Machine Learning

Data-driven approaches to design intelligent algorithms.

MERL has a long history of research activity in machine learning, including the development of various boosting algorithms and contributing to the theory and practice of highly scalable collaborative filtering. Our recent work has focused on deep learning and reinforcement learning, with application to a wide range of applications including automotive, robotics, factory automation, transportation, as well as building and home systems.

Quick Links
Researchers
Awards
- AWARD Mitsubishi Electric Team Wins Awards at GalFer Contest
  Date: June 23, 2025
  Awarded to: Bingnan Wang, Tatsuya Yamamoto, Yusuke Sakamoto, Siyuan Sun, Toshiaki Koike-Akino, and Ye Wang
  MERL Contacts: Toshiaki Koike-Akino; Bingnan Wang; Ye Wang
  Research Areas: Machine Learning, Multi-Physical Modeling, Optimization
  Brief
  - The MELSUR (Mitsubishi Electric SURrogate) team, consisting of a group of MERL and Mitsubishi Electric researchers, ranked first in two out of three categories in the GalFer Contest.
    
    The GalFer (Galileo Ferraris) contest aims to compare the accuracy and efficiency of data-driven methodologies for the multi-physics simulation of traction electric machines. A total of 26 teams worldwide participated in the contest, which consists of three categories. The MELSUR team, including MERL staff Bingnan Wang, Toshiaki Koike-Akino, Ye Wang, MERL intern Siyuan Sun, Mitsubishi Electric researchers Tatsuya Yamamoto and Yusuke Sakamoto, ranked first for the category of "Novelty" and "Interpolation". The results were announced during an award ceremony at the COMPUMAG 2025 conference in Naples, Italy.
- AWARD MERL work receives IEEE Transactions on Automation Science and Engineering Best New Application Paper Award from IEEE Robotics and Automation Society
  Date: May 19, 2025
  Awarded to: Yehan Ma, Yebin Wang, Stefano Di Cairano, Toshiaki Koike-Akino, Jianlin Guo, Philip Orlik, Xinping Guan and Chenyang Lu
  MERL Contacts: Stefano Di Cairano; Jianlin Guo; Toshiaki Koike-Akino; Philip V. Orlik; Yebin Wang
  Research Areas: Communications, Control, Machine Learning
  Brief
  - The paper “Smart Actuation for End-Edge Industrial Control Systems”, co-authored by MERL intern Yehan Ma, MERL researchers Yebin Wang, Stefano Di Cairano, Toshiaki Koike-Akino, Jianlin Guo, and Philip Orlik, and academic collaborators Xinping Guan and Chenyang Lu, was recognized as the Best New Application Paper of the IEEE Transactions on Automation Science and Engineering (T-ASE), for "a new industrial automation solution that ensures safety operation through coordinated co-design of edge model predictive control and local actuation".
    
    The award recognizes the best application paper published in T-ASE over the previous calendar year, for the significance of new applications, technical merit, originality, potential impact on the field, and clarity of presentation.
- AWARD MERL Wins Awards at NeurIPS LLM Privacy Challenge
  Date: December 15, 2024
  Awarded to: Jing Liu, Ye Wang, Toshiaki Koike-Akino, Tsunato Nakai, Kento Oonishi, Takuya Higashi
  MERL Contacts: Toshiaki Koike-Akino; Jing Liu; Ye Wang
  Research Areas: Artificial Intelligence, Machine Learning, Information Security
  Brief
  - The Mitsubishi Electric Privacy Enhancing Technologies (MEL-PETs) team, consisting of a collaboration of MERL and Mitsubishi Electric researchers, won awards at the NeurIPS 2024 Large Language Model (LLM) Privacy Challenge. In the Blue Team track of the challenge, we won the 3rd Place Award, and in the Red Team track, we won the Special Award for Practical Attack.
See All Awards for Machine Learning
News & Events
- NEWS Diego Romeres Delivers Invited Talks at Fraunhofer Italia and the University of Padua
  Date: July 16, 2025 - July 18, 2025
  MERL Contact: Diego Romeres
  Research Areas: Artificial Intelligence, Control, Machine Learning, Optimization, Robotics, Human-Computer Interaction
  Brief
  - MERL researcher Diego Romeres was invited to present MERL's latest research at two institutions in Italy this July, focusing on human-robot collaboration and LLM-driven assembly systems.
    
