the world's first gallium nitride (GaN) power amplifier that achieves a frequency range of 3,400MHz using a single power amplifier, which the company has demonstrated can be used for 4G, 5G and Beyond 5G/6G communication systems operating at different frequencies in a single base station. The amplifier is expected to enable the radio unit (transceiver) to be shared with different communication systems and lead to more power-efficient base stations.

      Mitsubishi Electric Researchers, Toshiaki Koike-Akino and Koon Hoo Teo helped developed the technology and device. Technical details will be presented at the IEEE International Microwave Symposium 2023 this month.

      Please see the link below for the full press release from Mitsubishi Electric.
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  •  NEWS    Ankush Chakrabarty co-organized three sessions at the ACC2023, and was nominated for Best Energy Systems Paper.
    Date: June 30, 2023 - June 2, 2023
    Where: San Diego, CA
    MERL Contact: Ankush Chakrabarty
    Research Areas: Applied Physics, Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
    Brief
    • Ankush Chakrabarty (researcher, Multiphysical Systems Team) co-organized and spoke at 3 sessions at the 2023 American Control Conference in San Diego, CA. These include: (1) A tutorial session (w/ Stefano Di Cairano) on "Physics Informed Machine Learning for Modeling and Control": an effort with contributions from multiple academic institutes and US research labs; (2) An invited session on "Energy Efficiency in Smart Buildings and Cities" in which his paper (w/ Chris Laughman) on "Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems" was nominated for Best Energy Systems Paper Award; and, (3) A special session on Diversity, Equity, and Inclusion to improve recruitment and retention of underrepresented groups in STEM research.
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  •  NEWS    MERL researchers present 10 papers at the American Control Conference (ACC)
    Date: May 31, 2023 - June 2, 2023
    Where: San Diego, CA
    MERL Contacts: Karl Berntorp; Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Devesh K. Jha; Christopher R. Laughman; Arvind Raghunathan; Diego Romeres; Abraham P. Vinod; Yebin Wang; Avishai Weiss
    Research Areas: Control, Machine Learning, Optimization
    Brief
    • MERL will present 10 papers at the American Control Conference (ACC) in San Diego, CA, with topics including autonomous-vehicle decision making and control, physics-informed machine learning, motion planning, control subject to nonconvex chance constraints, and optimal power management. Two talks are part of tutorial sessions.
      MERL will also be present at the conference as a sponsor, with a booth for discussing with researchers and students, and hosting a special session at lunch with highlights of MERL research and work philosophy.
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  •  NEWS    MERL Researchers Present Thirteen Papers at the 2023 IEEE International Conference on Robotics and Automation (ICRA)
    Date: May 29, 2023 - June 2, 2023
    Where: 2023 IEEE International Conference on Robotics and Automation (ICRA)
    MERL Contacts: Anoop Cherian; Radu Corcodel; Siddarth Jain; Devesh K. Jha; Toshiaki Koike-Akino; Tim K. Marks; Daniel N. Nikovski; Arvind Raghunathan; Diego Romeres
    Research Areas: Computer Vision, Machine Learning, Optimization, Robotics
    Brief
    • MERL researchers will present thirteen papers, including eight main conference papers and five workshop papers, at the 2023 IEEE International Conference on Robotics and Automation (ICRA) to be held in London, UK from May 29 to June 2. ICRA is one of the largest and most prestigious conferences in the robotics community. The papers cover a broad set of topics in Robotics including estimation, manipulation, vision-based object recognition and segmentation, tactile estimation and tool manipulation, robotic food handling, robot skill learning, and model-based reinforcement learning.

