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 MERL team wins the Generative Data Augmentation of Room Acoustics (GenDARA) 2025 Challenge
  Date: April 7, 2025
  Awarded to: Christopher Ick, Gordon Wichern, Yoshiki Masuyama, François G. Germain, and Jonathan Le Roux
  MERL Contacts: Jonathan Le Roux; Yoshiki Masuyama; Gordon Wichern
  Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
  Brief
  - MERL's Speech & Audio team ranked 1st out of 3 teams in the Generative Data Augmentation of Room Acoustics (GenDARA) 2025 Challenge, which focused on “generating room impulse responses (RIRs) to supplement a small set of measured examples and using the augmented data to train speaker distance estimation (SDE) models". The team was led by MERL intern Christopher Ick, and also included Gordon Wichern, Yoshiki Masuyama, François G. Germain, and Jonathan Le Roux.
    
    The GenDARA Challenge was organized as part of the Generative Data Augmentation (GenDA) workshop at the 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2025), and held on April 7, 2025 in Hyderabad, India. Yoshiki Masuyama presented the team's method, "Data Augmentation Using Neural Acoustic Fields With Retrieval-Augmented Pre-training".
    
    The GenDARA challenge aims to promote the use of generative AI to synthesize RIRs from limited room data, as collecting or simulating RIR datasets at scale remains a significant challenge due to high costs and trade-offs between accuracy and computational efficiency. The challenge asked participants to first develop RIR generation systems capable of expanding a sparse set of labeled room impulse responses by generating RIRs at new source–receiver positions. They were then tasked with using this augmented dataset to train speaker distance estimation systems. Ranking was determined by the overall performance on the downstream SDE task. MERL’s approach to the GenDARA challenge centered on a geometry-aware neural acoustic field model that was first pre-trained on a large external RIR dataset to learn generalizable mappings from 3D room geometry to room impulse responses. For each challenge room, the model was then adapted or fine-tuned using the small number of provided RIRs, enabling high-fidelity generation of RIRs at unseen source–receiver locations. These augmented RIR sets were subsequently used to train the SDE system, improving speaker distance estimation by providing richer and more diverse acoustic training data.
- 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.
See All Awards for Machine Learning
News & Events
- NEWS Abraham Vinod Delivers Invited Talks at The University of Texas at Austin and The University of Texas at Dallas
  Date: November 11, 2025 - November 13, 2025
  MERL Contact: Abraham P. Vinod
  Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Optimization, Robotics
  Brief
  - MERL researcher Abraham Vinod was invited to present MERL's latest research at the University of Texas at Austin and The University of Texas at Dallas this November. His talk discussed a tractable set-based method for a broad class of robust control problems with nonlinear dynamics and bounded uncertainty, with applications to powered descent guidance and drone motion planning problems. Additionally, he also presented MERL's recent research on environmental monitoring using hetereogenous robots, with applications in disaster management and search-and-rescue.
- NEWS Jonathan Le Roux Elected Vice Chair and Gordon Wichern Reelected as Member of the IEEE AASP Technical Committee
  Date: November 14, 2025
  MERL Contacts: Jonathan Le Roux; Gordon Wichern
  Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
  Brief
  - Two members of MERL’s Speech and Audio Team have been elected to important positions within the IEEE Audio and Acoustic Signal Processing Technical Committee (AASP TC), a leading body of the IEEE Signal Processing Society that brings together experts from academia and industry working on speech, music, environmental audio, spatial acoustics, enhancement, separation, and machine learning for audio. The committee plays a central role in guiding the scientific direction of the field by promoting emerging research areas, shaping major conferences such as ICASSP and WASPAA, organizing special sessions and tutorials, and fostering a vibrant and collaborative global community.
    
    Jonathan Le Roux, Senior Team Leader and Distinguished Research Scientist, has been elected as the next Vice Chair of the AASP TC. His election reflects his longstanding contributions to the audio and acoustic signal processing community, his leadership in workshop and conference organization, and his significant impact across a wide range of research areas within the TC’s scope. Jonathan will serve a one-year term as Vice Chair, after which he will succeed Prof. Minje Kim (UIUC) as Chair of the AASP TC for a two-year term in 2027–28, helping steer the committee’s strategic initiatives and continued growth.
    
