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 2025 European Control Conference
  Date: June 24, 2025 - June 27, 2025
  Where: Thessaloniki
  MERL Contacts: Stefano Di Cairano; Daniel N. Nikovski; Diego Romeres; Yebin Wang
  Research Areas: Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
  Brief
  - MERL researchers contributed to both the technical program and workshop organization at the 2025 European Control Conference (ECC), held in Thessaloniki, Greece, from June 24 to 27. ECC is one of the premier conferences in the field of control.
    
    In the main conference, MERL researchers presented four papers covering a range of topics, including: Representation learning, Motion planning for tractor-trailers, Motion planning for mobile manipulators, Learning high-dimensional dynamical systems, Model learning for robotics.
    
    Additionally, MERL co-organized a workshop with the University of Padua titled “Reinforcement Learning for Robotic Control: Recent Developments and Open Challenges.” MERL researcher Diego Romeres also delivered an invited talk titled “Human-Robot Collaborative Assembly” in that workshop.
- NEWS Toshiaki Koike-Akino to give a tutorial talk at ISIT 2025 Quantum Hackathon
  Date: June 22, 2025
  Where: IEEE International Symposium on Information Theory (ISIT)
  MERL Contact: Toshiaki Koike-Akino
  Research Areas: Artificial Intelligence, Communications, Data Analytics, Machine Learning, Optimization, Signal Processing, Human-Computer Interaction, Information Security
  Brief
  - Toshiaki Koike-Akino is invited to present a tutorial talk at IEEE ISIT 2025 Quantum Hackathon, to be held at Ann Arbor, Michigan, USA. The talk, entitled "Emerging Quantum AI Technology", will discuss the recent trends, challenges, and applications of quantum artificial intelligence (QAI) technologies.
    
    The ISIT 2025 Quantum Hackathon invites participants to explore the intersection of quantum computing and information theory. Participants will work with quantum simulators, available quantum hardware, and state-of-the-art development kits to create innovative solutions that connect quantum advancements with challenges in communication and signal processing.
    
