News & Events

1,516 News items, Awards, Events and Talks related to MERL and its staff.


  •  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    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
    Brief
    • 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.
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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'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    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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  •  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
    Brief
    • 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.
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  •  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; Devesh K. Jha; Arvind Raghunathan; Diego Romeres; Yebin Wang
    Research Areas: Control, Optimization
    Brief
    • 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.
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  •  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
    Brief
    • 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).

      Registration: https://mailchi.mp/merl/voh2022

      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.
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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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  •  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
    Abstract
    • 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.
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  •  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
    Brief
    • 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.
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  •  NEWS    Members of the Speech & Audio team elected to IEEE Technical Committee
    Date: November 28, 2022
    MERL Contacts: François Germain; Gordon Wichern
    Research Area: Speech & Audio
    Brief
    • 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.
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  •  NEWS    Bingnan Wang gave seminar talk at WEMPEC in University of Wisconsin-Madison
    Date: October 28, 2022
    MERL Contacts: Dehong Liu; Bingnan Wang; Jinyun Zhang
    Research Areas: Applied Physics, Data Analytics, Multi-Physical Modeling
    Brief
    • MERL researcher Bingnan Wang gave seminar talk at Wisconsin Electric Machines and Power Electronics Consortium (WEMPEC), which is recognized globally for its sustained contributions to electric machines and power electronics technology. He gave an overview of MERL research, especially on electric machines, and introduced our recent work on quantitative eccentricity fault diagnosis technologies for electric motors, including physical-model approach using improved winding function theory, and data-driven approach using topological data analysis to effectively differentiate signals from different fault conditions.