    On July 16th, Dr. Romeres delivered a talk titled “Human-Robot Collaborative Assembly” at Fraunhofer Italia – Innovation Engineering Center (EIC) in Bolzano. His presentation showcased research on human-robot collaboration for efficient and flexible assembly processes. Fraunhofer Italia EIC is a non-profit research institute focused on enabling digital and sustainable transformation through applied innovation in close collaboration with both public and private sectors.
    
    Two days later, on July 18th, Dr. Romeres was hosted by the University of Padua, one of Europe’s oldest and most renowned universities. His invited lecture, “Robot Assembly through Human Collaboration & Large Language Models”, explored how artificial intelligence can enhance human-robot synergy in complex assembly tasks.
- NEWS MERL researchers present 13 papers at ACC 2025
  Date: July 8, 2025 - July 10, 2025
  Where: Denver, USA
  MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Purnanand Elango; Jordan Leung; Saviz Mowlavi; Diego Romeres; Abraham P. Vinod; Yebin Wang; Avishai Weiss
  Research Areas: Control, Dynamical Systems, Electric Systems, Machine Learning, Multi-Physical Modeling, Robotics
  Brief
  - MERL researchers presented 13 papers at the recently concluded American Control Conference (ACC) 2025 in Denver, USA. The papers covered a wide range of topics including Bayesian optimization for personalized medicine, machine learning for battery performance in eVTOLs, model predictive control for space and building systems, process systems engineering for sustainability, GNSS-RTK optimization, convex set manipulation, PDE control, servo system modeling, battery fault diagnosis, truck fleet coordination, interactive motion planning, and satellite station keeping. Additionally, MERL researchers (Vedang Deshpande and Ankush Chakrabarty) organized an invited session on design and optimization of energy systems.
    
    As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
See All News & Events for Machine Learning
Research Highlights
Internships
- EA0076: Internship - Machine Learning for Electric Motor Design
  
  MERL is seeking a motivated and qualified intern to conduct research on machine learning based electric motor design and optimization. Ideal candidates should be Ph.D. students with a solid background and publication record in electric machine design, optimization, and machine learning. Hands-on experience with the implementation of optimization algorithms, machine learning and deep learning methods is required. Strong programming skills using Python/PyTorch are expected. Knowledge and experience with electric machine principle, design and finite-element analysis are highly desirable. Start date for this internship is flexible and the duration is about 3 months.
- ST0096: Internship - Multimodal Tracking and Imaging
  
  MERL is seeking a motivated intern to assist in developing hardware and algorithms for multimodal imaging applications. The project involves integration of radar, camera, and depth sensors in a variety of sensing scenarios. The ideal candidate should have experience with FMCW radar and/or depth sensing, and be fluent in Python and scripting methods. Familiarity with optical tracking of humans and experience with hardware prototyping is desired. Good knowledge of computational imaging and/or radar imaging methods is a plus.
  Required Specific Experience
  - Experience with Python and Python Deep Learning Frameworks.
  - Experience with FMCW radar and/or Depth Sensors.
- CI0169: Internship - Robotic AI Agent
  
