- 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.
-
- 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.
-
- 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.
-
- 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.
-
- 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.
-
- 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.
-
- 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.
-
- 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.
-
- 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.
-
- 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.
-
- 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.
-
- 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.
-
- 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).
-
- 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.
-
- 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/
-
- Date: Thursday, October 6, 2022
Location: Kendall Square, Cambridge, MA
MERL Contacts: Anoop Cherian; Jonathan Le Roux
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
Brief - SANE 2022, a one-day event gathering researchers and students in speech and audio from the Northeast of the American continent, was held on Thursday October 6, 2022 in Kendall Square, Cambridge, MA.
It was the 9th edition in the SANE series of workshops, which started in 2012 and was held every year alternately in Boston and New York until 2019. Since the first edition, the audience has grown to a record 200 participants and 45 posters in 2019. After a 2-year hiatus due to the pandemic, SANE returned with an in-person gathering of 140 students and researchers.
SANE 2022 featured invited talks by seven leading researchers from the Northeast: Rupal Patel (Northeastern/VocaliD), Wei-Ning Hsu (Meta FAIR), Scott Wisdom (Google), Tara Sainath (Google), Shinji Watanabe (CMU), Anoop Cherian (MERL), and Chuang Gan (UMass Amherst/MIT-IBM Watson AI Lab). It also featured a lively poster session with 29 posters.
SANE 2022 was co-organized by Jonathan Le Roux (MERL), Arnab Ghoshal (Apple), John Hershey (Google), and Shinji Watanabe (CMU). SANE remained a free event thanks to generous sponsorship by Bose, Google, MERL, and Microsoft.
Slides and videos of the talks will be released on the SANE workshop website.
-
- Date: October 10, 2022 - October 11, 2022
Where: University of Freiburg, Germany
Research Areas: Control, Machine Learning, Optimization
Brief - Rien Quirynen is an invited speaker at an international workshop on Embedded Optimization and Learning for Robotics and Mechatronics, which is organized by the ELO-X project at the University of Freiburg in Germany. This talk, entitled "Embedded learning, optimization and predictive control for autonomous vehicles", presents recent results from multiple projects at MERL that leverage embedded optimization, machine learning and optimal control for autonomous vehicles.
This workshop is part of the ELO-X Fall School and Workshop. Invited external lecturers will present state-of-the-art techniques and applications in the field of Embedded Optimization and Learning. ELO-X is a Marie Curie Innovative Training Network (ITN) funded by the European Commission Horizon 2020 program.
-
- Date: September 21, 2022
MERL Contacts: Philip V. Orlik; Anthony Vetro
Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio
Brief - Mitsubishi Electric Research Laboratories (MERL) invites qualified postdoctoral candidates to apply for the position of Postdoctoral Research Fellow. This position provides early career scientists the opportunity to work at a unique, academically-oriented industrial research laboratory. Successful candidates will be expected to define and pursue their own original research agenda, explore connections to established laboratory initiatives, and publish high impact articles in leading venues. Please refer to our web page for further details.
-
- Date: September 15, 2022
MERL Contact: Yebin Wang
Research Areas: Control, Dynamical Systems, Robotics
Brief - Yebin Wang, a Senior Principal Research Scientist in MERL's Electric Machines and Devices, is serving as an Associate Editor for the IEEE International Conference on Robotics and Automation (ICRA) 2023.
As the flagship conference of the IEEE Robotics and Automation Society, ICRA will bring together the world's top researchers and most important companies to share ideas and advances in our field.
-
- Date: September 6, 2022
Where: IEEE Transactions on Energy Conversion
Research Area: Electric Systems
Brief - The Star Reviewers are recognized by the Editorial Board as the individuals who have consistently provided rigorous quality reviews in a timely fashion and on multiple occasions within a given year.
