Artificial Intelligence
Making machines smarter for improved safety, efficiency and comfort.
Our AI research encompasses advances in computer vision, speech and audio processing, as well as data analytics. Key research themes include improved perception based on machine learning techniques, learning control policies through model-based reinforcement learning, as well as cognition and reasoning based on learned semantic representations. We apply our work to a broad range of automotive and robotics applications, as well as building and home systems.
Quick Links
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Researchers
Jonathan
Le Roux
Takaaki
Hori
Daniel
Nikovski
Michael
Jones
Tim
Marks
Chiori
Hori
Devesh
Jha
Toshiaki
Koike-Akino
Anoop
Cherian
Ye
Wang
Philip
Orlik
Alan
Sullivan
Gordon
Wichern
Bret
Harsham
Diego
Romeres
Niko
Moritz
Jeroen
van Baar
William
Yerazunis
Keisuke
Kojima
Kieran
Parsons
Arvind
Raghunathan
Petros
Boufounos
Siheng
Chen
Emil
Laftchiev
Saleh
Nabi
Hongbo
Sun
Kyeong Jin
(K.J.)
Kim
Chungwei
Lin
Hassan
Mansour
Yebin
Wang
Mouhacine
Benosman
Matthew
Brand
Radu
Corcodel
Bingnan
Wang
Jinyun
Zhang
Jianlin
Guo
Hiroyuki
Hashimoto
David
Millar
Anthony
Vetro
Siddarth
Jain
Suhas
Lohit
Kuan-Chuan
Peng
Kazuto
Sugimoto
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Awards
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AWARD Best Paper Award at the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) 2019 Date: December 18, 2019
Awarded to: Xuankai Chang, Wangyou Zhang, Yanmin Qian, Jonathan Le Roux, Shinji Watanabe
MERL Contact: Jonathan Le Roux
Research Areas: Artificial Intelligence, Machine Learning, Speech & AudioBrief- MERL researcher Jonathan Le Roux and co-authors Xuankai Chang, Shinji Watanabe (Johns Hopkins University), Wangyou Zhang, and Yanmin Qian (Shanghai Jiao Tong University) won the Best Paper Award at the 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2019), for the paper "MIMO-Speech: End-to-End Multi-Channel Multi-Speaker Speech Recognition". MIMO-Speech is a fully neural end-to-end framework that can transcribe the text of multiple speakers speaking simultaneously from multi-channel input. The system is comprised of a monaural masking network, a multi-source neural beamformer, and a multi-output speech recognition model, which are jointly optimized only via an automatic speech recognition (ASR) criterion. The award was received by lead author Xuankai Chang during the conference, which was held in Sentosa, Singapore from December 14-18, 2019.
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AWARD MERL Researchers win Best Paper Award at ICCV 2019 Workshop on Statistical Deep Learning in Computer Vision Date: October 27, 2019
Awarded to: Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Chen Feng, Xiaoming Liu
MERL Contact: Tim Marks
Research Areas: Artificial Intelligence, Computer Vision, Machine LearningBrief- MERL researcher Tim Marks, former MERL interns Abhinav Kumar and Wenxuan Mou, and MERL consultants Professor Chen Feng (NYU) and Professor Xiaoming Liu (MSU) received the Best Oral Paper Award at the IEEE/CVF International Conference on Computer Vision (ICCV) 2019 Workshop on Statistical Deep Learning in Computer Vision (SDL-CV) held in Seoul, Korea. Their paper, entitled "UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood Loss," describes a method which, given an image of a face, estimates not only the locations of facial landmarks but also the uncertainty of each landmark location estimate.
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AWARD MERL Researcher Devesh Jha Wins the Rudolf Kalman Best Paper Award 2019 Date: October 10, 2019
Awarded to: Devesh Jha, Nurali Virani, Zhenyuan Yuan, Ishana Shekhawat and Asok Ray
MERL Contact: Devesh Jha
Research Areas: Artificial Intelligence, Control, Data Analytics, Machine Learning, RoboticsBrief- MERL researcher Devesh Jha has won the Rudolf Kalman Best Paper Award 2019 for the paper entitled "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models". This paper, published in a Special Commemorative Issue for Rudolf E. Kalman in the ASME JDSMC in March 2018, uses Bayesian filtering for imitation learning in Hidden Mode Hybrid Systems. This award is given annually by the Dynamic Systems and Control Division of ASME to the authors of the best paper published in the ASME Journal of Dynamic Systems Measurement and Control during the preceding year.