  Those who are passionate about pushing the boundaries of embodied AI, join our cutting-edge research team as an intern and contribute to the development of generalist AI agents for humanoid robots. This is a unique opportunity to work on impactful projects aimed at publishing in top-tier AI and robotics venues.
  What We’re Looking For
  We’re seeking highly motivated individuals with:
  - Advanced research experience in robotic AI, edge AI, and agentic AI systems.
  - Hands-on expertise in Large Language Models (LLMs), Vision-Language-Action (VLA) models and Foundation Models
  - Strong proficiency with Python, PyTorch, deep learning, and robotic agent frameworks
  Internship Details
  - Duration: ~3 months
  - Start Date: Flexible
  - Goal: Publish research at leading AI/robotics conferences and journals
  If you're excited about shaping the future of humanoid robotics and AI agents, we’d love to hear from you!
See All Internships for Machine Learning
Openings
- CA0093: Research Scientist - Control for Autonomous Systems
See All Openings at MERL
Recent Publications
- Yang, C.-A., Peng, K.-C., Yeh, R., "Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts", IEEE International Conference on Computer Vision (ICCV), October 2025.
  BibTeX TR2025-124 PDF Data Presentation
  - @inproceedings{Yang2025oct,
  - author = {{{Yang, Chiao-An and Peng, Kuan-Chuan and Yeh, Raymond}}},
  - title = {{{Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts}}},
  - booktitle = {IEEE International Conference on Computer Vision (ICCV)},
  - year = 2025,
  - month = oct,
  - url = {https://www.merl.com/publications/TR2025-124}
  - }
- Liu, J., Koike-Akino, T., Wang, P., Brand, M., Parsons, K., Wang, Y., "LoDA: Low-Dimensional Adaptation of Large Language Models" in Springer Book, September 2025.
  BibTeX TR2025-130 PDF
  - @incollection{Liu2025sep,
  - author = {Liu, Jing and Koike-Akino, Toshiaki and Wang, Pu and Brand, Matthew and Parsons, Kieran and Wang, Ye},
  - title = {{LoDA: Low-Dimensional Adaptation of Large Language Models}},
  - booktitle = {Springer Book},
  - year = 2025,
  - month = sep,
  - url = {https://www.merl.com/publications/TR2025-130}
  - }
- Ji, D.-Y., Wang, B., Inoue, H., Kanemaru, M., "Simulation-to-Reality Domain Adaptation for Motor Fault Detection", IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), August 2025.
  BibTeX TR2025-126 PDF
  - @inproceedings{Ji2025aug,
  - author = {Ji, Dai-Yan and Wang, Bingnan and Inoue, Hiroshi and Kanemaru, Makoto},
  - title = {{Simulation-to-Reality Domain Adaptation for Motor Fault Detection}},
  - booktitle = {IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)},
  - year = 2025,
  - month = aug,
  - url = {https://www.merl.com/publications/TR2025-126}
  - }
- Yataka, R., Wang, P., Boufounos, P.T., Takahashi, R., "End-to-End Radar Human Segmentation with Differentiable Positional Encoding", European Signal Processing Conference (EUSIPCO), August 2025.
  BibTeX TR2025-125 PDF
  - @inproceedings{Yataka2025aug,
  - author = {Yataka, Ryoma and Wang, Pu and Boufounos, Petros T. and Takahashi, Ryuhei},
  - title = {{End-to-End Radar Human Segmentation with Differentiable Positional Encoding}},
  - booktitle = {European Signal Processing Conference (EUSIPCO)},
  - year = 2025,
  - month = aug,
  - url = {https://www.merl.com/publications/TR2025-125}
  - }
- Hussein, A., Khurana, S., Wichern, G., Germain, F.G., Le Roux, J., "HASRD: Hierarchical Acoustic and Semantic Representation Disentanglement", Interspeech, August 2025.
  BibTeX TR2025-122 PDF
  - @inproceedings{Hussein2025aug,
  - author = {Hussein, Amir and Khurana, Sameer and Wichern, Gordon and Germain, François G and {Le Roux}, Jonathan},
  - title = {{HASRD: Hierarchical Acoustic and Semantic Representation Disentanglement}},
  - booktitle = {Interspeech},
  - year = 2025,
  - month = aug,
  - url = {https://www.merl.com/publications/TR2025-122}
  - }
- Ick, C., Wichern, G., Masuyama, Y., Germain, F.G., Le Roux, J., "Direction-Aware Neural Acoustic Fields for Few-Shot Interpolation of Ambisonic Impulse Responses", Interspeech, DOI: 10.21437/Interspeech.2025-1912, August 2025, pp. 933-937.
  BibTeX TR2025-120 PDF
  - @inproceedings{Ick2025aug,
  - author = {Ick, Christopher and Wichern, Gordon and Masuyama, Yoshiki and Germain, François G and {Le Roux}, Jonathan},
  - title = {{Direction-Aware Neural Acoustic Fields for Few-Shot Interpolation of Ambisonic Impulse Responses}},
  - booktitle = {Interspeech},
  - year = 2025,
  - pages = {933--937},
  - month = aug,
  - doi = {10.21437/Interspeech.2025-1912},
  - url = {https://www.merl.com/publications/TR2025-120}
  - }
- Khurana, S., Klement, D., Laurent, A., Bobos, D., Novosad, J., Gazdik, P., Zhang, E., Huang, Z., Hussein, A., Marxer, R., Masuyama, Y., Aihara, R., Hori, C., Germain, F.G., Wichern, G., Le Roux, J., "Factorized RVQ-GAN For Disentangled Speech Tokenization", Interspeech, August 2025.
  BibTeX TR2025-123 PDF
  - @inproceedings{Khurana2025aug,
  - author = {Khurana, Sameer and Klement, Dominik and Laurent, Antoine and Bobos, Dominik and Novosad, Juraj and Gazdik, Peter and Zhang, Ellen and Huang, Zilli and Hussein, Amir and Marxer, Ricard and Masuyama, Yoshiki and Aihara, Ryo and Hori, Chiori and Germain, François G and Wichern, Gordon and {Le Roux}, Jonathan},
  - title = {{Factorized RVQ-GAN For Disentangled Speech Tokenization}},
  - booktitle = {Interspeech},
  - year = 2025,
  - month = aug,
  - url = {https://www.merl.com/publications/TR2025-123}
  - }
- Yang, H., Wichern, G., Aihara, R., Masuyama, Y., Khurana, S., Germain, F.G., Le Roux, J., "Investigating Continuous Autoregressive Generative Speech Enhancement", Interspeech, August 2025.
  BibTeX TR2025-119 PDF
  - @inproceedings{Yang2025aug,
  - author = {Yang, Haici and Wichern, Gordon and Aihara, Ryo and Masuyama, Yoshiki and Khurana, Sameer and Germain, François G and {Le Roux}, Jonathan},
  - title = {{Investigating Continuous Autoregressive Generative Speech Enhancement}},
  - booktitle = {Interspeech},
  - year = 2025,
  - month = aug,
  - url = {https://www.merl.com/publications/TR2025-119}
  - }
See All Publications for Machine Learning
Videos