      The seminar was given on Teams. MERL researchers Jin Zhang, Dehong Liu, Yusuke Sakamoto and Bingnan Wang held meetings with WEMPEC faculty members before the seminar to discuss various research topics, and met virtually with students after the talk.
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  •  NEWS    Rien Quirynen to give an invited talk at the University of California Santa Cruz
    Date: November 14, 2022
    Where: Zoom
    Research Areas: Control, Dynamical Systems, Optimization, Robotics
    Brief
    • Rien Quirynen will give an invited talk at the Electrical and Computer Engineering Department, University of California Santa Cruz on "Real-time Motion Planning and Predictive Control by Mixed-integer Programming for Autonomous Vehicles". The talk will present recent work on a tailored branch-and-bound method for real-time motion planning and decision making on embedded processing units, and recent results for two applications related to automated driving and traffic control.
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  •  NEWS    Avishai Weiss to give an invited talk at the University of Kentucky
    Date: November 11, 2022
    MERL Contact: Avishai Weiss
    Research Areas: Control, Dynamical Systems, Optimization
    Brief
    • Avishai Weiss will give an invited talk at the William Maxwell Reed Seminar Series, Mechanical and Aerospace Engineering Department, University of Kentucky on "Fail-Safe Spacecraft Rendezvous." The talk will present some recent developments at MERL on guaranteeing safe rendezvous trajectories that avoid colliding with the target in the event of thruster anomalies.
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  •  NEWS    MERL Contributes to the 2022 American Modelica Conference
    Date: October 26, 2022 - October 28, 2022
    Where: American Modelica Conference 2022
    MERL Contacts: Scott A. Bortoff; Christopher R. Laughman
    Research Area: Multi-Physical Modeling
    Brief
    • MERL researchers provided some key contributions to the 2022 American Modelica Conference, held October 26-28 at the University of Texas, Dallas. Chris Laughman, Senior Team Leader, Multiphysical Systems, was the Executive Coordinator of the conference, and worked to plan and stage the event. Scott A. Bortoff, Chief Scientist, gave a keynote address entitled "Sustainable HVAC: Research Opportunities for Modelicans." The talk posed the question: What are the modeling and control research challenges that, if addressed, will drive meaningful innovation in sustainable building HVAC systems in the next 20 years? In addition, the paper "Performance Enhancements for Zero-Flow Simulation of Vapor Compression Cycles," by Principal Research Scientist Hongtao Qiao and Chris Laughman, was a finalist for the conference Best Paper Award.
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  •  TALK    [MERL Seminar Series 2022] Prof. Jiajun Wu presents talk titled Understanding the Visual World Through Naturally Supervised Code
    Date & Time: Tuesday, November 1, 2022; 1:00 PM
    Speaker: Jiajun Wu, Stanford University
    MERL Host: Anoop Cherian
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    Abstract
    • The visual world has its inherent structure: scenes are made of multiple identical objects; different objects may have the same color or material, with a regular layout; each object can be symmetric and have repetitive parts. How can we infer, represent, and use such structure from raw data, without hampering the expressiveness of neural networks? In this talk, I will demonstrate that such structure, or code, can be learned from natural supervision. Here, natural supervision can be from pixels, where neuro-symbolic methods automatically discover repetitive parts and objects for scene synthesis. It can also be from objects, where humans during fabrication introduce priors that can be leveraged by machines to infer regular intrinsics such as texture and material. When solving these problems, structured representations and neural nets play complementary roles: it is more data-efficient to learn with structured representations, and they generalize better to new scenarios with robustly captured high-level information; neural nets effectively extract complex, low-level features from cluttered and noisy visual data.
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  •  TALK    [MERL Seminar Series 2022] Prof. Ufuk Topcu presents talk titled Autonomous systems in the intersection of formal methods, learning, and control
    Date & Time: Wednesday, October 26, 2022; 1:00 PM
    Speaker: Ufuk Topcu, The University of Texas at Austin
    MERL Host: Abraham P. Vinod
    Research Areas: Control, Dynamical Systems, Optimization
    Abstract
    • Autonomous systems are emerging as a driving technology for countlessly many applications. Numerous disciplines tackle the challenges toward making these systems trustworthy, adaptable, user-friendly, and economical. On the other hand, the existing disciplinary boundaries delay and possibly even obstruct progress. I argue that the nonconventional problems that arise in designing and verifying autonomous systems require hybrid solutions in the intersection of learning, formal methods, and controls. I will present examples of such hybrid solutions in the context of learning in sequential decision-making processes. These results offer novel means for effectively integrating physics-based, contextual, or structural prior knowledge into data-driven learning algorithms. They improve data efficiency by several orders of magnitude and generalizability to environments and tasks that the system had not experienced previously.
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  •  NEWS    Invited talk at The Penn State Seminar Series on Systems, Control, and Robotics.
    Date: October 20, 2022
    Where: University Park, PA
    MERL Contact: Devesh K. Jha
    Research Areas: Artificial Intelligence, Control, Robotics
    Brief
    • Devesh Jha, a Principal Research Scientist in the Data Analytics Group at MERL, delivered an invited talk at The Penn State Seminar Series on Systems, Control and Robotics. This talk presented some of the recent work done at MERL in the areas of optimization and control for robotic manipulation in unstructured environment.
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  •  NEWS    Stefano Di Cairano to give a public lecture on status and challenges of automotive driving at IEEE CSS Day
    Date: October 24, 2022
    Where: Online, 10/24/2022 9:00am (Eastern time)
    MERL Contact: Stefano Di Cairano
    Research Areas: Control, Dynamical Systems, Optimization, Robotics
    Brief
    • Dr. Stefano Di Cairano (Senior Team Leader at MERL) has been invited to give a public talk at the first IEEE CSS Day event on the status, challenges, and role of control in autonomous driving.

