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 2023] Dr. Rupert Way presents talk titled Empirically Grounded Technology Forecasts and the Energy Transition
      Date & Time: Tuesday, January 31, 2023; 11:00 AM
      Speaker: Rupert way, University of Oxford
      MERL Host: Ye Wang
      Abstract
      • Rapidly decarbonising the global energy system is critical for addressing climate change, but concerns about costs have been a barrier to implementation. Historically, most energy-economy models have overestimated the future costs of renewable energy technologies and underestimated their deployment, thereby overestimating total energy transition costs. These issues have driven calls for alternative approaches and more reliable technology forecasting methods. We use an approach based on probabilistic cost forecasting methods to estimate future energy system costs in a variety of scenarios. Our findings suggest that, compared to continuing with a fossil fuel-based system, a rapid green energy transition will likely result in net savings of many trillions of dollars - even without accounting for climate damages or co-benefits of climate policy.
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    •  NEWS    MERL's Quantum Machine Learning Technology Featured in Mitsubishi Electric Corporation Press Release
      Date: December 2, 2022
      MERL Contacts: Toshiaki Koike-Akino; Kieran Parsons; Pu (Perry) Wang; Ye Wang
      Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Signal Processing, Human-Computer Interaction
      Brief
      • Mitsubishi Electric Corporation announced its development of a quantum artificial intelligence (AI) technology that automatically optimizes inference models to downsize the scale of computation with quantum neural networks. The new quantum AI technology can be integrated with classical machine learning frameworks for diverse solutions.

        Mitsubishi Electric has confirmed that the technology can be incorporated in the world's first applications for terahertz (THz) imaging, Wi-Fi indoor monitoring, compressed sensing, and brain-computer interfaces. The technology is based on recent research by MERL's Connectivity & Information Processing team and Computational Sensing team.

        Mitsubishi Electric's new quantum machine learning (QML) technology realizes compact inference models by fully exploiting the enormous capacity of quantum computers to express exponentially larger-state space with the number of quantum bits (qubits). In a hybrid combination of both quantum and classical AI, the technology can compensate for limitations of classical AI to achieve superior performance while significantly downsizing the scale of AI models, even when using limited data.
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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

    • CI1946: Robust, Private, and Efficient Machine Learning

      MERL is seeking highly motivated and qualified interns to work on fundamental machine learning techniques for robustness, privacy, and efficiency. The ideal candidates would have significant research experience in one or more of the following topics: robust machine learning methods, defenses against adversarial examples, privacy issues in machine learning, membership inference attacks, federated/distributed learning, and/or efficient/Green AI. 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. Multiple positions are available throughout 2023 (Spring/Summer of course, but also as early as January), with expected durations of 3-6 months and flexible start dates.

