Ye Wang

Ye Wang
  • Biography

    Ye was a member of the Information Systems and Sciences Laboratory at Boston University, where he studied information-theoretically secure multiparty computation. His current research interests include information security, biometric authentication, and data privacy.

  • Recent News & Events

    •  NEWS   MERL published four papers in 2020 IEEE Global Communications Conference
      Date: December 7, 2020 - December 11, 2020
      Where: Taipei, Taiwan
      MERL Contacts: Kyeong Jin (K.J.) Kim; Toshiaki Koike-Akino; Philip Orlik; Pu (Perry) Wang; Ye Wang
      Research Areas: Communications, Computational Sensing, Machine Learning, Signal Processing
      Brief
      • MERL researchers have published four papers in 2020 IEEE Global Communications Conference (GlobeComm). This conference is one of the two IEEE Communications Societies flagship conferences dedicated to Communications for Human and Machine Intelligence. Topics of the published papers include, transmit diversity schemes, coding for molecular networks, and location and human activity sensing via WiFi signals.
    •  
    •  NEWS   MERL Scientists Presenting 5 Papers at IEEE International Conference on Communications (ICC) 2020
      Date: June 7, 2020 - June 11, 2020
      Where: Dublin, Ireland
      MERL Contacts: Kyeong Jin (K.J.) Kim; Toshiaki Koike-Akino; Ye Wang
      Research Areas: Communications, Machine Learning, Signal Processing, Digital Video
      Brief
      • Due to COVID-19, MERL Network Intelligence Team scientists remotely presented 5 papers at the IEEE International Conference on Communications (ICC) 2020, that was scheduled to be held in Dublin Ireland from June 7-11, 2020. Topics presented include recent advances in deep learning methods for communications and new access systems. Presentation videos are also found on our YouTube channel. Our developed technologies can facilitate a great advancement in broadband virtual conferencing which is required in post-COVID-19 society.

        IEEE ICC is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 2,900 scientific researchers submit proposals for program sessions to be held at the annual conference. The high-quality proposals are selected for the conference program, which includes technical papers, tutorials, workshops and industry sessions designed specifically to advance technologies, systems and infrastructure that are continuing to reshape the world and provide all users with access to an unprecedented spectrum of high-speed, seamless and cost-effective global telecommunications services.
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  • Internships with Ye

    • SP1537: Machine Learning for Wireless Communications

      MERL is seeking an intern to work on machine learning for wireless communication systems. The ideal candidate would be an experienced PhD student or post-graduate researcher working in wireless communications with a focus on machine learning methods. The candidate should have a detailed knowledge of wireless communications, with some experience in machine learning, MIMO, and/or channel equalization preferred. Strong programming skills in Python and machine learning frameworks are essential. The expected duration of the internship is 3-6 months with flexible start date and length. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    See All Internships at MERL
  • MERL Publications

