- Date: December 10, 2024 - December 15, 2024
Where: Advances in Neural Processing Systems (NeurIPS)
MERL Contacts: Petros T. Boufounos; Matthew Brand; Ankush Chakrabarty; Anoop Cherian; François Germain; Toshiaki Koike-Akino; Christopher R. Laughman; Jonathan Le Roux; Jing Liu; Suhas Lohit; Tim K. Marks; Yoshiki Masuyama; Kieran Parsons; Kuan-Chuan Peng; Diego Romeres; Pu (Perry) Wang; Ye Wang; Gordon Wichern
Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Human-Computer Interaction, Information Security
Brief - MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.
1. "RETR: Multi-View Radar Detection Transformer for Indoor Perception" by Ryoma Yataka (Mitsubishi Electric), Adriano Cardace (Bologna University), Perry Wang (Mitsubishi Electric Research Laboratories), Petros Boufounos (Mitsubishi Electric Research Laboratories), Ryuhei Takahashi (Mitsubishi Electric). Main Conference. https://neurips.cc/virtual/2024/poster/95530
2. "Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads" by Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Joanna Matthiesen (Math Kangaroo USA), Kevin Smith (Massachusetts Institute of Technology), Josh Tenenbaum (Massachusetts Institute of Technology). Main Conference, Datasets and Benchmarks track. https://neurips.cc/virtual/2024/poster/97639
3. "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?" by Young-Jin Park (Massachusetts Institute of Technology), Jing Liu (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Gordon Wichern (Mitsubishi Electric Research Laboratories), Navid Azizan (Massachusetts Institute of Technology), Christopher R. Laughman (Mitsubishi Electric Research Laboratories), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories). Time Series in the Age of Large Models Workshop.
4. "Forget to Flourish: Leveraging Model-Unlearning on Pretrained Language Models for Privacy Leakage" by Md Rafi Ur Rashid (Penn State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Shagufta Mehnaz (Penn State University), Ye Wang (Mitsubishi Electric Research Laboratories). Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?
5. "Spatially-Aware Losses for Enhanced Neural Acoustic Fields" by Christopher Ick (New York University), Gordon Wichern (Mitsubishi Electric Research Laboratories), Yoshiki Masuyama (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Jonathan Le Roux (Mitsubishi Electric Research Laboratories). Audio Imagination Workshop.
6. "FV-NeRV: Neural Compression for Free Viewpoint Videos" by Sorachi Kato (Osaka University), Takuya Fujihashi (Osaka University), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Takashi Watanabe (Osaka University). Machine Learning and Compression Workshop.
7. "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via VLM" by Keshav Bimbraw (Worcester Polytechnic Institute), Ye Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop.
8. "Smoothed Embeddings for Robust Language Models" by Hase Ryo (Mitsubishi Electric), Md Rafi Ur Rashid (Penn State University), Ashley Lewis (Ohio State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kieran Parsons (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories). Safe Generative AI Workshop.
9. "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation" by Xiangyu Chen (University of Kansas), Ye Wang (Mitsubishi Electric Research Laboratories), Matthew Brand (Mitsubishi Electric Research Laboratories), Pu Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). Workshop on Adaptive Foundation Models.
10. "Preference-based Multi-Objective Bayesian Optimization with Gradients" by Joshua Hang Sai Ip (University of California Berkeley), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Ali Mesbah (University of California Berkeley), Diego Romeres (Mitsubishi Electric Research Laboratories). Workshop on Bayesian Decision-Making and Uncertainty. Lightning talk spotlight.
11. "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensions with Trust-Region-based Bayesian Novelty Search" by Wei-Ting Tang (Ohio State University), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Joel A. Paulson (Ohio State University). Workshop on Bayesian Decision-Making and Uncertainty.
12. "MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track" by Ye Wang (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Special Award for Practical Attack.
13. "MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track" by Jing Liu (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Won 3rd Place Award.
MERL members also contributed to the organization of the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips24/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce Research), Kevin Smith (Massachusetts Institute of Technology), Tim K. Marks (Mitsubishi Electric Research Laboratories), Juan Carlos Niebles (Salesforce AI Research), Petar Veličković (Google DeepMind).
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- Date: March 20, 2024
Where: Austin, TX
MERL Contact: Ankush Chakrabarty
Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization
Brief - Ankush Chakrabarty, Principal Research Scientist in the Multiphysical Systems Team, was invited to speak as a guest lecturer in the seminar series on "Occupant-Centric Grid Interactive Buildings" in the Department of Civil, Architectural and Environmental Engineering (CAEE) at the University of Texas at Austin.
The talk, entitled "Deep Generative Networks and Fine-Tuning for Net-Zero Energy Buildings" described lessons learned from MERL's recent research on generative models for building simulation and control, along with meta-learning for on-the-fly fine-tuning to adapt and optimize energy expenditure.