    The IEEE International Symposium on Information Theory (ISIT) is the flagship conference of the IEEE Information Theory Society. The symposium centers around the presentation in all of the areas of information theory, including source and channel coding, communication theory and systems, cryptography and security, detection and estimation, networks, pattern recognition and learning, statistics, stochastic processes and complexity, and signal processing.
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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.
- 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.
- 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.
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Openings
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Recent Publications
- Almudévar, A., Hernández-Lobato, J.M., Khurana, S., Marxer, R., Ortega, A., "Aligning Multimodal Representations through an Information Bottleneck", International Conference on Machine Learning (ICML), July 2025.
  BibTeX TR2025-109 PDF
  - @inproceedings{Almudévar2025jul,
  - author = {Almudévar, Antonio and Hernández-Lobato, José, M and Khurana, Sameer and Marxer, Ricard and Ortega, Alfonso},
  - title = {{Aligning Multimodal Representations through an Information Bottleneck}},
  - booktitle = {International Conference on Machine Learning (ICML)},
  - year = 2025,
  - month = jul,
  - url = {https://www.merl.com/publications/TR2025-109}
  - }
- Zhang, Q., Avraamidou, S., Paulson, J.A., Thakkar, V., Wang, Z., Chiang, L., Braun, B., Rathi, T., Chakrabarty, A., Sorouifar, F., Tang, W.-T., Guertin, F., Munoz, P., Sampat, A., "Navigating the Trade-offs and Synergies of Economic and Environmental Sustainability Using Process Systems Engineering", American Control Conference (ACC), July 2025.
  BibTeX TR2025-106 PDF
  - @inproceedings{Zhang2025jul2,
  - author = {Zhang, Qi and Avraamidou, Styliani and Paulson, Joel A. and Thakkar, Vyom and Wang, Zhenyu and Chiang, Leo and Braun, Birgit and Rathi, Tushar and Chakrabarty, Ankush and Sorouifar, Farshud and Tang, Wei-Ting and Guertin, France and Munoz, Paola and Sampat, Apoorva},
  - title = {{Navigating the Trade-offs and Synergies of Economic and Environmental Sustainability Using Process Systems Engineering}},
  - booktitle = {American Control Conference (ACC)},
  - year = 2025,
  - month = jul,
  - url = {https://www.merl.com/publications/TR2025-106}
  - }
- Zhang, X., Mowlavi, S., Benosman, M., Basar, T., "Policy Optimization for PDE Control with a Warm Start", American Control Conference (ACC), July 2025.
  BibTeX TR2025-105 PDF
  - @inproceedings{Zhang2025jul,
  - author = {Zhang, Xiangyuan and Mowlavi, Saviz and Benosman, Mouhacine and Basar, Tamer},
  - title = {{Policy Optimization for PDE Control with a Warm Start}},
  - booktitle = {American Control Conference (ACC)},
  - year = 2025,
  - month = jul,
  - url = {https://www.merl.com/publications/TR2025-105}
  - }
- Chavez Armijos, A., Berntorp, K., Di Cairano, S., "Safe Interactive Motion Planning by Differentiable Optimal Control and Online Preference Learning", American Control Conference (ACC), July 2025.
  BibTeX TR2025-104 PDF
  - @inproceedings{ChavezArmijos2025jul,
  - author = {Chavez Armijos, Andres and Berntorp, Karl and {Di Cairano}, Stefano},
  - title = {{Safe Interactive Motion Planning by Differentiable Optimal Control and Online Preference Learning}},
  - booktitle = {American Control Conference (ACC)},
  - year = 2025,
  - month = jul,
  - url = {https://www.merl.com/publications/TR2025-104}
  - }
- Santra, A., Wang, P., Shaker, G., Mysore, B.S., Dolmans, G., Chen, Y., Shariati, N., Pandharipande, A., "Machine Learning-Powered Radio Frequency Sensing: A Review", IEEE Sensors Journal, June 2025.
  BibTeX TR2025-099 PDF
  - @article{Santra2025jun,
  - author = {Santra, Avik and Wang, Pu and Shaker, George and Mysore, Bhavani Shankar and Dolmans, Guido and Chen, Yan and Shariati, Negin and Pandharipande, Ashish},
  - title = {{Machine Learning-Powered Radio Frequency Sensing: A Review}},
  - journal = {IEEE Sensors Journal},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-099}
  - }
- Ma, H., Zhang, T., Li, N., Di Cairano, S., Wang, Y., "A Hierarchical Approach for Tractor-trailer Motion Planning Using Graph Search and Reinforcement Learning", European Control Conference (ECC), June 2025.
  BibTeX TR2025-093 PDF
  - @inproceedings{Ma2025jun,
  - author = {Ma, Haitong and Zhang, Tianpeng and Li, Na and {Di Cairano}, Stefano and Wang, Yebin},
  - title = {{A Hierarchical Approach for Tractor-trailer Motion Planning Using Graph Search and Reinforcement Learning}},
  - booktitle = {European Control Conference (ECC)},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-093}
  - }
- Wang, R., Wang, Y., Liu, J., Koike-Akino, T., "Quantum Diffusion Models for Few-Shot Learning", ICAD, June 2025.
  BibTeX TR2025-095 PDF
  - @inproceedings{Wang2025jun2,
  - author = {Wang, Ruhan and Wang, Ye and Liu, Jing and Koike-Akino, Toshiaki},
  - title = {{Quantum Diffusion Models for Few-Shot Learning}},
  - booktitle = {ICAD},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-095}
  - }
- Sun, S., Wang, Y., Koike-Akino, T., Yamamoto, T., Sakamoto, Y., Wang, B., "Image-based Deep Learning Models for Electric Motors", International Conference on the Computation of Electromagnetic Fields (COMPUMAG), June 2025.
  BibTeX TR2025-088 PDF
  - @inproceedings{Sun2025jun,
  - author = {Sun, Siyuan and Wang, Ye and Koike-Akino, Toshiaki and Yamamoto, Tatsuya and Sakamoto, Yusuke and Wang, Bingnan},
  - title = {{Image-based Deep Learning Models for Electric Motors}},
  - booktitle = {International Conference on the Computation of Electromagnetic Fields (COMPUMAG)},
  - year = 2025,
  - month = jun,
  - url = {https://www.merl.com/publications/TR2025-088}
  - }
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:

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

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