    •  Kojima, K., TaherSima, M., Koike-Akino, T., Jha, D., Tang, Y., Wang, Y., Parsons, K., "Deep Neural Networks for Inverse Design of Nanophotonic Devices", IEEE Journal of Lightwave Technology, January 2021.
      BibTeX TR2021-001 PDF
      • @article{Kojima2021jan,
      • author = {Kojima, Keisuke and TaherSima, Mohammad and Koike-Akino, Toshiaki and Jha, Devesh and Tang, Yingheng and Wang, Ye and Parsons, Kieran},
      • title = {Deep Neural Networks for Inverse Design of Nanophotonic Devices},
      • journal = {IEEE Journal of Lightwave Technology},
      • year = 2021,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2021-001}
      • }
    •  Matsumine, T., Koike-Akino, T., Wang, Y., "Polar Coding with Chemical Reaction Networks for Molecular Communications", IEEE Global Communications Conference (GLOBECOM), December 2020.
      BibTeX TR2020-160 PDF Video
      • @inproceedings{Matsumine2020dec,
      • author = {Matsumine, Toshiki and Koike-Akino, Toshiaki and Wang, Ye},
      • title = {Polar Coding with Chemical Reaction Networks for Molecular Communications},
      • booktitle = {IEEE Global Communications Conference (GLOBECOM)},
      • year = 2020,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2020-160}
      • }
    •  Yu, J., Wang, P., Koike-Akino, T., Wang, Y., Orlik, P.V., "Human Pose and Seat Occupancy Classification with Commercial MMWave WiFi", IEEE Global Communications Conference (GLOBECOM), December 2020.
      BibTeX TR2020-158 PDF
      • @inproceedings{Yu2020dec,
      • author = {Yu, Jianyuan and Wang, Pu and Koike-Akino, Toshiaki and Wang, Ye and Orlik, Philip V.},
      • title = {Human Pose and Seat Occupancy Classification with Commercial MMWave WiFi},
      • booktitle = {IEEE Global Communications Conference (GLOBECOM)},
      • year = 2020,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2020-158}
      • }
    •  Talreja, V., Koike-Akino, T., Wang, Y., Millar, D.S., Kojima, K., Parsons, K., "End-to-End Deep Learning for Phase Noise-Robust Multi-Dimensional Geometric Shaping", European Conference on Optical Communication (ECOC), November 2020.
      BibTeX TR2020-155 PDF Video
      • @inproceedings{Talreja2020nov,
      • author = {Talreja, Veeru and Koike-Akino, Toshiaki and Wang, Ye and Millar, David S. and Kojima, Keisuke and Parsons, Kieran},
      • title = {End-to-End Deep Learning for Phase Noise-Robust Multi-Dimensional Geometric Shaping},
      • booktitle = {European Conference on Optical Communication (ECOC)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-155}
      • }
    •  Tang, Y., Kojima, K., Koike-Akino, T., Wang, Y., Wu, P., TaherSima, M., Jha, D., Parsons, K., Qi, M., "Generative Deep Learning Model for Inverse Design of Integrated Nanophotonic Devices", Lasers and Photonics Reviews, DOI: 10.1002/lpor.202000287, Vol. 2020, pp. 2000287, October 2020.
      BibTeX TR2020-135 PDF
      • @article{Tang2020oct,
      • author = {Tang, Yingheng and Kojima, Keisuke and Koike-Akino, Toshiaki and Wang, Ye and Wu, Pengxiang and TaherSima, Mohammad and Jha, Devesh and Parsons, Kieran and Qi, Minghao},
      • title = {Generative Deep Learning Model for Inverse Design of Integrated Nanophotonic Devices},
      • journal = {Lasers and Photonics Reviews},
      • year = 2020,
      • volume = 2020,
      • pages = 2000287,
      • month = oct,
      • doi = {10.1002/lpor.202000287},
      • url = {https://www.merl.com/publications/TR2020-135}
      • }
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  • Software Downloads

  • Videos

  • MERL Issued Patents

    • Title: "Method and Systems using Privacy-Preserving Analytics for Aggregate Data"
      Inventors: Wang, Ye; Raval, Nisarg Jagdishbhai; Ishwar, Prakash
      Patent No.: 10,452,865
      Issue Date: Oct 22, 2019
    • Title: "Irregular Polar Code Encoding"
      Inventors: Koike-Akino, Toshiaki; Wang, Ye; Draper, Stark C.
      Patent No.: 10,313,056
      Issue Date: Jun 4, 2019
    • Title: "Soft-Output Decoding of Codewords Encoded with Polar Code"
      Inventors: Wang, Ye; Koike-Akino, Toshiaki; Draper, Stark C.
      Patent No.: 10,312,946
      Issue Date: Jun 4, 2019
    • Title: "Method and Systems using Privacy-Preserving Analytics for Aggregate Data"
      Inventors: Wang, Ye; Hattori, Mitsuhiro; Hirano, Takato; Shimizu, Rina; Matsuda, Nori
      Patent No.: 10,216,959
      Issue Date: Feb 26, 2019
    • Title: "Privacy Preserving Statistical Analysis on Distributed Databases"
      Inventors: Wang, Ye; Lin, Bing-Rong; Rane, Shantanu D.
      Patent No.: 10,146,958
      Issue Date: Dec 4, 2018
    • Title: "Method and System for Determining Hidden States of a Machine using Privacy-Preserving Distributed Data Analytics and a Semi-trusted Server and a Third-Party"
      Inventors: Wang, Ye
      Patent No.: 9,471,810
      Issue Date: Oct 18, 2016
    • Title: "Method for Determining Hidden States of Systems using Privacy-Preserving Distributed Data Analytics"
      Inventors: Wang, Ye; Rane, Shantanu D.; Xie, Qian
      Patent No.: 9,246,978
      Issue Date: Jan 26, 2016
    • Title: "Privacy Preserving Statistical Analysis for Distributed Databases"
      Inventors: Rane, Shantanu D.; Lin, Bing-Rong; Wang, Ye
      Patent No.: 8,893,292
      Issue Date: Nov 18, 2014
    • Title: "Secure Multi-Party Computation of Normalized Sum-Type Functions"
      Inventors: Rane, Shantanu D.; Sun, Wei; Wang, Ye
      Patent No.: 8,473,537
      Issue Date: Jun 25, 2013
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