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
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Researchers
Toshiaki
Koike-Akino
Ye
Wang
Jonathan
Le Roux
Ankush
Chakrabarty
Gordon
Wichern
Anoop
Cherian
Michael J.
Jones
Tim K.
Marks
Kieran
Parsons
Pu
(Perry)
WangStefano
Di Cairano
Philip V.
Orlik
Christopher R.
Laughman
Daniel N.
Nikovski
Devesh K.
Jha
Diego
Romeres
Chiori
Hori
Jing
Liu
Bingnan
Wang
Suhas
Lohit
Yebin
Wang
Matthew
Brand
Hassan
Mansour
Petros T.
Boufounos
Kuan-Chuan
Peng
Moitreya
Chatterjee
Abraham P.
Vinod
Arvind
Raghunathan
Vedang M.
Deshpande
Jianlin
Guo
Yoshiki
Masuyama
Siddarth
Jain
Scott A.
Bortoff
Pedro
Miraldo
Saviz
Mowlavi
Hongtao
Qiao
William S.
Yerazunis
Radu
Corcodel
Chungwei
Lin
Dehong
Liu
Joshua
Rapp
Hongbo
Sun
Wataru
Tsujita
Wael H.
Ali
Yanting
Ma
Anthony
Vetro
Jinyun
Zhang
Purnanand
Elango
Abraham
Goldsmith
Alexander
Schperberg
Avishai
Weiss
Kenji
Inomata
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Awards
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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, OptimizationBrief- 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.
- 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.
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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 LearningBrief- 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.
- 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".
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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 SecurityBrief- 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 -
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News & Events
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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 InteractionBrief- 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.
- 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.
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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, RoboticsBrief- 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.
- 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.
See All News & Events for Machine Learning -
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Research Highlights
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PS-NeuS: A Probability-guided Sampler for Neural Implicit Surface Rendering -
SAC-GNC: SAmple Consensus for adaptive Graduated Non-Convexity -
Quantum AI Technology -
TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models -
Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-Aware Spatio-Temporal Sampling -
Steered Diffusion -
Sustainable AI -
Edge-Assisted Internet of Vehicles for Smart Mobility -
Robust Machine Learning -
mmWave Beam-SNR Fingerprinting (mmBSF) -
Video Anomaly Detection -
Biosignal Processing for Human-Machine Interaction -
MERL Shopping Dataset -
Task-aware Unified Source Separation - Audio Examples
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Internships
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OR0164: Internship - Robotic 6D grasp pose estimation
MERL is looking for a highly motivated and qualified intern to work on methods for task-oriented 6-dof grasp pose detection using vision and tactile sensing. The objective is to enable a robot to identify multiple 6-DoF grasp poses tailored to specific tasks, allowing it to effectively grasp and manipulate objects. The ideal candidate would be a Ph.D. student familiar with the state-of-the-art methods for robotic grasping, object tracking, and imitation learning. This role involves developing, fine-tuning and deploying models on hardware. The successful candidate will work closely with MERL researchers to develop and implement novel algorithms, conduct experiments, and publish research findings at a top-tier conference. Start date and expected duration of the internship is flexible. Interested candidates are encouraged to apply with their updated CV and list of relevant publications.
Required Specific Experience
- Prior experience in robotic grasping
- Experience in Machine Learning
- Excellent programing skills
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OR0171: Internship - Foundation Models for Robotic Manipulation
MERL is seeking a highly motivated and qualified intern to conduct research on applying foundation models to robotic manipulation. The focus will be on leveraging large-scale pretrained models (e.g., vision-language models, multimodal transformers, diffusion policies) to enable generalist manipulation capabilities across diverse objects, tasks and embodiments including humanoids. Potential research topics include few-shot policy learning, multimodal grounding of multiple sensor modalities to robot actions, and adapting foundation models for precise control and high success rate.
The ideal candidate will be a senior Ph.D. student with a strong background in machine learning for robotics, particularly in areas such as foundation models, imitation learning, reinforcement learning, and multimodal perception. Knowledge on large-scale Vision-Language-Action (VLA) and multimodal foundation models is expected. The internship will involve algorithm design, model fine-tuning, simulation experiments, and deployment on physical robot platforms equipped with cameras, tactile sensors, and force/torque sensors. The successful candidate will collaborate closely with MERL researchers, with the expectation of publishing in top-tier robotics or AI conferences/journals. Interested candidates should apply with an updated CV and relevant publications.
Required Specific Experience
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Strong background in machine learning for robotics, particularly foundation models (e.g., pi_0, OpenVLA, RT-X, etc.) and imitation learning.
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Experience with simulation environments such as Mujoco, Isaac Gym, or RLBench.
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Experience with physical robot platforms and sensors (vision, tactile, force/torque).
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Proficiency in Python, PyTorch, and modern deep learning frameworks
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Strong publication record in robotics, machine learning, or AI venues
Internship Details
- Duration: ~3 months
- Start Date: Fall 2025 (flexible based on mutual agreement)
- Goal: Publish research at leading robotics/AI conferences and journals
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MS0156: Internship - Stochastic Model Predictive Control with Generative Models for Smart Building Control
MERL is looking for a research intern to develop efficient transformer-informed stochastic MPC to control net-zero energy buildings. This is an exciting opportunity to make a real impact in the field of cutting-edge deep learning and predictive control on a real system. Publication of results produced during the internship is desired. The expected duration of the internship is 3-6 months with a flexible start date.
