Ye Wang

Ye Wang
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  • 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 2025] Andy Zou presents talk titled Red Teaming AI Agents in-the-wild: Revealing Deployment Vulnerabilities
      Date & Time: Wednesday, March 26, 2025; 1:00 PM
      Speaker: Andy Zou, CMU & Gray Swan AI
      MERL Host: Ye Wang
      Research Areas: Artificial Intelligence, Machine Learning, Information Security
      Abstract
      • This presentation demonstrates how red teaming uncovers critical vulnerabilities in AI agents that challenge assumptions about safe deployment. The talk discusses the risks of integrating AI into real-world applications and recommends practical safeguards to enhance resilience and ensure dependable deployment in high-risk settings.
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    •  NEWS    MERL Papers and Workshops at AAAI 2025
      Date: February 25, 2025 - March 4, 2025
      Where: The Association for the Advancement of Artificial Intelligence (AAAI)
      MERL Contacts: Ankush Chakrabarty; Toshiaki Koike-Akino; Jing Liu; Kuan-Chuan Peng; Diego Romeres; Ye Wang
      Research Areas: Artificial Intelligence, Machine Learning, Optimization
      Brief
      • MERL researchers presented 2 conference papers, 2 workshop papers, and co-organized 1 workshop at the AAAI 2025 conference, which was held in Philadelphia from Feb. 25 to Mar. 4, 2025. AAAI is one of the most prestigious and competitive international conferences in artificial intelligence (AI). Details of MERL contributions are provided below.

        - AAAI Papers in Main Tracks:

        1. "Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage" by M.R.U. Rashid, J. Liu, T. Koike-Akino, Y. Wang, and S. Mehnaz. [Oral Presentation]

        This work proposes a novel unlearning-based model poisoning method that amplifies privacy breaches during fine-tuning. Extensive empirical studies show the proposed method’s efficacy on both membership inference and data extraction attacks. The attack is stealthy enough to bypass detection based defenses, and differential privacy cannot effectively defend against the attacks without significantly impacting model utility.

        Paper: https://www.merl.com/publications/TR2025-017

        2. "User-Preference Meets Pareto-Optimality: Multi-Objective Bayesian Optimization with Local Gradient Search" by J.H.S. Ip, A. Chakrabarty, A. Mesbah, and D. Romeres. [Poster Presentation]

        This paper introduces a sample-efficient multi-objective Bayesian optimization method that integrates user preferences with gradient-based search to find near-Pareto optimal solutions. The proposed method achieves high utility and reduces distance to Pareto-front solutions across both synthetic and real-world problems, underscoring the importance of minimizing gradient uncertainty during gradient-based optimization. Additionally, the study introduces a novel utility function that respects Pareto dominance and effectively captures diverse user preferences.

        Paper: https://www.merl.com/publications/TR2025-018

        - AAAI Workshop Papers:

        1. "Quantum Diffusion Models for Few-Shot Learning" by R. Wang, Y. Wang, J. Liu, and T. Koike-Akino.

        This work presents the quantum diffusion model (QDM) as an approach to overcome the challenges of quantum few-shot learning (QFSL). It introduces three novel algorithms developed from complementary data-driven and algorithmic perspectives to enhance the performance of QFSL tasks. The extensive experiments demonstrate that these algorithms achieve significant performance gains over traditional baselines, underscoring the potential of QDM to advance QFSL by effectively leveraging quantum noise modeling and label guidance.

        Paper: https://www.merl.com/publications/TR2025-025

        2. "Quantum Implicit Neural Compression", by T. Fujihashi and T., Koike-Akino.

        This work introduces a quantum counterpart of implicit neural representation (quINR) which leverages the exponentially rich expressivity of quantum neural networks to improve the classical INR-based signal compression methods. Evaluations using some benchmark datasets show that the proposed quINR-based compression could improve rate-distortion performance in image compression compared with traditional codecs and classic INR-based coding methods.

        Paper: https://www.merl.com/publications/TR2025-024

        - AAAI Workshops Contributed by MERL:

        1. "Scalable and Efficient Artificial Intelligence Systems (SEAS)"

        K.-C. Peng co-organized this workshop, which offers a timely forum for experts to share their perspectives in designing and developing robust computer vision (CV), machine learning (ML), and artificial intelligence (AI) algorithms, and translating them into real-world solutions.

