- Date: July 11, 2023
Where: Daegu, Korea
MERL Contacts: Siddarth Jain; Devesh K. Jha; Arvind Raghunathan
Research Areas: Artificial Intelligence, Machine Learning, Robotics
Brief - MERL researchers presented 3 papers at the 19th edition of Robotics:Science and Systems Conference in Daegu, Korea. RSS is the flagship conference of the RSS foundation and is run as a single track conference presenting a limited number of high-quality papers. This year the main conference had a total of 112 papers presented. MERL researchers presented 2 papers in the main conference on planning and perception for dexterous manipulation. Another paper was presented in a workshop of learning for dexterous manipulation. More details can be found here https://roboticsconference.org.
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- Date: June 26, 2023
Where: International Conference on Smart Computing (SMARTCOMP), Vanderbilt University, Nashville, Tennessee
MERL Contact: Philip V. Orlik
Research Areas: Communications, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Signal Processing
Brief - VP & Research Director, Philip Orlik, gave a keynote titled, "Smart Technologies for Smarter Buildings" at the 9th edition of the IEEE International Conference on Smart Computing (SMARTCOMP) focusing on some of the research challenges and opportunities that arise as we seek to achieve net-zero emissions in Smart building environments.
SMARTCOMP is the premier conference on smart computing. Smart computing is a multidisciplinary domain based on the synergistic influence of advances in sensor-based technologies, Internet of Things, cyber-physical systems, edge computing, big data analytics, machine learning, cognitive computing, and artificial intelligence.
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- Date: June 9, 2023
Awarded to: Darius Petermann, Gordon Wichern, Aswin Subramanian, Jonathan Le Roux
MERL Contacts: Jonathan Le Roux; Gordon Wichern
Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
Brief - Former MERL intern Darius Petermann (Ph.D. Candidate at Indiana University) has received a Best Student Paper Award at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023) for the paper "Hyperbolic Audio Source Separation", co-authored with MERL researchers Gordon Wichern and Jonathan Le Roux, and former MERL researcher Aswin Subramanian. The paper presents work performed during Darius's internship at MERL in the summer 2022. The paper introduces a framework for audio source separation using embeddings on a hyperbolic manifold that compactly represent the hierarchical relationship between sound sources and time-frequency features. Additionally, the code associated with the paper is publicly available at https://github.com/merlresearch/hyper-unmix.
ICASSP is the flagship conference of the IEEE Signal Processing Society (SPS). ICASSP 2023 was held in the Greek island of Rhodes from June 04 to June 10, 2023, and it was the largest ICASSP in history, with more than 4000 participants, over 6128 submitted papers and 2709 accepted papers. Darius’s paper was first recognized as one of the Top 3% of all papers accepted at the conference, before receiving one of only 5 Best Student Paper Awards during the closing ceremony.
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- 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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- 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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- 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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- Date: June 8, 2023
MERL Contact: Toshiaki Koike-Akino
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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- Date: June 8, 2023
Where: Zoom
MERL Contact: Abraham P. Vinod
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Optimization, Robotics
Brief - Abraham Vinod gave an invited talk at the Electrical and Computer Engineering Department, the University of California Santa Cruz, titled "Motion Planning under Constraints and Uncertainty using Data and Reachability". His presentation covered recent work on fast and safe motion planners that can allow for coordination among agents, mitigate uncertainty arising from sensing limitations and simplified models, and tolerate the possibility of failures.
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- 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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- Date: June 2, 2023
Awarded to: Yuki Shirai, Devesh Jha, Arvind Raghunathan and Dennis Hong
MERL Contacts: Devesh K. Jha; Arvind Raghunathan; Yuki Shirai
Research Areas: Artificial Intelligence, Optimization, Robotics
Brief - MERL's paper titled: "Closed-Loop Tactile Controller for Tool Manipulation" Won the Best Poster Award in the workshop on "Embracing contacts : Making robots physically interact with our world". First author and MERL intern, Yuki Shirai, was presented with the award at a ceremony held at ICRA in London. MERL researchers Devesh Jha, Principal Research Scientist, and Arvind Raghunathan, Senior Principal Research Scientist and Senior Team Leader as well as Prof. Dennis Hong of University of California, Los Angeles are also coauthors.
The paper presents a technique to manipulate an object using a tool in a closed-loop fashion using vision-based tactile sensors. More information about the workshop and the various speakers can be found here https://sites.google.com/view/icra2023embracingcontacts/home.
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- Date: June 1, 2023
Where: San Diego, CA
MERL Contact: Abraham P. Vinod
Research Areas: Control, Optimization
Brief - The student networking event provides an opportunity for all interested students attending American Control Conference 2023 to receive career advice from professionals working in industry, academia, and national laboratories during a structured event. The event aims to provide an engaging experience to students that illustrates the benefits of involvement in the control community and encourage their continued participation as the future leaders in the field.
