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
Jonathan
Le Roux
Toshiaki
Koike-Akino
Ye
Wang
Anoop
Cherian
Ankush
Chakrabarty
Tim K.
Marks
Philip V.
Orlik
Gordon
Wichern
Michael J.
Jones
Devesh K.
Jha
Stefano
Di Cairano
Chiori
Hori
Daniel N.
Nikovski
Kieran
Parsons
Karl
Berntorp
Diego
Romeres
Yebin
Wang
Christopher R.
Laughman
Pu
(Perry)
Wang
Mouhacine
Benosman
Kyeong Jin
(K.J.)
Kim
Hassan
Mansour
Rien
Quirynen
Arvind
Raghunathan
Matthew E.
Brand
Marcel
Menner
Bingnan
Wang
Petros T.
Boufounos
Jianlin
Guo
Suhas
Lohit
Hongbo
Sun
Radu
Corcodel
Siddarth
Jain
Hongtao
Qiao
William S.
Yerazunis
Scott A.
Bortoff
Chungwei
Lin
Dehong
Liu
Kuan-Chuan
Peng
Koon Hoo
Teo
Anthony
Vetro
Abraham P.
Vinod
Jing
Zhang
Jinyun
Zhang
Jose
Amaya
Marcus
Greiff
Yanting
Ma
Saleh
Nabi
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Awards
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AWARD Japan Telecommunications Advancement Foundation Award Date: March 15, 2022
Awarded to: Yukimasa Nagai, Jianlin Guo, Philip Orlik, Takenori Sumi, Benjamin A. Rolfe and Hiroshi Mineno
MERL Contacts: Jianlin Guo; Philip V. Orlik
Research Areas: Communications, Machine LearningBrief- MELCO/MERL research paper “Sub-1 GHz Frequency Band Wireless Coexistence for the Internet of Things” has won the 37th Telecommunications Advancement Foundation Award (Telecom System Technology Award) in Japan. This award started in 1984, and is given to research papers and works related to information and telecommunications that have made significant contributions and achievements to the advancement, development, and standardization of information and telecommunications from technical and engineering perspectives. The award recognizes both the IEEE 802.19.3 standardization efforts and the technological advancements using reinforcement learning and robust access methodologies for wireless communication system. This year, there were 43 entries with 5 winning awards and 3 winning encouragement awards. This is the first time MELCO/MERL has received this award. Our paper has been published by IEEE Access in 2021 and authors are Yukimasa Nagai, Jianlin Guo, Philip Orlik, Takenori Sumi, Benjamin A. Rolfe and Hiroshi Mineno.
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AWARD Mitsubishi Electric US Receives a 2022 CES Innovation Award for Touchless Elevator Control Jointly Developed with MERL Date: November 17, 2021
Awarded to: Elevators and Escalators Division of Mitsubishi Electric US, Inc.
MERL Contacts: Daniel N. Nikovski; William S. Yerazunis
Research Areas: Data Analytics, Machine Learning, Signal ProcessingBrief- The Elevators and Escalators Division of Mitsubishi Electric US, Inc. has been recognized as a 2022 CES® Innovation Awards honoree for its new PureRide™ Touchless Control for elevators, jointly developed with MERL. Sponsored by the Consumer Technology Association (CTA), the CES Innovation Awards is the largest and most influential technology event in the world. PureRide™ Touchless Control provides a simple, no-touch product that enables users to call an elevator and designate a destination floor by placing a hand or finger over a sensor. MERL initiated the development of PureRide™ in the first weeks of the COVID-19 pandemic by proposing the use of infra-red sensors for operating elevator call buttons, and participated actively in its rapid implementation and commercialization, resulting in a first customer installation in October of 2020.
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AWARD Daniel Nikovski receives Outstanding Reviewer Award at NeurIPS'21 Date: October 18, 2021
Awarded to: Daniel Nikovski
MERL Contact: Daniel N. Nikovski
Research Areas: Artificial Intelligence, Machine LearningBrief- Daniel Nikovski, Group Manager of MERL's Data Analytics group, has received an Outstanding Reviewer Award from the 2021 conference on Neural Information Processing Systems (NeurIPS'21). NeurIPS is the world's premier conference on neural networks and related technologies.
See All Awards for Machine Learning -
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News & Events
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NEWS MERL researchers win ASME Energy Systems Technical Committee Best Paper Award at 2022 American Control Conference Date: June 8, 2022
Where: 2022 American Control Conference
MERL Contacts: Ankush Chakrabarty; Christopher R. Laughman
Research Areas: Control, Machine Learning, Multi-Physical Modeling, OptimizationBrief- Researchers from EPFL (Wenjie Xu, Colin Jones) and EMPA (Bratislav Svetozarevic), in collaboration with MERL researchers Ankush Chakrabarty and Chris Laughman, recently won the ASME Energy Systems Technical Committee Best Paper Award at the 2022 American Control Conference for their work on "VABO: Violation-Aware Bayesian Optimization for Closed-Loop Performance Optimization with Unmodeled Constraints" out of 19 nominations and 3 finalists. The paper describes a data-driven framework for optimizing the performance of constrained control systems by systematically re-evaluating how cautiously/aggressively one should explore the search space to avoid sustained, large-magnitude constraint violations while tolerating small violations, and demonstrates these methods on a physics-based model of a vapor compression cycle.
