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 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.
- AWARD MERL team wins the Listener Acoustic Personalisation (LAP) 2024 Challenge
  Date: August 29, 2024
  Awarded to: Yoshiki Masuyama, Gordon Wichern, Francois G. Germain, Christopher Ick, and Jonathan Le Roux
  MERL Contacts: François Germain; Jonathan Le Roux; Gordon Wichern; Yoshiki Masuyama
  Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
  Brief
  - MERL's Speech & Audio team ranked 1st out of 7 teams in Task 2 of the 1st SONICOM Listener Acoustic Personalisation (LAP) Challenge, which focused on "Spatial upsampling for obtaining a high-spatial-resolution HRTF from a very low number of directions". The team was led by Yoshiki Masuyama, and also included Gordon Wichern, Francois Germain, MERL intern Christopher Ick, and Jonathan Le Roux.
    
    The LAP Challenge workshop and award ceremony was hosted by the 32nd European Signal Processing Conference (EUSIPCO 24) on August 29, 2024 in Lyon, France. Yoshiki Masuyama presented the team's method, "Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization", and received the award from Prof. Michele Geronazzo (University of Padova, IT, and Imperial College London, UK), Chair of the Challenge's Organizing Committee.
    
    The LAP challenge aims to explore challenges in the field of personalized spatial audio, with the first edition focusing on the spatial upsampling and interpolation of head-related transfer functions (HRTFs). HRTFs with dense spatial grids are required for immersive audio experiences, but their recording is time-consuming. Although HRTF spatial upsampling has recently shown remarkable progress with approaches involving neural fields, HRTF estimation accuracy remains limited when upsampling from only a few measured directions, e.g., 3 or 5 measurements. The MERL team tackled this problem by proposing a retrieval-augmented neural field (RANF). RANF retrieves a subject whose HRTFs are close to those of the target subject at the measured directions from a library of subjects. The HRTF of the retrieved subject at the target direction is fed into the neural field in addition to the desired sound source direction. The team also developed a neural network architecture that can handle an arbitrary number of retrieved subjects, inspired by a multi-channel processing technique called transform-average-concatenate.
See All Awards for Machine Learning
News & Events
- EVENT MERL Contributes to ICASSP 2025
  Date: Sunday, April 6, 2025 - Friday, April 11, 2025
  Location: Hyderabad, India
  MERL Contacts: Wael H. Ali; Petros T. Boufounos; Radu Corcodel; François Germain; Chiori Hori; Siddarth Jain; Devesh K. Jha; Toshiaki Koike-Akino; Jonathan Le Roux; Yanting Ma; Hassan Mansour; Yoshiki Masuyama; Joshua Rapp; Diego Romeres; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
  Research Areas: Artificial Intelligence, Communications, Computational Sensing, Electronic and Photonic Devices, Machine Learning, Robotics, Signal Processing, Speech & Audio
  Brief
  - MERL has made numerous contributions to both the organization and technical program of ICASSP 2025, which is being held in Hyderabad, India from April 6-11, 2025.
    
    Sponsorship
    
    MERL is proud to be a Silver Patron of the conference and will participate in the student job fair on Thursday, April 10. Please join this session to learn more about employment opportunities at MERL, including openings for research scientists, post-docs, and interns.
    
    MERL is pleased to be the sponsor of two IEEE Awards that will be presented at the conference. We congratulate Prof. Björn Erik Ottersten, the recipient of the 2025 IEEE Fourier Award for Signal Processing, and Prof. Shrikanth Narayanan, the recipient of the 2025 IEEE James L. Flanagan Speech and Audio Processing Award. Both awards will be presented in-person at ICASSP by Anthony Vetro, MERL President & CEO.
    
    Technical Program
    
    MERL is presenting 15 papers in the main conference on a wide range of topics including source separation, sound event detection, sound anomaly detection, speaker diarization, music generation, robot action generation from video, indoor airflow imaging, WiFi sensing, Doppler single-photon Lidar, optical coherence tomography, and radar imaging. Another paper on spatial audio will be presented at the Generative Data Augmentation for Real-World Signal Processing Applications (GenDA) Satellite Workshop.
    
    MERL Researchers Petros Boufounos and Hassan Mansour will present a Tutorial on “Computational Methods in Radar Imaging” in the afternoon of Monday, April 7.
    
    Petros Boufounos will also be giving an industry talk on Thursday April 10 at 12pm, on “A Physics-Informed Approach to Sensing".
    
