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

    •  TALK   [MERL Seminar Series 2021] Use the [Magnetic] Force for Good: Sustainability Through Magnetic Levitation
      Date & Time: Tuesday, December 7, 2021; 1:00 PM EST
      Speaker: Prof. Eric Severson, University of Wisconsin-Madison
      MERL Host: Bingnan Wang
      Research Area: Electric Systems
      Brief
      • Electric motors pump our water, heat and cool our homes and offices, drive critical medical and surgical equipment, and, increasingly, operate our transportation systems. Approximately 99% of the world’s electric energy is produced by a rotating generator and 45% of that energy is consumed by an electric motor. The efficiency of this technology is vital in enabling our energy sustainability and reducing our carbon footprint. The reliability and lifetime of this technology have severe, and sometimes life-altering, consequences. Today’s motor technology largely relies upon mechanical bearings to support the motor’s shaft. These bearings are the first components to fail, create frictional losses, and rely on lubricants that create contamination challenges and require periodic maintenance. In short, bearings are the Achilles' heel of modern electric motors.

        This seminar will explore the use of actively controlled magnetic forces to levitate the motor shaft, eliminating mechanical bearings and the problems associated with them. The working principles of traditional magnetic levitation technology (active magnetic bearings) will be reviewed and used to explain why this technology has not been successfully applied to the most high-impact motor applications. Research into “bearingless” motors offers a new levitation approach by manipulating the inherent magnetic force capability of all electric motors. While traditional motors are carefully designed to prevent shaft forces, the bearingless motor concept controls these forces to make the motor simultaneously function as an active magnetic bearing. The seminar will showcase the potential of bearingless technology to revolutionize motor systems of critical importance for energy and sustainability—from industrial compressors and blowers, such as those found in HVAC systems and wastewater aeration equipment, to power grid flywheel energy storage devices and electric turbochargers in fuel-efficient vehicles.
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    •  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 V. 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.
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  • Awards

    •  AWARD   MERL Ranked 1st Place in Cross-Subject Transfer Learning Task and 4th Place Overall at the NeurIPS2021 BEETL Competition for EEG Transfer Learning.
      Date: November 11, 2021
      Awarded to: Niklas Smedemark-Margulies, Toshiaki Koike-Akino, Ye Wang, Deniz Erdogmus
      MERL Contacts: Toshiaki Koike-Akino; Ye Wang
      Research Areas: Artificial Intelligence, Signal Processing, Human-Computer Interaction
      Brief
      • The MERL Signal Processing group achieved first place in the cross-subject transfer learning task and fourth place overall in the NeurIPS 2021 BEETL AI Challenge for EEG Transfer Learning. The team included Niklas Smedemark-Margulies (intern from Northeastern University), Toshiaki Koike-Akino, Ye Wang, and Prof. Deniz Erdogmus (Northeastern University). The challenge addresses two types of transfer learning tasks for EEG Biosignals: a homogeneous transfer learning task for cross-subject domain adaptation; and a heterogeneous transfer learning task for cross-data domain adaptation. There were 110+ registered teams in this competition, MERL ranked 1st in the homogeneous transfer learning task, 7th place in the heterogeneous transfer learning task, and 4th place for the combined overall score. For the homogeneous transfer learning task, MERL developed a new pre-shot learning framework based on feature disentanglement techniques for robustness against inter-subject variation to enable calibration-free brain-computer interfaces (BCI). MERL is invited to present our pre-shot learning technique at the NeurIPS 2021 workshop.
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  • Research Highlights

  • Internships with Ye

    • SP1734: Robust Machine Learning

      MERL is seeking a highly motivated and qualified intern to work on robust machine learning techniques. The intern will collaborate with MERL researchers on developing novel approaches to address the problem of adversarial examples. The ideal candidate would have research experience in robust machine learning methods and defenses against adversarial examples. A mature understanding of modern machine learning methods, proficiency with Python, and familiarity with deep learning frameworks are expected. Proficiency with other programming languages and software development experience is a plus. Candidates at or beyond the middle of their Ph.D. program are encouraged to apply.