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NEWS MERL researchers presented 9 papers at the American Control Conference (ACC) Date: June 8, 2022 - June 10, 2022
Where: Atlanta, GA
MERL Contacts: Karl Berntorp; Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Christopher R. Laughman; Marcel Menner; Rien Quirynen; Abraham P. Vinod; Avishai Weiss
Research Areas: Control, Machine Learning, OptimizationBrief- At the American Control Conference in Atlanta, GA, MERL presented 9 papers on subjects including autonomous-vehicle decision making and motion planning, realtime Bayesian inference and learning, reference governors for hybrid systems, Bayesian optimization, and nonlinear control.
See All News & Events for Machine Learning -
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Research Highlights
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Internships
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CI1733: ML for GNSS-based Applications
MERL is seeking a highly motivated, qualified intern to work on machine learning for Global Navigation Satellite System (GNSS) applications. The ideal candidate is working towards a PhD and is expected to develop innovative machine learning technologies to increase accuracy and integrity of GNSS-based positioning systems. Candidates should have strong knowledge about as many as possible of GNSS signal processing for multipath mitigation, handling RINEX data, neural network and learning techniques, such as feature extraction, deep machine learning, reinforcement learning, domain adaptation, and distributed learning. Proficient programming skills with PyTorch, Matlab, and C++, and strong mathematical analysis will be additional assets to this position. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply.
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MS1866: Deep Unsupervised/Semi-Supervised Learning for Smart Buildings
MERL is seeking a highly motivated and qualified intern to collaborate with the Multiphysical Systems (MS) team in research on unsupervised/semi-supervised learning using data from real building energy systems. The ideal candidate is expected to be working towards a Ph.D. in deep learning for time-series, with special interest in learning representations for deep clustering. Fluency in Python and either PyTorch/Tensorflow is required. Previous peer-reviewed publications in related research topics and/or experience with mining from real-world data is preferred. The minimum duration of the internship is 12 weeks; start time is flexible.
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MD1715: Electric Motor Fault Analysis
MERL is seeing a motivated and qualified individual to conduct research on electric machine fault analysis and detection. The ideal candidate should have solid background in electric machine theory, modeling, numerical analysis, operation, and fault detection techniques, including machine learning. Research experiences on modeling and analysis of electric machines and fault detection are required. Hands-on experience with permanent magnet motor design and analysis, and knowledge on machine learning are desirable. Senior Ph.D. students in related expertise are encouraged to apply. Start date for this internship is flexible.
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Recent Publications
- , "Calibrating building simulation models using multi-source datasets and meta-learned Bayesian optimization", Energy and Buildings, DOI: 10.1016/j.enbuild.2022.112278, Vol. 270, pp. 112278, September 2022.BibTeX TR2022-072 PDF
- @article{Zhan2023jan,
- author = {Zhan, Sicheng and Wichern, Gordon and Laughman, Christopher R. and Chong, Adrian and Chakrabarty, Ankush},
- title = {Calibrating building simulation models using multi-source datasets and meta-learned Bayesian optimization},
- journal = {Energy and Buildings},
- year = 2022,
- volume = 270,
- pages = 112278,
- month = sep,
- doi = {10.1016/j.enbuild.2022.112278},
- url = {https://www.merl.com/publications/TR2022-072}
- }
- , "AutoVAE: Mismatched Variational Autoencoder with Irregular Posterior Prior Pairing", IEEE International Symposium on Information Theory (ISIT), July 2022.BibTeX TR2022-071 PDF Video Presentation
- @inproceedings{Koike-Akino2022jul,
- author = {Koike-Akino, Toshiaki and Wang, Ye},
- title = {AutoVAE: Mismatched Variational Autoencoder with Irregular Posterior Prior Pairing},
- booktitle = {IEEE International Symposium on Information Theory (ISIT)},
- year = 2022,
- month = jul,
- url = {https://www.merl.com/publications/TR2022-071}
- }
- , "An Empirical Analysis of Boosting Deep Networks", International Joint Conference on Neural Networks (IJCNN), July 2022.BibTeX TR2022-075 PDF Presentation
- @inproceedings{Rambhatla2022jul,
- author = {Rambhatla, Sai and Jones, Michael J. and Chellappa, Rama},
- title = {An Empirical Analysis of Boosting Deep Networks},
- booktitle = {International Joint Conference on Neural Networks (IJCNN)},
- year = 2022,
- month = jul,
- url = {https://www.merl.com/publications/TR2022-075}
- }
- , "Location and Driver-Specific Vehicle Adaptation Using Crowdsourced Data", European Control Conference (ECC), DOI: 10.23919/ECC55457.2022.9838135, July 2022, pp. 769-774.BibTeX TR2022-095 PDF
- @inproceedings{Menner2022jul,
- author = {Menner, Marcel and Ma, Ziyi and Berntorp, Karl and Di Cairano, Stefano},
- title = {Location and Driver-Specific Vehicle Adaptation Using Crowdsourced Data},
- booktitle = {European Control Conference (ECC)},
- year = 2022,
- pages = {769--774},
- month = jul,
- doi = {10.23919/ECC55457.2022.9838135},
- url = {https://www.merl.com/publications/TR2022-095}
- }