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- Date: November 14, 2023
Where: Istanbul, Turkey
MERL Contact: Ankush Chakrabarty
Research Areas: Control, Data Analytics, Machine Learning, Multi-Physical Modeling, Optimization
Brief - Ankush Chakrabarty, Principal Research Scientist in the Multiphysical Systems team at MERL, served as Co-Chair at the 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities (BALANCES'23). The workshop places spotlights on two different IEA EBC Annexes: the Annex 81 - Data-Driven Smart Buildings and Annex 82 - Energy Flexible Buildings Towards Resilient Low Carbon Energy Systems.
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- Date: August 30, 2023
Awarded to: Bingnan Wang, Hiroshi Inoue, and Makoto Kanemaru
MERL Contact: Bingnan Wang
Research Areas: Applied Physics, Data Analytics, Multi-Physical Modeling
Brief - MERL and Mitsubishi Electric's paper titled “Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches” was awarded one of three best paper awards at the 14th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED 2023). MERL Senior Principal Research Scientist Bingnan Wang presented the paper and received the award at the symposium. Co-authors of the paper include Mitsubishi Electric researchers Hiroshi Inoue and Makoto Kanemaru.
SDEMPED was established as the only international symposium entirely devoted to the diagnostics of electrical machines, power electronics and drives. It is now a regular biennial event. The 14th version, SDEMPED 2023 was held in Chania, Greece from August 28th to 31st, 2023.
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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: 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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- 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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- 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.
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- Date: July 5, 2022 - July 7, 2022
Research Areas: Control, Data Analytics, Dynamical Systems
Brief - The Benelux meeting is an annual conference gathering of the scientific community of Belgium, the Netherlands, and Luxemburg around systems and control. It is especially intended for PhD researchers and a number of activities are dedicated to them, including plenary talks and a mini-course.
Dr. Benosman has been invited to give the mini-course of the 2022 edition of the conference. This course, entitled 'A hybrid approach to control: classical control theory meets machine learning theory', will be centered around the topic of safe and robust machine learning-based control.
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- Date: November 17, 2021
Awarded to: Elevators and Escalators Division of Mitsubishi Electric US, Inc.
MERL Contacts: Daniel N. Nikovski; William S. Yerazunis
Research Areas: Data Analytics, Machine Learning, Signal Processing
Brief - The Elevators and Escalators Division of Mitsubishi Electric US, Inc. has been recognized as a 2022 CES® Innovation Awards honoree for its new PureRide™ Touchless Control for elevators, jointly developed with MERL. Sponsored by the Consumer Technology Association (CTA), the CES Innovation Awards is the largest and most influential technology event in the world. PureRide™ Touchless Control provides a simple, no-touch product that enables users to call an elevator and designate a destination floor by placing a hand or finger over a sensor. MERL initiated the development of PureRide™ in the first weeks of the COVID-19 pandemic by proposing the use of infra-red sensors for operating elevator call buttons, and participated actively in its rapid implementation and commercialization, resulting in a first customer installation in October of 2020.
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- Date: September 17, 2021 - October 31, 2021
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Optimization, Robotics
Brief - Diego Romeres, a Principal Research Scientist in MERL's Data Analytics group, is serving as an Associate Editor (AE) for the IEEE International Conference on Robotics and Automation (ICRA) 2022.
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- Date: March 14, 2021 - April 20, 2021
Where: IROS
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, Robotics
Brief - Diego Romeres, a Principal Research Scientist in MERL's Data Analytics group, is serving as an Associate Editor (AE) for the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021).
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- Date: February 4, 2021
Where: N/A
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, Machine Learning
Brief - Dr. Diego Romeres, Principal Research Scientist in the Data Analytics group, will serve on the Programme Committee for the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI), 2021.
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- Date: August 25, 2020
MERL Contact: Ankush Chakrabarty
Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
Brief - Ankush Chakrabarty co-organized an invited session on “Data-Driven Control For Industrial Applications” at the IEEE Conference on Control Technology and Applications with Shahin Shahrampour (Asst. Prof., Texas A&M). Talks covered topics including reinforcement learning for aerospace systems, constrained reinforcement learning for motors, deep Q learning for traffic systems and participants included speakers from Stanford University, North Carolina State University, Texas A&M, Oklahoma State University, University of Science and Technology at Beijing, and TU Delft.
MERL presented research (Chakrabarty, Danielson, Wang) on constraint-enforcing output-tracking with approximate dynamic programming for servomotor systems.
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- Date: July 12, 2020 - July 18, 2020
Where: Vienna, Austria (virtual this year)
MERL Contacts: Anoop Cherian; Devesh K. Jha; Daniel N. Nikovski
Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Dynamical Systems, Machine Learning, Optimization, Robotics
Brief - MERL researchers are presenting three papers at the International Conference on Machine Learning (ICML 2020), which is virtually held this year from 12-18th July. ICML is one of the top-tier conferences in machine learning with an acceptance rate of 22%. The MERL papers are:
1) "Finite-time convergence in Continuous-Time Optimization" by Orlando Romero and Mouhacine Benosman.
2) "Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?" by Kei Ota, Tomoaki Oiki, Devesh Jha, Toshisada Mariyama, and Daniel Nikovski.
3) "Representation Learning Using Adversarially-Contrastive Optimal Transport" by Anoop Cherian and Shuchin Aeron.