-
- Date & Time: Tuesday, September 6, 2022; 12:00 PM EDT
Speaker: Chuang Gan, UMass Amherst & MIT-IBM Watson AI Lab
MERL Host: Jonathan Le Roux
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
Abstract 
Human sensory perception of the physical world is rich and multimodal and can flexibly integrate input from all five sensory modalities -- vision, touch, smell, hearing, and taste. However, in AI, attention has primarily focused on visual perception. In this talk, I will introduce my efforts in connecting vision with sound, which will allow machine perception systems to see objects and infer physics from multi-sensory data. In the first part of my talk, I will introduce a. self-supervised approach that could learn to parse images and separate the sound sources by watching and listening to unlabeled videos without requiring additional manual supervision. In the second part of my talk, I will show we may further infer the underlying causal structure in 3D environments through visual and auditory observations. This enables agents to seek the sound source of repeating environmental sound (e.g., alarm) or identify what object has fallen, and where, from an intermittent impact sound.
-
- Date: August 25, 2022
Awarded to: Marcus Greiff
Research Areas: Control, Dynamical Systems, Robotics
Brief - Marcus Greiff, a Visiting Research Scientist at MERL, was awarded one of three outstanding student paper awards at the IEEE CCTA 2022 conference for his paper titled "Quadrotor Control on SU(2)xR3 with SLAM Integration". The award was given for originality, clarity, and potential impact on practical applications of control. The work presents a complete UAV control system design, facilitating autonomous supermarket inventorying without the need for external motion capture systems. A video of the experiments is on YouTube, including both simulations and real-time examples.
-
- Date: August 22, 2022
MERL Contacts: Chiori Hori; Jonathan Le Roux; Anthony Vetro
Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
Brief - IEEE has announced that the recipient of the 2023 IEEE James L. Flanagan Speech and Audio Processing Award will be Prof. Alex Waibel (CMU/Karlsruhe Institute of Technology), “For pioneering contributions to spoken language translation and supporting technologies.” Mitsubishi Electric Research Laboratories (MERL), which has become the new sponsor of this prestigious award in 2022, extends our warmest congratulations to Prof. Waibel.
MERL Senior Principal Research Scientist Dr. Chiori Hori, who worked with Dr. Waibel at Carnegie Mellon University and collaborated with him as part of national projects on speech summarization and translation, comments on his invaluable contributions to the field: “He has contributed not only to the invention of groundbreaking technology in speech and spoken language processing but also to the promotion of an abundance of research projects through international research consortiums by linking American, European, and Asian research communities. Many of his former laboratory members and collaborators are now leading R&D in the AI field.”
The IEEE Board of Directors established the IEEE James L. Flanagan Speech and Audio Processing Award in 2002 for outstanding contributions to the advancement of speech and/or audio signal processing. This award has recognized the contributions of some of the most renowned pioneers and leaders in their respective fields. MERL is proud to support the recognition of outstanding contributions to the field of speech and audio processing through its sponsorship of this award.
-
- Date: July 14, 2022
Awarded to: Weidong Cao, Mouhacine Benosman, Xuan Zhang, and Rui Ma
MERL Contact: Mouhacine Benosman
Research Area: Artificial Intelligence
Brief - The Conference committee of the 59th Design Automation Conference has chosen MERL's paper entitled 'Domain Knowledge-Infused Deep Learning for Automated Analog/RF Circuit Parameter Optimization', as a DAC Best Paper Award nominee. The committee evaluated both manuscript and submitted presentation recording, and has chosen MERL's paper as one of six nominees for this prestigious award. Decisions were based on the submissions’ innovation, impact and exposition.
-
- Date: June 15, 2022
Awarded to: Yuxiang Sun, Mouhacine Benosman, Rui Ma.
MERL Contact: Mouhacine Benosman
Research Area: Artificial Intelligence
Brief - The committee of the International Conference on Artificial Intelligence Circuits and Systems (AICAS) 2022, has selected MERL's paper entitled 'GaN Distributed RF Power Amplifier Automation Design with Deep Reinforcement Learning' as a winner of the AICAS 2022 Openedges Award.
In this paper MERL researchers propose a novel design automation methodology based on deep reinforcement learning (RL), for wide-band non-uniform distributed RF power amplifiers, known for their high dimensional design challenges.
-