    During the same election, Senior Principal Research Scientist Gordon Wichern, who currently serves as Chair of the Review Subcommittee, was reelected for a second three-year term as a member of the AASP TC, serving from 2026 to 2028. His continued presence on the committee reflects his impactful research and active service to the audio and acoustic signal processing community.
See All News & Events for Machine Learning
Research Highlights
Internships
See All Internships for Machine Learning
Openings
See All Openings at MERL
Recent Publications
- Hori, C., Masuyama, Y., Jain, S., Corcodel, R., Jha, D.K., Romeres, D., Le Roux, J., "Robot Confirmation Generation and Action Planning Using Long-context Q-Former Integrated with Multimodal LLM", IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), December 2025.
  BibTeX TR2025-167 PDF
  - @inproceedings{Hori2025dec,
  - author = {Hori, Chiori and Masuyama, Yoshiki and Jain, Siddarth and Corcodel, Radu and Jha, Devesh K. and Romeres, Diego and {Le Roux}, Jonathan},
  - title = {{Robot Confirmation Generation and Action Planning Using Long-context Q-Former Integrated with Multimodal LLM}},
  - booktitle = {IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)},
  - year = 2025,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2025-167}
  - }
- Xie, X., Mowlavi, S., Benosman, M., "Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization", Advances in Neural Information Processing Systems (NeurIPS) workshop on Machine Learning and the Physical Sciences (ML4PS), December 2025.
  BibTeX TR2025-166 PDF
  - @inproceedings{Xie2025dec,
  - author = {{{Xie, Xiaoyu and Mowlavi, Saviz and Benosman, Mouhacine}}},
  - title = {{{Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization}}},
  - booktitle = {Advances in Neural Information Processing Systems (NeurIPS) workshop on Machine Learning and the Physical Sciences (ML4PS)},
  - year = 2025,
  - month = dec,
  - url = {https://www.merl.com/publications/TR2025-166}
  - }
- Xiang, X., Peng, K.-C., Lohit, S., Jones, M.J., Zhang, J., "Towards Open-Vocabulary Multimodal 3D Object Detection with Attributes", British Machine Vision Conference (BMVC), November 2025.
  BibTeX TR2025-162 PDF Video Data Presentation
  - @inproceedings{Xiang2025nov,
  - author = {{{Xiang, Xinhao and Peng, Kuan-Chuan and Lohit, Suhas and Jones, Michael J. and Zhang, Jiawei}}},
  - title = {{{Towards Open-Vocabulary Multimodal 3D Object Detection with Attributes}}},
  - booktitle = {British Machine Vision Conference (BMVC)},
  - year = 2025,
  - month = nov,
  - url = {https://www.merl.com/publications/TR2025-162}
  - }
- Wilkinghoff, K., Fujimura, T., Imoto, K., Le Roux, J., Tan, Z.-H., Toda, T., "Handling Domain Shifts for Anomalous Sound Detection: A Review of DCASE-Related Work", Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), DOI: 10.5281/zenodo.17251589, October 2025, pp. 20-24.
  BibTeX TR2025-157 PDF
  - @inproceedings{Wilkinghoff2025oct,
  - author = {Wilkinghoff, Kevin and Fujimura, Takuya and Imoto, Keisuke and {Le Roux}, Jonathan and Tan, Zheng-Hua and Toda, Tomoki},
  - title = {{Handling Domain Shifts for Anomalous Sound Detection: A Review of DCASE-Related Work}},
  - booktitle = {Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE)},
  - year = 2025,
  - pages = {20--24},
  - month = oct,
  - doi = {10.5281/zenodo.17251589},
  - isbn = {978-84-09-77652-8},
  - url = {https://www.merl.com/publications/TR2025-157}
  - }