      In addition to the paper presentations, MERL robotics researchers will also host an exhibition booth and look forward to discussing our research with visitors.
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  •  NEWS    MERL researchers presented four papers and organized a special session at The 14th IEEE International Electric Machines and Drives Conference
    Date: May 15, 2023 - May 18, 2023
    Where: San Francisco, CA
    MERL Contacts: Dehong Liu; Bingnan Wang
    Research Areas: Applied Physics, Control, Electric Systems, Machine Learning, Optimization, Signal Processing
    Brief
    • MERL researchers Yusuke Sakamoto, Anantaram Varatharajan, and
      Bingnan Wang presented four papers at IEMDC 2023 held May 15-18 in San Francisco, CA. The topics of the four oral presentations range from electric machine design optimization, to fault detection and sensorless control. Bingnan Wang organized a special session at the conference entitled: Learning-based Electric Machine Design and Optimization. Bingnan Wang and Yusuke Sakamoto together chaired the special session, as well as a session on: Condition Monitoring, Fault Diagnosis and Prognosis.

      The 14th IEEE International Electric Machines and Drives Conference: IEMDC 2023, is one of the major conferences in the area of electric machines and drives. The conference was established in 1997 and has taken place every two years thereafter.
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  •  EVENT    MERL Contributes to ICASSP 2023
    Date: Sunday, June 4, 2023 - Saturday, June 10, 2023
    Location: Rhodes Island, Greece
    MERL Contacts: Petros T. Boufounos; François Germain; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Suhas Lohit; Yanting Ma; Hassan Mansour; Joshua Rapp; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
    Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Signal Processing, Speech & Audio
    Brief
    • MERL has made numerous contributions to both the organization and technical program of ICASSP 2023, which is being held in Rhodes Island, Greece from June 4-10, 2023.

      Organization

      Petros Boufounos is serving as General Co-Chair of the conference this year, where he has been involved in all aspects of conference planning and execution.

      Perry Wang is the organizer of a special session on Radar-Assisted Perception (RAP), which will be held on Wednesday, June 7. The session will feature talks on signal processing and deep learning for radar perception, pose estimation, and mutual interference mitigation with speakers from both academia (Carnegie Mellon University, Virginia Tech, University of Illinois Urbana-Champaign) and industry (Mitsubishi Electric, Bosch, Waveye).

      Anthony Vetro is the co-organizer of the Workshop on Signal Processing for Autonomous Systems (SPAS), which will be held on Monday, June 5, and feature invited talks from leaders in both academia and industry on timely topics related to autonomous systems.

      Sponsorship

      MERL is proud to be a Silver Patron of the conference and will participate in the student job fair on Thursday, June 8. Please join this session to learn more about employment opportunities at MERL, including openings for research scientists, post-docs, and interns.

      MERL is pleased to be the sponsor of two IEEE Awards that will be presented at the conference. We congratulate Prof. Rabab Ward, the recipient of the 2023 IEEE Fourier Award for Signal Processing, and Prof. Alexander Waibel, the recipient of the 2023 IEEE James L. Flanagan Speech and Audio Processing Award.

      Technical Program

      MERL is presenting 13 papers in the main conference on a wide range of topics including source separation and speech enhancement, radar imaging, depth estimation, motor fault detection, time series recovery, and point clouds. One workshop paper has also been accepted for presentation on self-supervised music source separation.

      Perry Wang has been invited to give a keynote talk on Wi-Fi sensing and related standards activities at the Workshop on Integrated Sensing and Communications (ISAC), which will be held on Sunday, June 4.

      Additionally, Anthony Vetro will present a Perspective Talk on Physics-Grounded Machine Learning, which is scheduled for Thursday, June 8.