The Ideal Candidate Will Have:
- Significant hands-on experience with stochastic MPC
- Publications in SMPC are a strong plus
- Fluency in Python and PyTorch
- Understanding of probabilistic time-series prediction
- Experience with convex programming
- Convex formulations of MPC/SMPC problems are a strong plus
- Completed their MS, or >50% of their PhD program
Mitsubishi Electric Research Labs, Inc. "MERL" provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.
MERL expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of MERL’s employees to perform their job duties may result in discipline up to and including discharge.
Working at MERL requires full authorization to work in the U.S and access to technology, software and other information that is subject to governmental access control restrictions, due to export controls. Employment is conditioned on continued full authorization to work in the U.S and the availability of government authorization for the release of these items, which might include without limitation, obtaining an export license or other documentation. MERL may delay commencement of employment, rescind an offer of employment, terminate employment, and/or modify job responsibilities, compensation, benefits, and/or access to MERL facilities and information systems, as MERL deems appropriate, to ensure practical compliance with applicable employment law and government access control restrictions.
- Significant hands-on experience with stochastic MPC
See All Internships for Machine Learning -
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Openings
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CI0177: Postdoctoral Research Fellow - Agentic AI
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CA0093: Research Scientist - Control for Autonomous Systems
See All Openings at MERL -
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Recent Publications
- "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}
- }
, - "AI-Driven Scenario Discovery: Diffusion Models and Multi-Armed Bandits for Building Control Validation", Energy and Buildings, September 2025.BibTeX TR2025-132 PDF
- @article{Tang2025sep,
- author = {Tang, Wei-Ting and Vinod, Abraham P. and Germain, François G and Paulson, Joel A. and Laughman, Christopher R. and Chakrabarty, Ankush},
- title = {{AI-Driven Scenario Discovery: Diffusion Models and Multi-Armed Bandits for Building Control Validation}},
- journal = {Energy and Buildings},
- year = 2025,
- month = sep,
- url = {https://www.merl.com/publications/TR2025-132}
- }
, - "Zero-Shot Parameter Estimation of Modelica Models using Patch Transformer Networks", International Modelica and FMI Conference, September 2025.BibTeX TR2025-133 PDF
- @inproceedings{Chakrabarty2025sep,
- author = {Chakrabarty, Ankush and Forgione, Marco and Piga, Dario and Bemporad, Alberto and Laughman, Christopher R.},
- title = {{Zero-Shot Parameter Estimation of Modelica Models using Patch Transformer Networks}},
- booktitle = {International Modelica and FMI Conference},
- year = 2025,
- month = sep,
- url = {https://www.merl.com/publications/TR2025-133}
- }
, - "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}
- }
, - "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}
- }
, - "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}
- }
, - "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}
- }
, - "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}
- }
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- "Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts", IEEE International Conference on Computer Vision (ICCV), October 2025.
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Videos
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Software & Data Downloads
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Generalization in Deep RL with a Robust Adaptation Module -
Subject- and Dataset-Aware Neural Field for HRTF Modeling -
Local Density-Based Anomaly Score Normalization for Domain Generalization -
MEL-PETs Joint-Context Attack for LLM Privacy Challenge -
Learned Born Operator for Reflection Tomographic Imaging -
MEL-PETs Defense for LLM Privacy Challenge -
Long-Tailed Online Anomaly Detection dataset -
Group Representation Networks -
Stabilizing Subject Transfer in EEG Classification with Divergence Estimation -
Task-Aware Unified Source Separation -
Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization -
ComplexVAD Dataset -
Self-Monitored Inference-Time INtervention for Generative Music Transformers -
Radar dEtection TRansformer -
Millimeter-wave Multi-View Radar Dataset -
Gear Extensions of Neural Radiance Fields -
Long-Tailed Anomaly Detection Dataset -
Target-Speaker SEParation -
Pixel-Grounded Prototypical Part Networks -
Steered Diffusion -
BAyesian Network for adaptive SAmple Consensus -
Meta-Learning State Space Models -
Explainable Video Anomaly Localization -
Simple Multimodal Algorithmic Reasoning Task Dataset -
Partial Group Convolutional Neural Networks -
SOurce-free Cross-modal KnowledgE Transfer -
Audio-Visual-Language Embodied Navigation in 3D Environments -
Nonparametric Score Estimators -
3D MOrphable STyleGAN -
Instance Segmentation GAN -
Audio Visual Scene-Graph Segmentor -
Generalized One-class Discriminative Subspaces -
Hierarchical Musical Instrument Separation -
Generating Visual Dynamics from Sound and Context -
Adversarially-Contrastive Optimal Transport -
Online Feature Extractor Network -
MotionNet -
FoldingNet++ -
Quasi-Newton Trust Region Policy Optimization -
Landmarks’ Location, Uncertainty, and Visibility Likelihood -
Robust Iterative Data Estimation -
Gradient-based Nikaido-Isoda -
Circular Maze Environment -
Discriminative Subspace Pooling -
Kernel Correlation Network -
Fast Resampling on Point Clouds via Graphs -
FoldingNet -
Deep Category-Aware Semantic Edge Detection -
MERL Shopping Dataset -
Open Vocabulary Attribute Detection Dataset
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