[CVPR 2025] TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection

FDPP: Fine-tune Diffusion Policy with Human Preference

[MERL Seminar Series Spring 2025] Red Teaming AI Agents in-the-wild: Revealing Deployment Vulnerabilities

[MERL Seminar Series Spring 2025] The Emergence of Generalizability and Semantic Low-Dim Subspaces in Diffusion Models

[MERL Seminar Series Spring 2025] Amplifying human performance in combinatorial competitive programming

[WACV 2025] Towards Zero-shot 3D Anomaly Localization

Data-driven Spatial Classification using Multi-Arm Bandits for Monitoring with Robot Teams

[NeurIPS 2024] MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track

[NeurIPS 2024] MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track

[NeurIPS 2024] Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads

[MERL Seminar Series Fall 2024] Audio for Object and Spatial Awareness

[IROS 2024] Few-shot Transparent Instance Segmentation for Bin Picking

[MERL Seminar Series Fall 2024] Tools from cognitive science to understand the behavior of large language models

[ECCV 2024] PS-NEUS: A Probability-guided Sampler for Neural Implicit Surface Rendering

[ECCV 2024] Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection

[CVPR 2024] SIRA: Scalable Inter-frame Relation and Association for Radar Perception

Decentralized, Safe, Multi-agent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning

[MERL Seminar Series Spring 2024] Neural Certificates and LLMs in Large-Scale Autonomy Design

[MERL Seminar Series Spring 2024] Decoding Hidden Worlds: Unprecedented Sensing and Connectivity for Climate, Robotics, & Smart Environments

Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling

[MERL Seminar Series Spring 2024] Are Emergent Abilities of Large Language Models a Mirage?