- Chakrabarty, A., Wichern, G., Deshpande, V.M., Vinod, A.P., Berntorp, K., Laughman, C.R., "Meta-Learning for Physically-Constrained Neural System Identification", Neurocomputing, DOI: 10.1016/j.neucom.2025.130945, Vol. 651, pp. 130945, October 2025.
  BibTeX TR2025-159 PDF
  - @article{Chakrabarty2025nov,
  - author = {Chakrabarty, Ankush and Wichern, Gordon and Deshpande, Vedang M. and Vinod, Abraham P. and Berntorp, Karl and Laughman, Christopher R.},
  - title = {{Meta-Learning for Physically-Constrained Neural System Identification}},
  - journal = {Neurocomputing},
  - year = 2025,
  - volume = 651,
  - pages = 130945,
  - month = nov,
  - doi = {10.1016/j.neucom.2025.130945},
  - issn = {0925-2312},
  - url = {https://www.merl.com/publications/TR2025-159}
  - }
- Sun, H., Otake, Y., Matsuyama, K., Raghunathan, A., "Switchgear Partial Discharge Diagnosis Using Scarce Fault Records", IEEE PES Innovative Smart Grid Technologies Conference - Europe (ISGT Europe), October 2025.
  BibTeX TR2025-155 PDF
  - @inproceedings{Sun2025oct,
  - author = {Sun, Hongbo and Otake, Yasutomo and Matsuyama, Kotaro and Raghunathan, Arvind},
  - title = {{Switchgear Partial Discharge Diagnosis Using Scarce Fault Records}},
  - booktitle = {IEEE PES Innovative Smart Grid Technologies Conference - Europe (ISGT Europe)},
  - year = 2025,
  - month = oct,
  - url = {https://www.merl.com/publications/TR2025-155}
  - }
- Yataka, R., Wang, P., Boufounos, P.T., Takahashi, R., "Radar-Conditioned 3D Bounding Box Diffusion for Indoor Human Perception", IEEE International Conference on Computer Vision (ICCV) Workshop, October 2025.
  BibTeX TR2025-154 PDF
  - @inproceedings{Yataka2025oct,
  - author = {Yataka, Ryoma and Wang, Pu and Boufounos, Petros T. and Takahashi, Ryuhei},
  - title = {{Radar-Conditioned 3D Bounding Box Diffusion for Indoor Human Perception}},
  - booktitle = {IEEE International Conference on Computer Vision (ICCV) Workshop},
  - year = 2025,
  - month = oct,
  - url = {https://www.merl.com/publications/TR2025-154}
  - }
- Kuwabara, A., Kirihara, H., Kato, S., Koike-Akino, T., Fujihashi, T., "L-GGSC: Learnable Graph-based Gaussian Splatting Compression", IEEE International Conference on Computer Vision Workshops (ICCV), October 2025.
  BibTeX TR2025-148 PDF
  - @inproceedings{Kuwabara2025oct,
  - author = {Kuwabara, Akihiro and Kirihara, Hinata and Kato, Sorachi and Koike-Akino, Toshiaki and Fujihashi, Takuya},
  - title = {{L-GGSC: Learnable Graph-based Gaussian Splatting Compression}},
  - booktitle = {IEEE International Conference on Computer Vision Workshops (ICCV)},
  - year = 2025,
  - month = oct,
  - url = {https://www.merl.com/publications/TR2025-148}
  - }
See All Publications for Machine Learning
Videos

[ICASSP 2025] Indoor Airflow Imaging Using Physics-Informed Schlieren Tomography

[ICCV 2025] SAC-GNC: SAmple Consensus for adaptive Graduated Non-Convexity

[BMVC 2025] Towards Open-Vocabulary Multimodal 3D Object Detection with Attributes

[ICCV Workshop 2025] Joint Training of Image Generator and Detector for Road Defect Detection

[ICCV 2025] Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts

[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

MERL Research on Autonomous Vehicles

Semantic Scene Labeling

Obstacle Detection

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:

Radar-based 3D Pose Estimation using Transformer (RAPTR)
MMHOI Dataset: Modeling Complex 3D Multi-Human Multi-Object Interactions (MMHOI)
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)

See All Downloads