    About ICASSP
    
    ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event has been attracting more than 4000 participants each year.
- TALK [MERL Seminar Series 2025] Andy Zou presents talk titled Red Teaming AI Agents in-the-wild: Revealing Deployment Vulnerabilities
  Date & Time: Wednesday, March 26, 2025; 1:00 PM
  Speaker: Andy Zou, CMU & Gray Swan AI
  MERL Host: Ye Wang
  Research Areas: Artificial Intelligence, Machine Learning, Information Security
  Abstract
  - This presentation demonstrates how red teaming uncovers critical vulnerabilities in AI agents that challenge assumptions about safe deployment. The talk discusses the risks of integrating AI into real-world applications and recommends practical safeguards to enhance resilience and ensure dependable deployment in high-risk settings.
See All News & Events for Machine Learning
Research Highlights
Internships
- EA0070: Internship - Multi-modal sensor fusion
  
  MERL is looking for a self-motivated intern to work on multi-modal sensor fusion for health condition monitoring and predictive maintenance of motor drive systems. The ideal candidate would be a Ph.D. candidate in electrical engineering or computer science with solid research background in signal processing and machine learning. Experience in motor drive system is a plus. The intern is expected to collaborate with MERL researchers to collect data, explore multi-modal data relationship, and prepare manuscripts for publications. The total duration is anticipated to be 3 months and the start date is flexible.
  Required Specific Experience
  - Experience with multi-modal sensor fusion.
- EA0073: Internship - Fault Detection for Electric Machines
  