      The talk, titled "The Long Voyage Towards Autonomous Driving, with Control Systems as the Co-Pilot", will review some history of autonomous driving, some of the open challenges that control technology may help address, and the next steps towards full-autonomy. The talk is designed for a non-technical audience, to explain the role and impact of control in automated driving technology.
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  •  NEWS    MERL Researcher Kyeong Jin Kim organizes the second international workshop in 2023 IEEE International Conference on Communications (ICC).
    Date: May 28, 2023 - June 1, 2023
    Where: Rome, Italy
    Research Areas: Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal Processing
    Brief
    • Kyeong Jin Kim, a Senior Principal Research Scientist in the Connectivity & Information Processing Team, organizes the second international workshop in 2023 IEEE International Conference on Communications (ICC). The workshop is titled, "Industrial Private 5G-and-beyond Wireless Networks," and aims to bring researchers for technical discussion on fundamental and practically relevant questions to many emerging challenges in industrial private wireless networks. This workshop is also being organized with the help of other researchers from industry and academia such as Huawei Technology, University of South Florida, Aalborg University, Jinan University, and South China University of Technology. IEEE ICC is one of two IEEE Communications Society's flagship conferences.
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  •  TALK    [MERL Seminar Series 2022] Prof. Gianmario Pellegrino presents talk titled Design, Identification and Simulation of PM Synchronous Machines for Traction
    Date & Time: Friday, October 14, 2022; 11:00 AM
    Speaker: Gianmario Pellegrino, Politecnico di Tornio, Italy
    Research Areas: Electric Systems, Electronic and Photonic Devices, Multi-Physical Modeling, Optimization
    Abstract
    • This seminar presents a comprehensive design and simulation procedure for Permanent Magnet Synchronous Machines (PMSMs) for traction application. The design of heavily saturated traction PMSMs is a multidisciplinary engineering challenge that CAD software suites struggle to grasp, whereas design equations are way too approximated for the purpose. This tutorial will present the design toolchain of SyR-e, where magnetic and structural design equations are fast-FEA corrected for an insightful initial design, later FEA calibrated with free or commercial FEA tools. One e-motor will be designed from zero referring to the specs and size of the Tesla Model 3 rear-axle e-motor. The circuital model of one motor with inverter and discrete-time control will be automatically generated, in Simulink and PLECS, with accessible torque control source code, for simulation of healthy and faulty conditions, ready for real-time implementation (e.g. HiL).
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  •  TALK    A Tunable Control/Learning Framework for Autonomous Systems
    Date & Time: Thursday, October 13, 2022; 1:30pm-2:30pm
    Speaker: Prof. Shaoshuai Mou, Purdue University
    MERL Host: Yebin Wang
    Research Areas: Control, Machine Learning, Optimization
    Abstract
    • Modern society has been relying more and more on engineering advance of autonomous systems, ranging from individual systems (such as a robotic arm for manufacturing, a self-driving car, or an autonomous vehicle for planetary exploration) to cooperative systems (such as a human-robot team, swarms of drones, etc). In this talk we will present our most recent progress in developing a fundamental framework for learning and control in autonomous systems. The framework comes from a differentiation of Pontryagin’s Maximum Principle and is able to provide a unified solution to three classes of learning/control tasks, i.e. adaptive autonomy, inverse optimization, and system identification. We will also present applications of this framework into human-autonomy teaming, especially in enabling an autonomous system to take guidance from human operators, which is usually sparse and vague.
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  •  NEWS    MERL Researcher Interviewed by Globest.com about "High Tech Airflow Control for Smarter Energy Use"
    Date: August 25, 2022
    MERL Contact: Anthony Vetro
    Research Areas: Dynamical Systems, Machine Learning, Multi-Physical Modeling
    Brief
    • MERL researcher Saleh Nabi was interviewed by Globest.com regarding the use of airflow optimization for smarter energy use and disease prevention. The article titled "High Tech Airflow Control for Smarter Energy Use: Reducing costs and improving effectiveness means a lot of tricky math" was recently published and describes how the solutions to complex fluid dynamical equations leads to improved HVAC control.

      Globest.com is a trusted and independent team of experts providing commercial real estate professionals with comprehensive coverage and best practices necessary to innovate and build their businesses. More details about Globest can be found here: https://www.globest.com/static/about-us/
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