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- Date: June 18, 2020
Awarded to: Tong Huang, Hongbo Sun, K.J. Kim, Daniel Nikovski, Le Xie
MERL Contacts: Daniel N. Nikovski; Hongbo Sun
Research Areas: Data Analytics, Electric Systems, Optimization
Brief - A paper on A Holistic Framework for Parameter Coordination of Interconnected Microgrids Against Natural Disasters, written by Tong Huang, a former MERL intern from Texas A&M University, has been selected as one of the Best Conference Papers at the 2020 Power and Energy Society General Meeting (PES-GM). IEEE PES-GM is the flagship conference for the IEEE Power and Energy Society. The work was done in collaboration with Hongbo Sun, K. J. Kim, and Daniel Nikovski from MERL, and Tong's advisor, Prof. Le Xie from Texas A&M University.
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- Date: June 9, 2020
Where: ICRAxMIT
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, Dynamical Systems, Machine Learning, Robotics
Brief - Diego Romeres, a Principal Research Scientist in MERL's Data Analytics group, gave an invited talk at the workshop ICRAxMIT organized at MIT. The talk briefly described a derivative-free framework that doesn't take in consideration velocities and accelerations to model and control robotic systems. The proposed approach is validated in two real robotic systems.
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- Date: February 14, 2020
Where: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, Robotics
Brief - Diego Romeres, a Research Scientist in MERL's Data Analytics group, will be serving as an Associate Editor (AE) for the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020).
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- Date: February 10, 2020
Research Areas: Control, Data Analytics, Dynamical Systems
Brief - Dr. Benosman has been nominated as an associate editor at the IEEE Control Systems Letters (L-CSS).
The L-CSS publishes peer-reviewed brief articles that provide a rapid and concise account of innovative ideas regarding the theory, design, and applications of all aspects of control engineering.
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- Date: November 20, 2019
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, Machine Learning, Robotics
Brief - Diego Romeres, a Research Scientist in MERL's Data Analytics group, gave a seminar lecture at the Electrical and Computer Engineering Colloquium of the University of Connecticut. The talk described novel reinforcement algorithms based on combining physical models with non-parametric models of robotic systems derived from data.
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- Date: October 10, 2019
Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
MERL Contact: Devesh K. Jha
Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, Robotics
Brief - MERL researcher Devesh Jha has won the Rudolf Kalman Best Paper Award 2019 for the paper entitled "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models". This paper, published in a Special Commemorative Issue for Rudolf E. Kalman in the ASME JDSMC in March 2018, uses Bayesian filtering for imitation learning in Hidden Mode Hybrid Systems. This award is given annually by the Dynamic Systems and Control Division of ASME to the authors of the best paper published in the ASME Journal of Dynamic Systems Measurement and Control during the preceding year.
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- Date & Time: July 29, 2019; 10 AM
Where: US National Congress on Computational Mechanics 2019, in Austin Texas
Research Areas: Control, Data Analytics, Dynamical Systems
Brief - MERL researcher Mouhacine Benosman will present his work on 'Learning-based Robust Stabilization for Reduced-Order Models of 3D Boussinesq Equations' as a keynote speaker at the mini-symposium 'Data assimilation in Model Order Techniques for Computational Mechanics', during the next US National Congress on Computational Mechanics 2019, in Austin Texas.
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- Date: April 23, 2019
Awarded to: Teng-yok Lee
Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Machine Learning
Brief - MERL researcher Teng-yok Lee has won the Best Visualization Note Award at the PacificVis 2019 conference held in Bangkok Thailand, from April 23-26, 2019. The paper entitled "Space-Time Slicing: Visualizing Object Detector Performance in Driving Video Sequences" presents a visualization method called Space-Time Slicing to assist a human developer in the development of object detectors for driving applications without requiring labeled data. Space-Time Slicing reveals patterns in the detection data that can suggest the presence of false positives and false negatives.
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- Date: February 4, 2019
Research Areas: Control, Data Analytics, Dynamical Systems
Brief - Mouhacine Benosman is a guest editor of a special issue on Learning-based Adaptive Control: Theory and Application, recently published by the International Journal of Adaptive Control and Signal Processing. Other guest editors included Professor F.L. Lewis (University of Texas at Arlington Research Institute), Professor M. Guay (Queen's University), and Professor D. Owens (The University of Sheffield).
The special issue presents results of current research on learning-based adaptive methods, merging together model-based and data-driven machine learning approaches.
More information on the content of this special issue can be found at:
https://onlinelibrary.wiley.com/toc/10991115/2019/33/2.
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- Date: November 1, 2018
Research Areas: Control, Data Analytics, Dynamical Systems
Brief - Wiley has recently launched the Journal of Advanced Control for Applications: Engineering and Industrial Systems, which seeks original and high-quality contributions on the design of advanced control for applications. The aim is to stimulate the adoption of new and improved control design methods and provide a forum for the discussion of control application problems. Papers for the journal must include sufficient novelty in either the control design methods, the modelling and simulation techniques used, or the applications studied. MERL researcher, Mouhacine Benosman, has been invited to join the Editorial Board of this new journal.
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