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- Date: May 31, 2023 - June 2, 2023
Where: San Diego, CA
MERL Contacts: 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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- Date: May 31, 2023 - June 3, 2023
Where: 2023 SIAM Conference on Optimization
MERL Contacts: Devesh K. Jha; Arvind Raghunathan
Research Areas: Control, Optimization, Robotics
Brief - Arvind Raghunathan, Senior Team Leader and Senior Principal Research Scientist in Optimization & Intelligent Robotics team, will organize two minisymposia at the 2023 SIAM Conference on Optimization to be held in Seattle from May 31 to June 3. The two minisymposia titled "Optimization in Control – Algorithms, Applications, and Software" and "New Algorithmic Techniques for Global Optimization" will feature twelve invited speakers from academia and national labs.
Additionally, Arvind together with Devesh Jha, Principal Research Scientist in Optimization & Intelligent Robotics Team, and collaborators will present five invited talks covering the topics of algorithms for convex programs, multilinear programs, mixed-integer nonlinear programs, and robotics.
See:
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=76268
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=76269
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=76270
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=76256
https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=75897
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- 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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- 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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- 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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- Date & Time: Wednesday, May 17, 2023; 1:00 PM
Speaker: Mark Ku, The University of Delaware
MERL Host: Chungwei Lin
Research Areas: Applied Physics, Computational Sensing
Abstract 
Quantum technology holds potential for revolutionizing how information is processed, transmitted, and acquired. While quantum computation and quantum communication have been among the well-known examples of quantum technology, it is increasingly recognized that quantum sensing is the application with the most potential for immediate wide-spread practical utilization. In this talk, I will provide an overview of the field of quantum sensing with nitrogen vacancy (NV) centers in diamond as a specific example. I will introduce the physical system of NV and describe some basic quantum sensing protocols. Then, I will present some state-of-the-art and examples where quantum sensors such as NV can accomplish what traditional sensors cannot. Lastly, I will discuss potential future directions in the area of NV quantum sensing.
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- Date: April 30, 2023
MERL Contact: Arvind Raghunathan
Research Area: Optimization
Brief - Arvind Raghunathan, Senior Team Leader and Senior Principal Research Scientist with Optimization and Intelligent Robotics team, will serve as the Chair of The 2022 Howard Rosenbrock Prize Committee. Every year, Optimization and Engineering (OPTE) journal honors excellence in scientific research by presenting the Rosenbrock Prize to the best paper published in the previous year. The prize recognizes outstanding research contributions that demonstrate Howard Rosenbrock’s own dedication to bridging the gap between optimization and engineering.
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- 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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- 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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- Date: April 1, 2023
MERL Contact: Anthony Vetro Brief - Anthony Vetro has been appointed President and Chief Executive Officer (CEO) of Mitsubishi Electric Research Laboratories (MERL), the research and development arm for Mitsubishi Electric Corporation in the U.S. In his role, Vetro will oversee all aspects of the lab’s operations; manage and direct its long-term goals, growth and return on investment; and collaborate closely with Mitsubishi Electric’s research counterparts in Japan on advancing the company’s global smart society and sustainability initiatives.
Vetro previously served as Vice President & Director of MERL. In this role, he contributed to the strategic direction of the company where he established new research programs and led teams in a variety of emerging technology areas. He succeeds Richard C. Waters, a founding MERL member, who was CEO for 24 years, and will continue as chairperson.
“Anthony has played a significant role in shaping MERL’s evolution as a premier research lab that drives innovation across all of our U.S. business units,” said Mike Corbo, chief representative for the America’s region. “His leadership will further advance our long-term vision to integrate our cross-industry products, services and technologies, and enable the interconnection of people-centric technology and systems that will help realize a truly sustainable smart society.”
Vetro began his MERL career in 1996, where he conducted research in the area of multimedia signal processing, with a focus on video compression. His research contributed to the transfer and development of several technologies that were integrated into Mitsubishi Electric products, including digital television receivers and displays, surveillance and camera monitoring systems, automotive equipment, as well as satellite imaging systems. He holds a Ph.D. in electrical engineering from New York University – Tandon School of Engineering and is an IEEE Fellow.
“MERL celebrated its 30th Anniversary last year, where we recognized our many innovations and the people that made them possible,” said Vetro. “I’m looking forward to working even more closely with our teams to further strengthen relationships among our business units and serve as technological bridge for product innovation throughout the next 30 years and beyond.”
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- 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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- Date: March 27, 2023
Awarded to: Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik, Hiroshi Mineno
MERL Contacts: Jianlin Guo; Philip V. Orlik; Kieran Parsons
Research Areas: Communications, Signal Processing
Brief - MELCO/MERL research paper “IEEE 802.19.3 Standardization for Coexistence of IEEE 802.11ah and IEEE 802.15.4g Systems in Sub-1GHz Frequency Bands” has won the Best Paper Award of the 2022 IPSJ Transactions on Consumer Devices and Systems. The Information Processing Society of Japan (IPSJ) award was established in 1970 and is conferred on the authors of particularly excellent papers, which are published in the IPSJ journals and transactions. Our paper was published by the IPSJ Transaction on Consumer Device and System Vol. 29 in 2021 and authors are Yukimasa Nagai, Takenori Sumi, Jianlin Guo, Philip Orlik and Hiroshi Mineno.
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- 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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- 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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