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

    •  NEWS   Zhong-Qiu Wang joins MERL's Speech and Audio Team
      Date: June 22, 2020
      MERL Contact: Zhongqiu Wang
      Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
      Brief
      • We are excited to announce that Dr. Zhong-Qiu Wang, who recently obtained his Ph.D. from The Ohio State University, has joined MERL's Speech and Audio Team as a Visiting Research Scientist. Zhong-Qiu brings strong expertise in microphone array processing, speech enhancement, blind source/speaker separation, and robust automatic speech recognition, for which he has developed some of the most advanced machine learning and deep learning methods.

        Prior to joining MERL, Zhong-Qiu received the B.Eng. degree in 2013 from Harbin Institute of Technology, Harbin, China, and the M.Sc. and Ph.D. degree in 2017 and 2020 from The Ohio State University, Columbus, USA, all in Computer Science. He was a summer research intern at Microsoft Research, Mitsubishi Electric Research Laboratories, and Google AI. He received a Best Student Paper Award at ICASSP 2018 for his work as an intern at MERL, and a Graduate Research Award from OSU Department of Computer Science and Engineering in 2020.
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    •  NEWS   MERL researchers presenting four papers and organizing two workshops at CVPR 2020 conference
      Date: June 14, 2020 - June 19, 2020
      MERL Contacts: Siheng Chen; Anoop Cherian; Michael Jones; Toshiaki Koike-Akino; Tim Marks; Kuan-Chuan Peng; Ye Wang
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
      Brief
      • MERL researchers are presenting four papers (two oral papers and two posters) and organizing two workshops at the IEEE/CVF Computer Vision and Pattern Recognition (CVPR 2020) conference.

        CVPR 2020 Orals with MERL authors:
        1. "Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction," by Maosen Li, Siheng Chen, Yangheng Zhao, Ya Zhang, Yanfeng Wang, Qi Tian
        2. "Collaborative Motion Prediction via Neural Motion Message Passing," by Yue Hu, Siheng Chen, Ya Zhang, Xiao Gu

        CVPR 2020 Posters with MERL authors:
        3. "LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood," by Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Ye Wang, Michael Jones, Anoop Cherian, Toshiaki Koike-Akino, Xiaoming Liu, Chen Feng
        4. "MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird’s Eye View Maps," by Pengxiang Wu, Siheng Chen, Dimitris N. Metaxas

        CVPR 2020 Workshops co-organized by MERL researchers:
        1. Fair, Data-Efficient and Trusted Computer Vision
        2. Deep Declarative Networks.
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  • Research Highlights

  • Internships

    • 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.

    • 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.

    • SP1419: Simulation of Multimodal Sensors

      MERL is seeking a motivated intern to assist in generating simulated multimodal data for machine learning applications. The project involves integrating several existing software components to generate optical and radar data in a variety of sensing scenarios, and executing the simulations under a variety of conditions. The ideal candidate should have experience with C++, Python, and scripting methods. Some knowledge or experience with Blender, computer graphics, and computer vision would be preferred, but is not required. Project duration is flexible in the range of 1-2 months. Intern has the choice of part-time or full-time occupation and may start immediately.


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  • Recent Publications

    •  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), 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,
      • 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}
      • }
    •  Li, M., Chen, S., Zhao, Y., Zhang, Y., Wang, Y., Tia, Q., "Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.
      BibTeX TR2020-073 PDF
      • @inproceedings{Li2020jun,
      • author = {Li, Maosen and Chen, Sihen and Zhao, Yangheng and Zhang, Ya and Wang, Yanfeng and Tia, Qi},
      • title = {Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2020,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2020-073}
      • }
    •  Wang, Y., Koike-Akino, T., "Learning to Modulate for Non-coherent MIMO", IEEE International Conference on Communications (ICC), June 2020.
      BibTeX TR2020-071 PDF Video Presentation
      • @inproceedings{Wang2020jun,
      • author = {Wang, Ye and Koike-Akino, Toshiaki},
      • title = {Learning to Modulate for Non-coherent MIMO},
      • booktitle = {IEEE International Conference on Communications (ICC)},
      • year = 2020,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2020-071}
      • }
    •  Kumar, A., Marks, T., Mou, W., Wang, Y., Cherian, A., Jones, M.J., Liu, X., Koike-Akino, T., Feng, C., "LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.
      BibTeX TR2020-067 PDF
      • @inproceedings{Kumar2020jun,
      • author = {Kumar, Abhinav and Marks, Tim and Mou, Wenxuan and Wang, Ye and Cherian, Anoop and Jones, Michael J. and Liu, Xiaoming and Koike-Akino, Toshiaki and Feng, Chen},
      • title = {LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2020,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2020-067}
      • }
    •  Wu, P., Chen, S., "MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird’s Eye View Maps", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.
      BibTeX TR2020-068 PDF Software
      • @inproceedings{Wu2020jun,
      • author = {Wu, Pengxiang and Chen, Siheng},
      • title = {MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird’s Eye View Maps},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2020,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2020-068}
      • }
    •  Chen, S., Liu, B., Feng, C., Vallespi-Gonzalez, C., Wellington, C., "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}
      • }
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  • Software Downloads