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.

  • Researchers

  • Awards

    •  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 & Audio
      Brief
      • 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 Learning
      Brief
      • 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, Robotics
      Brief
      • 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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  • Research Highlights

  • Internships

    • 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.

    • 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.

    • SP1460: Advanced Vehicular Technologies

      MERL is seeking a highly motivated, qualified intern to collaborate with the Signal Processing group and the Control for Autonomy team in developing technologies for Connected Automated Vehicles. The ideal candidate is expected to be involved in research on collaborative learning between infrastructure and vehicles. The candidate is expected to develop learning-based technologies to achieve vehicle coordination, estimation and GNSS-based localization using data and computation sharing between vehicle and infrastructure. The candidates should have knowledge of machine learning, connected vehicles and V2X communications. Knowledge of one or more traffic and/or multi-vehicle simulators (SUMO, Vissim, etc.) and GNSS is a plus. Candidates in their junior or senior years of a Ph.D. program are encouraged to apply. The expected duration of the internship is 3-6 months, with start date in September/October 2020.


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  • Recent Publications

    •  Han, M., Ozdenizci, O., Wang, Y., Koike-Akino, T., Erdogmus, D., "Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction", IEEE Signal Processing Letters, DOI: 10.1109/LSP.2020.3020215, Vol. 27, pp. 1565-1569, September 2020.
      BibTeX TR2020-128 PDF
      • @article{Han2020sep,
      • author = {Han, Mo and Ozdenizci, Ozan and Wang, Ye and Koike-Akino, Toshiaki and Erdogmus, Deniz},
      • title = {Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction},
      • journal = {IEEE Signal Processing Letters},
      • year = 2020,
      • volume = 27,
      • pages = {1565--1569},
      • month = sep,
      • doi = {10.1109/LSP.2020.3020215},
      • issn = {1558-2361},
      • url = {https://www.merl.com/publications/TR2020-128}
      • }
    •  Pishdadian, F., Wichern, G., Le Roux, J., "Finding Strength in Weakness: Learning to Separate Sounds with Weak Supervision", IEEE/ACM Transactions on Audio, Speech, and Language Processing, September 2020.
      BibTeX TR2020-126 PDF
      • @article{Pishdadian2020sep,
      • author = {Pishdadian, Fatemeh and Wichern, Gordon and Le Roux, Jonathan},
      • title = {Finding Strength in Weakness: Learning to Separate Sounds with Weak Supervision},
      • journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
      • year = 2020,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2020-126}
      • }
    •  Cherian, A., Chatterjee, M., Ahuja, N., "Sound2Sight: Generating Visual Dynamics from Sound and Context", European Conference on Computer Vision (ECCV), August 2020.
      BibTeX TR2020-121 PDF
      • @inproceedings{Cherian2020aug,
      • author = {Cherian, Anoop and Chatterjee, Moitreya and Ahuja, Narendra},
      • title = {Sound2Sight: Generating Visual Dynamics from Sound and Context},
      • booktitle = {European Conference on Computer Vision (ECCV)},
      • year = 2020,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2020-121}
      • }
    •  Han, M., Ozdenizci, O., Wang, Y., Koike-Akino, T., Erdogmus, D., "Disentangled Adversarial Transfer Learning for Physiological Biosignals", International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 2020.
      BibTeX TR2020-109 PDF Video
      • @inproceedings{Han2020jul,
      • author = {Han, Mo and Ozdenizci, Ozan and Wang, Ye and Koike-Akino, Toshiaki and Erdogmus, Deniz},
      • title = {Disentangled Adversarial Transfer Learning for Physiological Biosignals},
      • booktitle = {International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-109}
      • }
    •  Seetharaman, P., Wichern, G., Le Roux, J., Pardo, B., "Bootstrapping Unsupervised Deep Music Separation from Primitive Auditory Grouping Principles", ICML 2020 Workshop on Self-supervision in Audio and Speech, July 2020.
      BibTeX TR2020-111 PDF
      • @inproceedings{Seetharaman2020jul,
      • author = {Seetharaman, Prem and Wichern, Gordon and Le Roux, Jonathan and Pardo, Bryan},
      • title = {Bootstrapping Unsupervised Deep Music Separation from Primitive Auditory Grouping Principles},
      • booktitle = {ICML 2020 Workshop on Self-supervision in Audio and Speech},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-111}
      • }
    •  Cherian, A., Aeron, S., "Representation Learning via Adversarially-Contrastive Optimal Transport", International Conference on Machine Learning (ICML), July 2020.
      BibTeX TR2020-093 PDF
      • @inproceedings{Cherian2020jul,
      • author = {Cherian, Anoop and Aeron, Shuchin},
      • title = {Representation Learning via Adversarially-Contrastive Optimal Transport},
      • booktitle = {International Conference on Machine Learning (ICML)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-093}
      • }
    •  Koike-Akino, T., Wang, Y., "Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction", IEEE International Symposium on Information Theory (ISIT), DOI: 10.1109/ISIT44484.2020.9174523, June 2020.
      BibTeX TR2020-075 PDF Video Presentation
      • @inproceedings{Koike-Akino2020jun,
      • author = {Koike-Akino, Toshiaki and Wang, Ye},
      • title = {Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction},
      • booktitle = {IEEE International Symposium on Information Theory (ISIT)},
      • year = 2020,
      • month = jun,
      • publisher = {IEEE},
      • doi = {10.1109/ISIT44484.2020.9174523},
      • issn = {2157-8117},
      • isbn = {978-1-7281-6432-8},
      • url = {https://www.merl.com/publications/TR2020-075}
      • }
    •  Hu, Y., Chen, S., Zhang, Y., Gu, X., "Collaborative Motion Prediction via Neural Motion Message Passing", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.
      BibTeX TR2020-072 PDF
      • @inproceedings{Hu2020jun,
      • author = {Hu, Yue and Chen, Siheng and Zhang, Ya and Gu, Xiao},
      • title = {Collaborative Motion Prediction via Neural Motion Message Passing},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2020,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2020-072}
      • }
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  • Software Downloads