      About ICASSP

      ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year.
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  •  TALK    [MERL Seminar Series 2023] Prof. Dan Stowell presents talk titled Fine-grained wildlife sound recognition: Towards the accuracy of a naturalist
    Date & Time: Tuesday, April 25, 2023; 11:00 AM
    Speaker: Dan Stowell, Tilburg University / Naturalis Biodiversity Centre
    MERL Host: Gordon Wichern
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Abstract
    • Machine learning can be used to identify animals from their sound. This could be a valuable tool for biodiversity monitoring, and for understanding animal behaviour and communication. But to get there, we need very high accuracy at fine-grained acoustic distinctions across hundreds of categories in diverse conditions. In our group we are studying how to achieve this at continental scale. I will describe aspects of bioacoustic data that challenge even the latest deep learning workflows, and our work to address this. Methods covered include adaptive feature representations, deep embeddings and few-shot learning.
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  •  TALK    [MERL Seminar Series 2023] Dr. Michael Muehlebach presents talk titled Learning and Dynamical Systems
    Date & Time: Tuesday, April 11, 2023; 11:00 AM
    Speaker: Michael Muehlebach, Max Planck Institute for Intelligent Systems
    Research Areas: Control, Dynamical Systems, Machine Learning, Optimization, Robotics
    Abstract
    • The talk will be divided into two parts. The first part of the talk introduces a class of first-order methods for constrained optimization that are based on an analogy to non-smooth dynamical systems. The key underlying idea is to express constraints in terms of velocities instead of positions, which has the algorithmic consequence that optimizations over feasible sets at each iteration are replaced with optimizations over local, sparse convex approximations. This results is a simplified suite of algorithms and an expanded range of possible applications in machine learning. In the second part of my talk, I will present a robot learning algorithm for trajectory tracking. The method incorporates prior knowledge about the system dynamics and by optimizing over feedforward actions, the risk of instability during deployment is mitigated. The algorithm will be evaluated on a ping-pong playing robot that is actuated by soft pneumatic muscles.
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  •  TALK    [MERL Seminar Series 2023] Prof. Zoltan Nagy presents talk titled Investigating Multi-Agent Reinforcement Learning for Grid-Interactive Smart Communities using CityLearn
    Date & Time: Wednesday, March 29, 2023; 1:00 PM
    Speaker: Zoltan Nagy, The University of Texas at Austin
    MERL Host: Ankush Chakrabarty
    Research Areas: Control, Machine Learning, Multi-Physical Modeling
    Abstract
    • The decarbonization of buildings presents new challenges for the reliability of the electrical grid because of the intermittency of renewable energy sources and increase in grid load brought about by end-use electrification. To restore reliability, grid-interactive efficient buildings can provide flexibility services to the grid through demand response. Residential demand response programs are hindered by the need for manual intervention by customers. To maximize the energy flexibility potential of residential buildings, an advanced control architecture is needed. Reinforcement learning is well-suited for the control of flexible resources as it can adapt to unique building characteristics compared to expert systems. Yet, factors hindering the adoption of RL in real-world applications include its large data requirements for training, control security and generalizability. This talk will cover some of our recent work addressing these challenges. We proposed the MERLIN framework and developed a digital twin of a real-world 17-building grid-interactive residential community in CityLearn. We show that 1) independent RL-controllers for batteries improve building and district level KPIs compared to a reference RBC by tailoring their policies to individual buildings, 2) despite unique occupant behaviors, transferring the RL policy of any one of the buildings to other buildings provides comparable performance while reducing the cost of training, 3) training RL-controllers on limited temporal data that does not capture full seasonality in occupant behavior has little effect on performance. Although, the zero-net-energy (ZNE) condition of the buildings could be maintained or worsened because of controlled batteries, KPIs that are typically improved by ZNE condition (electricity price and carbon emissions) are further improved when the batteries are managed by an advanced controller.
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  •  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
    Abstract
    • 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.
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  •  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
    Abstract
    • 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.
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  •  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
    Abstract
    • 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.
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  •  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
    Brief
    • 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.
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  •  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
    Research Areas: Control, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
    Brief
    • 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.
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  •  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
    Brief
    • 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.
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  •  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
    Brief
    • 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.
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  •  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
    Brief
    • 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.
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  •  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
    Brief
    • 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 (https://us.mitsubishielectric.com/en/news/releases/global/2022/1202-a/index.html).

      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.
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  •  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
    Brief
    • In addition to 5 papers in recent news (https://www.merl.com/news/news-20221129-1450), 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.
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  •  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
    Brief
    • 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.
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  •  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
    Brief
    • 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.
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  •  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
    Brief
    • 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.
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