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News & Events
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TALK A Prospect in Wireless Connectivity Beyond 5G: Heterogeneity, Learning, Caution, and New Opportunities Date & Time: Thursday, May 7, 2020; 11:00 AM
Speaker: Prof. Petar Popovski, Aalborg University, Denmark
MERL Host: Toshiaki Koike-Akino
Research Areas: Artificial Intelligence, Communications, Machine Learning, Signal Processing, Information SecurityBrief
The wireless landscape evolves towards supporting a large population of connections for humans and machines with very diverse features and requirements. Perhaps the main motivation of 5G wireless systems is its flexibility to support heterogeneous connectivity requirements: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). However, this classification is rather limited and is currently undergoing a revision within the research community. The first part of this talk will discuss how this heterogeneity can be revised and which opportunities it opens with respect to spectrum usage. The second part of the talk will deal with performance guarantees of wireless services and, specifically, ultra-reliable communication and outline the importance of machine learning in that context. The final part of the talk will provide a broader view on the evolution of wireless connectivity, including aspects that are implied by the resistance to the deployment of 5G, but also the new opportunities that can transform the way we build and utilize connected systems.
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NEWS Diego Romeres appointed as an Associate Editor for IROS 2020 Date: February 14, 2020
Where: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Data Analytics, RoboticsBrief- Diego Romeres, a Research Scientist in MERL's Data Analytics group, will be serving as an Associate Editor (AE) for the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020).
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Research Highlights
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Internships
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SP1424: Advanced computational sensing technologies
The Computational Sensing team at MERL is seeking motivated and qualified individuals to develop computational imaging algorithms for a variety of sensing applications. Ideal candidates should be Ph.D. students and have solid background and publication record in any of the following, or related areas: imaging inverse problems, learning for inverse problems, large-scale optimization, blind inverse scattering, radar/lidar/sonar imaging, or wave-based inversion. Experience with experimentally measured data is desirable. Publication of the results produced during our internships is expected. The duration of the internships is anticipated to be 3-6 months. Start date is flexible.
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MD1441: Advanced Phased Array Transceiver
MERL is looking for a highly motivated, and qualified individual to join our internship program of advanced phased array research. The ideal candidate should be a senior Ph.D. student with rich experience in beam forming technologies. Knowledge of wireless communication, transceiver architecture, and digital signal processing, FPGA and/or Matlab programming skills are required. RF circuits knowledge will be a plus. Duration is 3-6 months with a flexible start date.
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CA1400: Autonomous Vehicle Planning and Control
The Control and Dynamical Systems (CD) group at MERL is seeking highly motivated interns at different levels of expertise to conduct research on planning and control for autonomous vehicles. The research domain includes algorithms for path planning, vehicle control, high level decision making, sensor-based navigation, driver-vehicle interaction. PhD students will be considered for algorithm development and analysis, and property proving. Master students will be considered for development and implementation in a scaled robotic test bench for autonomous vehicles. For algorithm development and analysis it is highly desirable to have deep background in one or more among: sampling-based planning methods, particle filtering, model predictive control, reachability methods, formal methods and abstractions of dynamical systems, and experience with their implementation in Matlab/Python/C++. For algorithm implementation, it is required to have working knowledge of Matlab, C++, and ROS, and it is a plus to have background in some of the above mentioned methods. The expected duration of the internship is 3-6 months with flexible start date after April 1st, 2020.