        Workshop link: https://seasworkshop.github.io/aaai25/index.html

        2. "Quantum Computing and Artificial Intelligence"

        T. Koike-Akino served a session chair of Quantum Neural Network in this workshop, which focuses on seeking contributions encompassing theoretical and applied advances in quantum AI, quantum computing (QC) to enhance classical AI, and classical AI to tackle various aspects of QC.

        Workshop link: https://sites.google.com/view/qcai2025/
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  • Awards

    •  AWARD    MERL Wins Awards at NeurIPS LLM Privacy Challenge
      Date: December 15, 2024
      Awarded to: Jing Liu, Ye Wang, Toshiaki Koike-Akino, Tsunato Nakai, Kento Oonishi, Takuya Higashi
      MERL Contacts: Toshiaki Koike-Akino; Jing Liu; Ye Wang
      Research Areas: Artificial Intelligence, Machine Learning, Information Security
      Brief
      • The Mitsubishi Electric Privacy Enhancing Technologies (MEL-PETs) team, consisting of a collaboration of MERL and Mitsubishi Electric researchers, won awards at the NeurIPS 2024 Large Language Model (LLM) Privacy Challenge. In the Blue Team track of the challenge, we won the 3rd Place Award, and in the Red Team track, we won the Special Award for Practical Attack.
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    •  AWARD    MERL’s Paper on Wi-Fi Sensing Earns Top 3% Paper Recognition at ICASSP 2023, Selected as a Best Student Paper Award Finalist
      Date: June 9, 2023
      Awarded to: Cristian J. Vaca-Rubio, Pu Wang, Toshiaki Koike-Akino, Ye Wang, Petros Boufounos and Petar Popovski
      MERL Contacts: Petros T. Boufounos; Toshiaki Koike-Akino; Pu (Perry) Wang; Ye Wang
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Dynamical Systems, Machine Learning, Signal Processing
      Brief
      • A MERL Paper on Wi-Fi sensing was recognized as a Top 3% Paper among all 2709 accepted papers at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). Co-authored by Cristian Vaca-Rubio and Petar Popovski from Aalborg University, Denmark, and MERL researchers Pu Wang, Toshiaki Koike-Akino, Ye Wang, and Petros Boufounos, the paper "MmWave Wi-Fi Trajectory Estimation with Continous-Time Neural Dynamic Learning" was also a Best Student Paper Award finalist.

        Performed during Cristian’s stay at MERL first as a visiting Marie Skłodowska-Curie Fellow and then as a full-time intern in 2022, this work capitalizes on standards-compliant Wi-Fi signals to perform indoor localization and sensing. The paper uses a neural dynamic learning framework to address technical issues such as low sampling rate and irregular sampling intervals.

        ICASSP, a flagship conference of the IEEE Signal Processing Society (SPS), was hosted on the Greek island of Rhodes from June 04 to June 10, 2023. ICASSP 2023 marked the largest ICASSP in history, boasting over 4000 participants and 6128 submitted papers, out of which 2709 were accepted.
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    •  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

    • CI0139: Internship - Trustworthy and General AI

      MERL is seeking passionate and skilled research interns to join our team focused on developing trustworthy, safe, and robust machine learning technologies towards realizing more capable, general agents. This is an exciting opportunity to make an impact on the field of AI safety and generalization, with the aim of publishing at leading AI research venues.

      What We're Looking For:

      • Advanced research experience with generative models related to the topics of AI safety, robustness, trustworthiness, and/or more capable agents.
      • Hands-on skills for large language models (LLM), vision language models (VLM), large multi-modal models (LMM), foundation models (FoMo)
      • Deep understanding of state-of-the-art machine learning methods
      • Proficiency in Python and PyTorch
      • Familiarity with other relevant deep learning frameworks
      • Ph.D. candidates who have completed at least half of their program

      Internship Details:

      • Duration: approximately 3 months
      • Flexible start dates available
      • Objective: publish research results at leading AI research venues

      If you're a highly motivated individual with a passion for tackling AI safety and privacy challenges, we want to hear from you! This internship offers a unique chance to work on meaningful AI research projects, combined with the opportunity to publish and add to your thesis.