    See All Internships at MERL
  • MERL Publications

    •  Smedemark-Margulies, N., Wang, Y., Koike-Akino, T., Erdogmus, D., "AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data", arXiv, December 2021.
      BibTeX arXiv
      • @article{Smedemark-Margulies2021dec,
      • author = {Smedemark-Margulies, Niklas and Wang, Ye and Koike-Akino, Toshiaki and Erdogmus, Deniz},
      • title = {AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data},
      • journal = {arXiv},
      • year = 2021,
      • month = dec,
      • url = {https://arxiv.org/abs/2112.09796}
      • }
    •  Kojima, K., Tang, Y., Wang, Y., Koike-Akino, T., "Machine Learning for design and optimization of photonic devices" in Machine Learning for Future Fiber Optic Communication Systems (Elsevier Book), November 2021.
      BibTeX TR2021-142 PDF
      • @incollection{Kojima2021nov,
      • author = {Kojima, Keisuke and Tang, Yingheng and Wang, Ye and Koike-Akino, Toshiaki},
      • title = {Machine Learning for design and optimization of photonic devices},
      • booktitle = {Machine Learning for Future Fiber Optic Communication Systems (Elsevier Book)},
      • year = 2021,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2021-142}
      • }
    •  Yu, J., Pu, W., Koike-Akino, T., Wang, Y., Orlik, P.V., "Multi-Band Wi-Fi Sensing with Granularity Matching", arXiv, November 2021.
      BibTeX arXiv
      • @article{Yu2021nov,
      • author = {Yu, Jianyuan and Pu, Wang and Koike-Akino, Toshiaki and Wang, Ye and Orlik, Philip V.},
      • title = {Multi-Band Wi-Fi Sensing with Granularity Matching},
      • journal = {arXiv},
      • year = 2021,
      • month = nov,
      • url = {http://arxiv.org/abs/2112.14006}
      • }
    •  Demir, A., Koike-Akino, T., Wang, Y., Erdogmus, D., Haruna, M., "EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals", International IEEE EMBS Conference on Neural Engineering, DOI: 10.1109/​EMBC46164.2021.9630194, October 2021.
      BibTeX TR2021-136 PDF Video Presentation
      • @inproceedings{Demir2021oct,
      • author = {Demir, Andac and Koike-Akino, Toshiaki and Wang, Ye and Erdogmus, Deniz and Haruna, Masaki},
      • title = {EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals},
      • booktitle = {International IEEE EMBS Conference on Neural Engineering},
      • year = 2021,
      • month = oct,
      • publisher = {IEEE},
      • doi = {10.1109/EMBC46164.2021.9630194},
      • issn = {2694-0604},
      • isbn = {978-1-7281-1179-7},
      • url = {https://www.merl.com/publications/TR2021-136}
      • }
    •  Medin, S., Egger, B., Cherian, A., Wang, Y., Tenanbaum, J., Liu, X., Marks, T.K., "MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation", arXiv, October 2021.
      BibTeX
      • @article{Medin2021oct,
      • author = {Medin, Safa and Egger, Bernhard and Cherian, Anoop and Wang, Ye and Tenanbaum, Joshua and Liu, Xiaoming and Marks, Tim K.},
      • title = {MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation},
      • journal = {arXiv},
      • year = 2021,
      • month = oct
      • }
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  • Software Downloads

  • Videos

  • MERL Issued Patents

    • Title: "DATA-DRIVEN PRIVACY-PRESERVING COMMUNICATION"
      Inventors: Wang, Ye; Ishwar, Prakash; Tripathy, Ardhendu S
      Patent No.: 11,132,453
      Issue Date: Sep 28, 2021
    • Title: "Irregular Polar Code Encoding"
      Inventors: Koike-Akino, Toshiaki; Wang, Ye; Draper, Stark C.
      Patent No.: 10,862,621
      Issue Date: Dec 8, 2020
    • 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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