- , "DNN-assisted phase distance tuned PSK modulation for PAM4-to-QPSK format conversion gateway node", Optics Express, DOI: 10.1364/OE.449812, Vol. 30, No. 7, pp. 10866-10876, June 2022.BibTeX TR2022-091 PDF
- @article{Kodama2022jun,
- author = {Kodama, Takahiro and Koike-Akino, Toshiaki and Millar, David S. and Kojima, Keisuke and Parsons, Kieran},
- title = {DNN-assisted phase distance tuned PSK modulation for PAM4-to-QPSK format conversion gateway node},
- journal = {Optics Express},
- year = 2022,
- volume = 30,
- number = 7,
- pages = {10866--10876},
- month = jun,
- doi = {10.1364/OE.449812},
- url = {https://www.merl.com/publications/TR2022-091}
- }
- , "Auto-Tuning of Controller and Online Trajectory Planner for Legged Robots", IEEE Robotics and Automation Letters, DOI: 10.1109/LRA.2022.3185387, Vol. 7, No. 3, pp. 7802-7809, June 2022.BibTeX TR2022-085 PDF
- @article{Schperberg2022jun,
- author = {Schperberg, Alexander and Di Cairano, Stefano and Menner, Marcel},
- title = {Auto-Tuning of Controller and Online Trajectory Planner for Legged Robots},
- journal = {IEEE Robotics and Automation Letters},
- year = 2022,
- volume = 7,
- number = 3,
- pages = {7802--7809},
- month = jun,
- doi = {10.1109/LRA.2022.3185387},
- url = {https://www.merl.com/publications/TR2022-085}
- }
- , "Calibrating building simulation models using multi-source datasets and meta-learned Bayesian optimization", Energy and Buildings, DOI: 10.1016/j.enbuild.2022.112278, Vol. 270, pp. 112278, September 2022.
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Videos
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[MERL Seminar Series Spring 2022] Hybrid robotics and implicit learning
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Toshiaki Koike-Akino Gives Seminar Talk at IEEE Boston Photonics
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[MERL Seminar Series Spring 2022] RLMPC: An Ideal Combination of Formal Optimal Control and Reinforcement Learning?
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[MERL Seminar Series Spring 2022] Self-Supervised Scene Representation Learning
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[MERL Seminar Series Spring 2022] Learning Speech Representations with Multimodal Self-Supervision
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[MERL Seminar Series Spring 2022] Extreme optics design as a large-scale optimization problem
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HealthCam: A system for non-contact monitoring of vital signs
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[MERL Seminar Series 2021] Harnessing machine learning to build better Earth system models for climate projection
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[MERL Seminar Series 2021] Deep probabilistic regression
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[MERL Seminar Series 2021] Learning to See by Moving: Self-supervising 3D scene representations for perception, control, and visual reasoning
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Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
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Application of Deep Learning for Nanophotonic Device Design (Invited)
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Towards Human-Level Learning of Complex Physical Puzzles
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Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry
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Scene-Aware Interaction Technology
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Action Detection Using A Deep Recurrent Neural Network
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Obstacle Detection
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Semantic Scene Labeling
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MERL Research on Autonomous Vehicles
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Deep Hierarchical Parsing for Semantic Segmentation
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Global Local Face Upsampling Network
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Software Downloads
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SOurce-free Cross-modal KnowledgE Transfer
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Instance Segmentation GAN
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Audio Visual Scene-Graph Segmentor
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Hierarchical Musical Instrument Separation
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Generating Visual Dynamics from Sound and Context
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Adversarially-Contrastive Optimal Transport
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Online Feature Extractor Network
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MotionNet
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FoldingNet++
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Quasi-Newton Trust Region Policy Optimization
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Landmarks’ Location, Uncertainty, and Visibility Likelihood
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Robust Iterative Data Estimation
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Gradient-based Nikaido-Isoda
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Circular Maze Environment
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Discriminative Subspace Pooling
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Kernel Correlation Network
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Fast Resampling on Point Clouds via Graphs
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FoldingNet
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Deep Category-Aware Semantic Edge Detection
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