  MERL is seeking a motivated and qualified individual to conduct research on electric machine fault analysis and detection methods. Ideal candidates should be Ph.D. students with a solid background and publication record in one more research area on electric machines: electric and magnetic modeling, machine design and prototyping, harmonic analysis, fault detection, and predictive maintenance. Knowledge on data analysis and machine learning algorithms, and strong programming skills using Python/PyTorch are expected. Research experience on modeling and analysis of electric machines and fault diagnosis is desired. Senior Ph.D. students in related expertise, such as electrical engineering, mechanical engineering, and applied physics are encouraged to apply. Start date for this internship is flexible and the duration is 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.
See All Internships for Machine Learning
Openings
See All Openings at MERL
Recent Publications
- Chakrabarty, A., Vanfretti, L., Wang, Y., Mineyuki, T., Zhan, S., Tang, W.-T., Paulson, J.A., Deshpande, V.M., Bortoff, S.A., Laughman, C.R., "Time-Series Generative Networks for Synthesizing Realistic Scenarios in Occupant-Centric Building Simulation", Building Simulation, April 2025.
  BibTeX TR2025-043 PDF
  - @article{Chakrabarty2025apr,
  - author = {Chakrabarty, Ankush and Vanfretti, Luigi and Wang, Ye and Mineyuki, Takuma and Zhan, Sicheng and Tang, Wei-Ting and Paulson, Joel A. and Deshpande, Vedang M. and Bortoff, Scott A. and Laughman, Christopher R.},
  - title = {{Time-Series Generative Networks for Synthesizing Realistic Scenarios in Occupant-Centric Building Simulation}},
  - journal = {Building Simulation},
  - year = 2025,
  - month = apr,
  - url = {https://www.merl.com/publications/TR2025-043}
  - }
- Ick, C., Wichern, G., Masuyama, Y., Germain, F.G., Le Roux, J., "Data Augmentation Using Neural Acoustic Fields With Retrieval-Augmented Pre-training", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Satellite Workshop on Generative Data Augmentation for Real-World Signal Processing Applications (GenDA), April 2025.
  BibTeX TR2025-045 PDF
  - @inproceedings{Ick2025apr,
  - author = {Ick, Christopher and Wichern, Gordon and Masuyama, Yoshiki and Germain, François G and {Le Roux}, Jonathan},
  - title = {{Data Augmentation Using Neural Acoustic Fields With Retrieval-Augmented Pre-training}},
  - booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Satellite Workshop on Generative Data Augmentation for Real-World Signal Processing Applications (GenDA)},
  - year = 2025,
  - month = apr,
  - url = {https://www.merl.com/publications/TR2025-045}
  - }
- Smedemark-Margulies, N., Wang, Y., Koike-Akino, T., Liu, J., Parsons, K., Bicer, Y., Erdogmus, D., "Improving Subject Transfer in EEG Classification with Divergence Estimation", Journal of Neural Engineering, DOI: 10.1088/1741-2552/ad9777, Vol. 21, No. 6, April 2025.
  BibTeX TR2025-044 PDF Software
  - @article{Smedemark-Margulies2025apr,
  - author = {Smedemark-Margulies, Niklas and Wang, Ye and Koike-Akino, Toshiaki and Liu, Jing and Parsons, Kieran and Bicer, Yunus and Erdogmus, Deniz},
  - title = {{Improving Subject Transfer in EEG Classification with Divergence Estimation}},
  - journal = {Journal of Neural Engineering},
  - year = 2025,
  - volume = 21,
  - number = 6,
  - month = apr,
  - doi = {10.1088/1741-2552/ad9777},
  - url = {https://www.merl.com/publications/TR2025-044}
  - }
- Wang, J.-W., Nikovski, D.N., "Learning Visual Servoing for Nonholonomic Mobile Robots with Uncalibrated Cameras", The 40th ACM/SIGAPP Symposium On Applied Computing, March 2025.
  BibTeX TR2025-042 PDF
  - @inproceedings{Wang2025mar2,
  - author = {Wang, Jen-Wei and Nikovski, Daniel N.},
  - title = {{Learning Visual Servoing for Nonholonomic Mobile Robots with Uncalibrated Cameras}},
  - booktitle = {The 40th ACM/SIGAPP Symposium On Applied Computing},
  - year = 2025,
  - month = mar,
  - url = {https://www.merl.com/publications/TR2025-042}
  - }
- Das, A., Rahman, S., Xiang, X., Palash, R.H., Hossain, T., Sikder, B., Yagyu, E., Nakamura, M., Teo, K.H., Chowdhury, N., "Inverse Design of AlGaN/GaN HEMT RF Device with Source Connected Field Plate", Advanced Theory and Simulations, March 2025.
  BibTeX TR2025-040 PDF
  - @article{Das2025mar,
  - author = {Das, Aurick and Rahman, Saimur and Xiang, Xiaofeng and Palash, Raffd Hassan and Hossain, Toiyob and Sikder, Bejoy and Yagyu, Eiji and Nakamura, Marika and Teo, Koon Hoo and Chowdhury, Nadim},
  - title = {{Inverse Design of AlGaN/GaN HEMT RF Device with Source Connected Field Plate}},
  - journal = {Advanced Theory and Simulations},
  - year = 2025,
  - month = mar,
  - url = {https://www.merl.com/publications/TR2025-040}
  - }
- Ranade, S., Pais, G., Whitaker, R., Nascimento, J., Miraldo, P., Ramalingam, S., "SurfR: Surface Reconstruction with Multi-scale Attention", International Conference on 3D Vision (3DV), March 2025.
  BibTeX TR2025-039 PDF
  - @inproceedings{Ranade2025mar,
  - author = {Ranade, Siddhant and Pais, Goncalo and Whitaker, Ross and Nascimento, Jacinto and Miraldo, Pedro and Ramalingam, Srikumar},
  - title = {{SurfR: Surface Reconstruction with Multi-scale Attention}},
  - booktitle = {International Conference on 3D Vision (3DV)},
  - year = 2025,
  - month = mar,
  - url = {https://www.merl.com/publications/TR2025-039}
  - }
- Araki, S., Ito, N., Haeb-Umbach, R., Wichern, G., Wang, Z.-Q., Mitsufuji, Y., "30+ Years of Source Separation Research: Achievements and Future Challenges", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2025.
  BibTeX TR2025-036 PDF
  - @inproceedings{Araki2025mar,
  - author = {Araki, Shoko and Ito, Nobutaka and Haeb-Umbach, Reinhold and Wichern, Gordon and Wang, Zhong-Qiu and Mitsufuji, Yuki},
  - title = {{30+ Years of Source Separation Research: Achievements and Future Challenges}},
  - booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
  - year = 2025,
  - month = mar,
  - url = {https://www.merl.com/publications/TR2025-036}
  - }
- Ebbers, J., Germain, F.G., Wilkinghoff, K., Wichern, G., Le Roux, J., "No Class Left Behind: A Closer Look at Class Balancing for Audio Tagging", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2025.
  BibTeX TR2025-037 PDF
  - @inproceedings{Ebbers2025mar,
  - author = {Ebbers, Janek and Germain, François G and Wilkinghoff, Kevin and Wichern, Gordon and {Le Roux}, Jonathan},
  - title = {{No Class Left Behind: A Closer Look at Class Balancing for Audio Tagging}},
  - booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
  - year = 2025,
  - month = mar,
  - url = {https://www.merl.com/publications/TR2025-037}
  - }
See All Publications for Machine Learning
Videos

[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:

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)
MEL-PETs Joint-Context Attack for LLM Privacy Challenge (melpets-llmpc2024-red-team)

See All Downloads