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  • MERL Publications

    •  Kojima, K., Koike-Akino, T., Wang, Y., Jung Minwoo, , Brand, M.E., "Inverse design of two-dimensional freeform metagrating using an adversarial conditional variational autoencoder", SPIE Photonics West, January 2023.
      BibTeX TR2023-004 PDF
      • @inproceedings{Kojima2023jan,
      • author = {Kojima, Keisuke and Koike-Akino, Toshiaki and Wang, Ye and Jung Minwoo and Brand, Matthew E.},
      • title = {Inverse design of two-dimensional freeform metagrating using an adversarial conditional variational autoencoder},
      • booktitle = {SPIE Photonics West},
      • year = 2023,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2023-004}
      • }
    •  Singla, V., Aeron, S., Koike-Akino, T., Parsons, K., Brand, M.E., Wang, Y., "Learning with noisy labels using low-dimensional model trajectory", NeurIPS 2022 Workshop on Distribution Shifts (DistShift), December 2022.
      BibTeX TR2022-156 PDF
      • @inproceedings{Singla2022dec,
      • author = {Singla, Vasu and Aeron, Shuchin and Koike-Akino, Toshiaki and Parsons, Kieran and Brand, Matthew E. and Wang, Ye},
      • title = {Learning with noisy labels using low-dimensional model trajectory},
      • booktitle = {NeurIPS 2022 Workshop on Distribution Shifts (DistShift)},
      • year = 2022,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2022-156}
      • }
    •  Yu, X., Smedemark-Margulies, N., Aeron, S., Koike-Akino, T., Moulin, P., Brand, M.E., Parsons, K., Wang, Y., "Improving Adversarial Robustness by Learning Shared Information", Pattern Recognition, DOI: 10.1016/​j.patcog.2022.109054, Vol. 134, pp. 109054, November 2022.
      BibTeX TR2022-141 PDF
      • @article{Yu2022nov,
      • author = {Yu, Xi and Smedemark-Margulies, Niklas and Aeron, Shuchin and Koike-Akino, Toshiaki and Moulin, Pierre and Brand, Matthew E. and Parsons, Kieran and Wang, Ye},
      • title = {Improving Adversarial Robustness by Learning Shared Information},
      • journal = {Pattern Recognition},
      • year = 2022,
      • volume = 134,
      • pages = 109054,
      • month = nov,
      • doi = {10.1016/j.patcog.2022.109054},
      • issn = {0031-3203},
      • url = {https://www.merl.com/publications/TR2022-141}
      • }
    •  Koike-Akino, T., Wang, Y., "quEEGNet: Quantum AI for Biosignal Processing", IEEE Conference on Biomedical and Health Informatics (BHI), September 2022.
      BibTeX TR2022-121 PDF Video Presentation
      • @inproceedings{Koike-Akino2022sep,
      • author = {Koike-Akino, Toshiaki and Wang, Ye},
      • title = {quEEGNet: Quantum AI for Biosignal Processing},
      • booktitle = {IEEE Conference on Biomedical and Health Informatics (BHI)},
      • year = 2022,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2022-121}
      • }
    •  Liu, Bryan, Koike-Akino, Toshiaki, Wang, Ye, Kim, Kyeong Jin, Brand, Matthew E., Aeron, Shuchin, Parsons, Kieran, "Data Privacy and Protection on Deep Leakage from Gradients by Layer-Wise Pruning", Tech. Rep. TR2022-081, Mitsubishi Electric Research Laboratories, Cambridge, MA, August 2022.
      BibTeX TR2022-081 PDF
      • @techreport{MERL_TR2022-081,
      • author = {Liu, Bryan; Koike-Akino, Toshiaki; Wang, Ye; Kim, Kyeong Jin; Brand, Matthew E.; Aeron, Shuchin; Parsons, Kieran},
      • title = {Data Privacy and Protection on Deep Leakage from Gradients by Layer-Wise Pruning},
      • institution = {MERL - Mitsubishi Electric Research Laboratories},
      • address = {Cambridge, MA 02139},
      • number = {TR2022-081},
      • month = aug,
      • year = 2022,
      • url = {https://www.merl.com/publications/TR2022-081/}
      • }
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  • Software Downloads

  • Videos

  • MERL Issued Patents

    • Title: "Protograph Quasi-Cyclic Polar Codes and Related Low-Density Generator Matrix Family"
      Inventors: Koike-Akino, Toshiaki; Wang, Ye
      Patent No.: 11,463,114
      Issue Date: Oct 4, 2022
    • Title: "Battery Diagnostic System for Estimating Remaining useful Life (RUL) of a Battery"
      Inventors: Pajovic, Milutin; Wang, Ye; Gorrachategui, Ivan Sanz
      Patent No.: 11,346,891
      Issue Date: May 31, 2022
    • Title: "Generative Model for Inverse Design of Materials, Devices, and Structures"
      Inventors: Kojima, Keisuke; Tang, Yingheng; Koike-Akino, Toshiaki; Wang, Ye
      Patent No.: 11,251,896
      Issue Date: Feb 15, 2022
    • 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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