MERL's Quantum AI Technology

[GLOBECOM 2022/VCC 2023 Tutorial] Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications (Session 2)

[GLOBECOM 2022/VCC 2023 Tutorial] Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications (Session I)

[MERL Seminar Series Fall 2023] Robust and Physics-informed machine learning for low light imaging

[MERL Seminar Series Fall 2023] Multiplicity in Machine Learning

Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection

[MERL Seminar Series Fall 2023] Visual Programming - A compositional approach to building General Purpose Vision Systems

[MERL Seminar Series Fall 2023] The Confluence of Vision, Language, and Robotics

[MERL Seminar Series Fall 2023] A Process Systems Engineering Perspective on Carbon Capture: Key Challenges and Opportunities

[TRO 2024] Safe Multiagent Motion Planning Under Uncertainty for Drones Using Filtered Reinforcement Learning

Are Deep Neural Networks SMARTer than Second Graders?

[CVPR 2023] EVAL: Explainable Video Anomaly Localization

[MERL Seminar Series Spring 2023] Learning and Dynamical Systems

[MERL Seminar Series Spring 2023] Investigating Multi-Agent Reinforcement Learning for Grid-Interactive Smart Communities using CityLearn

[MERL Seminar Series Spring 2023] Pitfalls and Opportunities in Interpretable Machine Learning

[MERL Seminar Series Spring 2023] Neural Implicit Flow

[MERL Seminar Series Spring 2023] Towards Complex Language in Partially Observed Environments

[MERL Seminar Series Spring 2022] Hybrid robotics and implicit learning

Toshiaki Koike-Akino Gives Seminar Talk at IEEE Boston Photonics

[MERL Seminar Series Spring 2022] RLMPC: An Ideal Combination of Formal Optimal Control and Reinforcement Learning?

[MERL Seminar Series Spring 2022] Self-Supervised Scene Representation Learning

[MERL Seminar Series Spring 2022] Learning Speech Representations with Multimodal Self-Supervision

[MERL Seminar Series Spring 2022] Extreme optics design as a large-scale optimization problem

HealthCam: A system for non-contact monitoring of vital signs

[MERL Seminar Series 2021] Harnessing machine learning to build better Earth system models for climate projection

[MERL Seminar Series 2021] Deep probabilistic regression

[MERL Seminar Series 2021] Learning to See by Moving: Self-supervising 3D scene representations for perception, control, and visual reasoning

Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models

Application of Deep Learning for Nanophotonic Device Design (Invited)

Towards Human-Level Learning of Complex Physical Puzzles

Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry

Scene-Aware Interaction Technology

Action Detection Using A Deep Recurrent Neural Network

Semantic Scene Labeling

Obstacle Detection

MERL Research on Autonomous Vehicles

Deep Hierarchical Parsing for Semantic Segmentation

Global Local Face Upsampling Network
Software & Data Downloads

Software & Data Downloads

MERL is making Machine Learning software and data available to the research community:

Subject- and Dataset-Aware Neural Field for HRTF Modeling (SuDaField)
Open Vocabulary Attribute Detection Dataset (OVAD)
Task-Aware Unified Source Separation (TUSS)
Local Density-Based Anomaly Score Normalization for Domain Generalization (anomaly-score-normalization)
Long-Tailed Online Anomaly Detection dataset (LTOAD)
Group Representation Networks (G-RepsNets)

See All Downloads