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.

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Researchers
Awards
- 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.
- AWARD University of Padua and MERL team wins the AI Olympics with RealAIGym competition at IROS24
  Date: October 17, 2024
  Awarded to: Niccolò Turcato, Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli, Diego Romeres
  MERL Contact: Diego Romeres
  Research Areas: Artificial Intelligence, Dynamical Systems, Machine Learning, Robotics
  Brief
  - The team composed of the control group at the University of Padua and MERL's Optimization and Robotic team ranked 1st out of the 4 finalist teams that arrived to the 2nd AI Olympics with RealAIGym competition at IROS 24, which focused on control of under-actuated robots. The team was composed by Niccolò Turcato, Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli and Diego Romeres. The competition was organized by the German Research Center for Artificial Intelligence (DFKI), Technical University of Darmstadt and Chalmers University of Technology.
    
    The competition and award ceremony was hosted by IEEE International Conference on Intelligent Robots and Systems (IROS) on October 17, 2024 in Abu Dhabi, UAE. Diego Romeres presented the team's method, based on a model-based reinforcement learning algorithm called MC-PILCO.
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News & Events
- NEWS MERL contributes to ICRA 2025
  Date: May 19, 2025 - May 23, 2025
  Where: IEEE ICRA
  MERL Contacts: Stefano Di Cairano; Jianlin Guo; Chiori Hori; Siddarth Jain; Devesh K. Jha; Toshiaki Koike-Akino; Philip V. Orlik; Arvind Raghunathan; Diego Romeres; Yuki Shirai; Abraham P. Vinod; Yebin Wang
  Research Areas: Artificial Intelligence, Computer Vision, Control, Dynamical Systems, Machine Learning, Optimization, Robotics, Human-Computer Interaction
  Brief
  - MERL made significant contributions to both the organization and the technical program of the International Conference on Robotics and Automation (ICRA) 2025, which was held in Atlanta, Georgia, USA, from May 19th to May 23rd.
    
    MERL was a Bronze sponsor of the conference, and MERL researchers chaired four sessions in the areas of Manipulation Planning, Human-Robot Collaboration, Diffusion Policy, and Learning for Robot Control.
    
    MERL researchers presented four papers in the main conference on the topics of contact-implicit trajectory optimization, proactive robotic assistance in human-robot collaboration, diffusion policy with human preferences, and dynamic and model learning of robotic manipulators. In addition, five more papers were presented in the workshops: “Structured Learning for Efficient, Reliable, and Transparent Robots,” “Safely Leveraging Vision-Language Foundation Models in Robotics: Challenges and Opportunities,” “Long-term Human Motion Prediction,” and “The Future of Intelligent Manufacturing: From Innovation to Implementation.”
    
    MERL researcher Diego Romeres delivered an invited talk titled “Dexterous Robotics: From Multimodal Sensing to Real-World Physical Interactions.”
    
    MERL also collaborated with the University of Padua on one of the conference’s challenges: the “3rd AI Olympics with RealAIGym” (https://ai-olympics.dfki-bremen.de).
    
    During the conference, MERL researchers received the IEEE Transactions on Automation Science and Engineering Best New Application Paper Award for their paper titled “Smart Actuation for End-Edge Industrial Control Systems.”
    
    About ICRA
    
    The IEEE International Conference on Robotics and Automation (ICRA) is the flagship conference of the IEEE Robotics and Automation Society and the world’s largest and most comprehensive technical conference focused on research advances and the latest technological developments in robotics. The event attracts over 7,000 participants, 143 partners and exhibitors, and receives more than 4,000 paper submissions.
- NEWS Suhas Lohit presents invited talk at Boston Symmetry Day 2025
  Date: March 31, 2025
  Where: Northeastern University, Boston, MA
  MERL Contact: Suhas Lohit
  Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
  Brief
  - MERL researcher Suhas Lohit was an invited speaker at Boston Symmetry Day, held at Northeastern University. Boston Symmetry Day, an annual workshop organized by researchers at MIT and Northeastern, brought together attendees interested in symmetry-informed machine learning and its applications. Suhas' talk, titled “Efficiency for Equivariance, and Efficiency through Equivariance” discussed recent MERL works that show how to build general and efficient equivariant neural networks, and how equivariance can be utilized in self-supervised learning to yield improved 3D object detection. The abstract and slides can be found in the link below.
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Research Highlights
Internships
- ST0105: Internship - Surrogate Modeling for Sound Propagation
  
  MERL is seeking a motivated and qualified individual to work on fast surrogate models for sound emission and propagation from complex vibrating structures, with applications in HVAC noise reduction. The ideal candidate will be a PhD student in engineering or related fields with a solid background in frequency-domain acoustic modeling and numerical techniques for partial differential equations (PDEs). Preferred skills include knowledge of the boundary element method (BEM), data-driven modeling, and physics-informed machine learning. Publication of the results obtained during the internship is expected. The duration is expected to be at least 3 months with a flexible start date.
- MS0098: Internship - Control and Estimation for Large-Scale Thermofluid Systems
  
  MERL is seeking a motivated graduate student to research methods for state and parameter estimation and optimization of large-scale systems for process applications. Representative applications include large vapor-compression cycles and other multiphysical systems for energy conversion that couple thermodynamic, fluid, and electrical domains. The ideal candidate would have a solid background in control and estimation, numerical methods, and optimization; strong programming skills and experience with Julia/Python/Matlab are also expected. Knowledge of the fundamental physics of thermofluid flows (e.g., thermodynamics, heat transfer, and fluid mechanics), nonlinear dynamics, or equation-oriented languages (Modelica, gPROMS) is a plus. The expected duration of this internship is 3 months.
- CI0082: Internship - Quantum AI
  