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Recent Publications
- , "3D Point Cloud Processing and Learning for Autonomous Driving", IEEE Signal Processing Magazine, May 2020.BibTeX TR2020-066 PDF
- @article{Chen2020may2,
- author = {Chen, Siheng and Liu, Baoan and Feng, Chen and Vallespi-Gonzalez, Carlos and Wellington, Carl},
- title = {3D Point Cloud Processing and Learning for Autonomous Driving},
- journal = {IEEE Signal Processing Magazine},
- year = 2020,
- month = may,
- url = {https://www.merl.com/publications/TR2020-066}
- }
- , "High-Quality Soft Image Delivery with Deep Image Denoising", IEEE International Conference on Communications (ICC), May 2020.BibTeX TR2020-060 PDF Video
- @inproceedings{Fujihashi2020may,
- author = {Fujihashi, Takuya and Koike-Akino, Toshiaki and Watanabe, Takashi and Orlik, Philip V.},
- title = {High-Quality Soft Image Delivery with Deep Image Denoising},
- booktitle = {IEEE International Conference on Communications (ICC)},
- year = 2020,
- month = may,
- url = {https://www.merl.com/publications/TR2020-060}
- }
- , "Artificial Intelligence-Based Distributed Belief Propagation and Recurrent Neural Network Algorithm for Wide-Area Monitoring Systems", IEEE Network, May 2020.BibTeX TR2020-058 PDF
- @article{Bhamidipati2020may,
- author = {Bhamidipati, Sriramya and Kim, Kyeong Jin and Sun, Hongbo and Orlik, Philip V.},
- title = {Artificial Intelligence-Based Distributed Belief Propagation and Recurrent Neural Network Algorithm for Wide-Area Monitoring Systems},
- journal = {IEEE Network},
- year = 2020,
- month = may,
- url = {https://www.merl.com/publications/TR2020-058}
- }
- , "Learning Invariant Representations from EEG via Adversarial Inference", IEEE Access, DOI: 10.1109/ACCESS.2020.2971600, Vol. 8, pp. 27074-27085, April 2020.BibTeX TR2020-049 PDF
- @article{Ozdenizci2020apr,
- author = {Ozdenizci, Ozan and Wang, Ye and Koike-Akino, Toshiaki and Erdogmus, Deniz},
- title = {Learning Invariant Representations from EEG via Adversarial Inference},
- journal = {IEEE Access},
- year = 2020,
- volume = 8,
- pages = {27074--27085},
- month = apr,
- doi = {10.1109/ACCESS.2020.2971600},
- issn = {2169-3536},
- url = {https://www.merl.com/publications/TR2020-049}
- }
- , "Learning Plug-and-Play Proximal Quasi-Newton Denoisers", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP40776.2020.9054537, April 2020, pp. 8896-8900.BibTeX TR2020-045 PDF Video
- @inproceedings{Al-Shabili2020apr,
- author = {Al-Shabili, Abdullah and Mansour, Hassan and Boufounos, Petros T.},
- title = {Learning Plug-and-Play Proximal Quasi-Newton Denoisers},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2020,
- pages = {8896--8900},
- month = apr,
- publisher = {IEEE},
- doi = {10.1109/ICASSP40776.2020.9054537},
- issn = {2379-190X},
- isbn = {978-1-5090-6631-5},
- url = {https://www.merl.com/publications/TR2020-045}
- }
- , "End-To-End Multi-Speaker Speech Recognition with Transformer", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP40776.2020.9054029, April 2020, pp. 6134-6138.BibTeX TR2020-043 PDF Video
- @inproceedings{Chang2020apr,
- author = {Chang, Xuankai and Zhang, Wangyou and Qian, Yanmin and Le Roux, Jonathan and Watanabe, Shinji},
- title = {End-To-End Multi-Speaker Speech Recognition with Transformer},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2020,
- pages = {6134--6138},
- month = apr,
- publisher = {IEEE},
- doi = {10.1109/ICASSP40776.2020.9054029},
- issn = {2379-190X},
- isbn = {978-1-5090-6631-5},
- url = {https://www.merl.com/publications/TR2020-043}
- }
- , "WHAMR!: Noisy and Reverberant Single-Channel Speech Separation", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP40776.2020.9053327, April 2020, pp. 696-700.BibTeX TR2020-042 PDF Video
- @inproceedings{Maciejewski2020apr,
- author = {Maciejewski, Matthew and Wichern, Gordon and McQuinn, Emmett and Le Roux, Jonathan},
- title = {WHAMR!: Noisy and Reverberant Single-Channel Speech Separation},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2020,
- pages = {696--700},
- month = apr,
- publisher = {IEEE},
- doi = {10.1109/ICASSP40776.2020.9053327},
- issn = {2379-190X},
- isbn = {978-1-5090-6631-5},
- url = {https://www.merl.com/publications/TR2020-042}
- }
- , "Streaming Automatic Speech Recognition With The Transformer Model", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP40776.2020.9054476, April 2020, pp. 6074-6078.BibTeX TR2020-040 PDF Video
- @inproceedings{Moritz2020apr,
- author = {Moritz, Niko and Hori, Takaaki and Le Roux, Jonathan},
- title = {Streaming Automatic Speech Recognition With The Transformer Model},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2020,
- pages = {6074--6078},
- month = apr,
- publisher = {IEEE},
- doi = {10.1109/ICASSP40776.2020.9054476},
- issn = {2379-190X},
- isbn = {978-1-5090-6631-5},
- url = {https://www.merl.com/publications/TR2020-040}
- }
- , "3D Point Cloud Processing and Learning for Autonomous Driving", IEEE Signal Processing Magazine, May 2020.
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