  MERL is excited to announce an internship opportunity in the field of Quantum Machine Learning (QML) and Quantum AI (QAI). We are seeking a highly motivated and talented individual to join our research team. This is an exciting opportunity to make a real impact in the field of quantum computing and AI, with the aim of publishing at leading research venues.
  Responsibilities:
  - Conduct cutting-edge research in quantum machine learning.
  - Collaborate with a team of experts in quantum computing, deep learning, and signal processing.
  - Develop and implement algorithms using PyTorch and PennyLane.
  - Publish research results at leading research venues.
  Qualifications:
  - Currently pursuing a PhD or a post-graduate researcher in a relevant field.
  - Strong background and solid publication records in quantum computing, deep learning, and signal processing.
  - Proficient programming skills in PyTorch and PennyLane are highly desirable.
  What We Offer:
  - An opportunity to work on groundbreaking research in a leading research lab.
  - Collaboration with a team of experienced researchers.
  - A stimulating and supportive work environment.
  If you are passionate about quantum machine learning and meet the above qualifications, we encourage you to apply. Please submit your resume and a brief cover letter detailing your research experience and interests. Join us at MERL and contribute to the future of quantum machine learning!
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Openings
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Recent Publications
- Chen, X., Liu, J., Wang, Y., Brand, M., Wang, P., Koike-Akino, T., "TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) workshop on Efficient and On-Device Generation, June 2025.
  BibTeX TR2025-079 PDF
  - @inproceedings{Chen2025jun,
  - author = {Chen, Xiangyu and Liu, Jing and Wang, Ye and Brand, Matthew and Wang, Pu and Koike-Akino, Toshiaki},
  - title = {{TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models}},
  - booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) workshop on Efficient and On-Device Generation},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-079}
  - }
- Hegde, D., Lohit, S., Peng, K.-C., Jones, M.J., Patel, V.M., "Multimodal 3D Object Detection on Unseen Domains", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, June 2025.
  BibTeX TR2025-078 PDF
  - @inproceedings{Hegde2025jun,
  - author = {Hegde, Deepti and Lohit, Suhas and Peng, Kuan-Chuan and Jones, Michael J. and Patel, Vishal M.},
  - title = {{Multimodal 3D Object Detection on Unseen Domains}},
  - booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-078}
  - }
- Jung, Y.G., Park, J., Yoon, J., Peng, K.-C., Kim, W., Teoh, A.B.J., Camps, O., "TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2025.
  BibTeX TR2025-077 PDF Presentation
  - @inproceedings{Jung2025jun,
  - author = {{{Jung, Yoon G. and Park, Jaewoo and Yoon, Jaeho and Peng, Kuan-Chuan and Kim, Wonchul and Teoh, Andrew B. J. and Camps, Octavia}}},
  - title = {{{TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection}}},
  - booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-077}
  - }
- Koike-Akino, T., Chen, X., Liu, J., Wang, Y., Wang, P., Brand, M., "LatentLLM: Attention-Aware Joint Tensor Compression", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, June 2025.
  BibTeX TR2025-075 PDF
  - @inproceedings{Koike-Akino2025jun,
  - author = {Koike-Akino, Toshiaki and Chen, Xiangyu and Liu, Jing and Wang, Ye and Wang, Pu and Brand, Matthew},
  - title = {{LatentLLM: Attention-Aware Joint Tensor Compression}},
  - booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-075}
  - }
- Lai, Y.-H., Ebbers, J., Wang, Y.-C.F., Germain, F.G., Jones, M.J., Chatterjee, M., "UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2025.
  BibTeX TR2025-072 PDF
  - @inproceedings{Lai2025jun,
  - author = {Lai, Yung-Hsuan and Ebbers, Janek and Wang, Yu-Chiang Frank and Germain, François G and Jones, Michael J. and Chatterjee, Moitreya},
  - title = {{UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing}},
  - booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-072}
  - }
- Li, K., Zhang, T., Peng, K.-C., Wang, G., "PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop, June 2025.
  BibTeX TR2025-076 PDF
  - @inproceedings{Li2025jun,
  - author = {Li, Kaidong and Zhang, Tianxiao and Peng, Kuan-Chuan and Wang, Guanghui},
  - title = {{PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector}},
  - booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-076}
  - }
- Ni, Y., Wen, S., Koniusz, P., Cherian, A., "Noise Consistency Regularization for Improved Subject-Driven Image Synthesis", IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR), June 2025.
  BibTeX TR2025-073 PDF
  - @inproceedings{Ni2025jun,
  - author = {Ni, Yao and Wen, Song and Koniusz, Piotr and Cherian, Anoop},
  - title = {{Noise Consistency Regularization for Improved Subject-Driven Image Synthesis}},
  - booktitle = {IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR)},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-073}
  - }
- Sawada, N., Miraldo, P., Lohit, S., Marks, T.K., Chatterjee, M., "FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations", IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR), June 2025.
  BibTeX TR2025-074 PDF
  - @inproceedings{Sawada2025jun,
  - author = {Sawada, Naoko and Miraldo, Pedro and Lohit, Suhas and Marks, Tim K. and Chatterjee, Moitreya},
  - title = {{FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations}},
  - booktitle = {IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR)},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-074}
  - }
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Videos

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:

Group Representation Networks (G-RepsNets)
Self-Monitored Inference-Time INtervention for Generative Music Transformers (SMITIN)
Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization (ranf-hrtf)
Radar dEtection TRansformer (RETR)
Meta-Learning State Space Models (MetaLIC)
MEL-PETs Defense for LLM Privacy Challenge (melpets-llmpc2024-blue-team)

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