    See All Internships at MERL

  • MERL Publications

    •  Chakrabarty, A., Vanfretti, L., Wang, Y., Mineyuki, T., Zhan, S., Tang, W.-T., Paulson, J.A., Deshpande, V.M., Bortoff, S.A., Laughman, C.R., "Time-Series Generative Networks for Synthesizing Realistic Scenarios in Occupant-Centric Building Simulation", Building Simulation, April 2025.
      BibTeX TR2025-043 PDF
      • @article{Chakrabarty2025apr,
      • author = {Chakrabarty, Ankush and Vanfretti, Luigi and Wang, Ye and Mineyuki, Takuma and Zhan, Sicheng and Tang, Wei-Ting and Paulson, Joel A. and Deshpande, Vedang M. and Bortoff, Scott A. and Laughman, Christopher R.},
      • title = {{Time-Series Generative Networks for Synthesizing Realistic Scenarios in Occupant-Centric Building Simulation}},
      • journal = {Building Simulation},
      • year = 2025,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2025-043}
      • }
    •  Smedemark-Margulies, N., Wang, Y., Koike-Akino, T., Liu, J., Parsons, K., Bicer, Y., Erdogmus, D., "Improving Subject Transfer in EEG Classification with Divergence Estimation", Journal of Neural Engineering, DOI: 10.1088/​1741-2552/​ad9777, Vol. 21, No. 6, April 2025.
      BibTeX TR2025-044 PDF
      • @article{Smedemark-Margulies2025apr,
      • author = {Smedemark-Margulies, Niklas and Wang, Ye and Koike-Akino, Toshiaki and Liu, Jing and Parsons, Kieran and Bicer, Yunus and Erdogmus, Deniz},
      • title = {{Improving Subject Transfer in EEG Classification with Divergence Estimation}},
      • journal = {Journal of Neural Engineering},
      • year = 2025,
      • volume = 21,
      • number = 6,
      • month = apr,
      • doi = {10.1088/1741-2552/ad9777},
      • url = {https://www.merl.com/publications/TR2025-044}
      • }
    •  Wang, R., Wang, Y., Liu, J., Koike-Akino, T., "Quantum Diffusion Models for Few-Shot Learning", AAAI Conference on Artificial Intelligence, March 2025.
      BibTeX TR2025-025 PDF
      • @inproceedings{Wang2025mar,
      • author = {Wang, Ruhan and Wang, Ye and Liu, Jing and Koike-Akino, Toshiaki},
      • title = {{Quantum Diffusion Models for Few-Shot Learning}},
      • booktitle = {AAAI Conference on Artificial Intelligence},
      • year = 2025,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2025-025}
      • }
    •  Rashid, M.R.U., Liu, J., Koike-Akino, T., Wang, Y., Mehnaz, S., "Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage", AAAI Conference on Artificial Intelligence, February 2025.
      BibTeX TR2025-017 PDF
      • @inproceedings{Rashid2025feb,
      • author = {Rashid, Md Rafi Ur and Liu, Jing and Koike-Akino, Toshiaki and Wang, Ye and Mehnaz, Shagufta},
      • title = {{Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage}},
      • booktitle = {AAAI Conference on Artificial Intelligence},
      • year = 2025,
      • month = feb,
      • url = {https://www.merl.com/publications/TR2025-017}
      • }
    •  Lewis, A., White, M., Liu, J., Koike-Akino, T., Parsons, K., Wang, Y., "Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in QA Agents", arXiv, February 2025.
      BibTeX arXiv
      • @article{Lewis2025feb,
      • author = {Lewis, Ashley and White, Michael and Liu, Jing and Koike-Akino, Toshiaki and Parsons, Kieran and Wang, Ye},
      • title = {{Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in QA Agents}},
      • journal = {arXiv},
      • year = 2025,
      • month = feb,
      • url = {https://www.arxiv.org/abs/2502.19545}
      • }
    See All MERL Publications for Ye

  • Software & Data Downloads

  • Videos

  • MERL Issued Patents

    • Title: "Generative Model for Inverse Design of Materials, Devices, and Structures"
      Inventors: Kojima, Keisuke; Koike-Akino, Toshiaki; Tang, Yingheng; Wang, Ye
      Patent No.: 12,260,3339
      Issue Date: Mar 25, 2025
    • Title: "Automated Construction of Neural Network Architecture with Bayesian Graph Exploration"
      Inventors: Koike-Akino, Toshiaki; Wang, Ye; Demir, Andac; Erdogmus, Deniz
      Patent No.: 12,061,985
      Issue Date: Aug 13, 2024
    • Title: "Anomaly Detection and Diagnosis in Factory Automation System using Pre-Processed Time-Delay Neural Network with Loss Function Adaptation"
      Inventors: Guo, Jianlin; Liu, Bryan; Koike-Akino, Toshiaki; Wang, Ye; Kim, Kyeong-Jin; Parsons, Kieran; Orlik, Philip V.
      Patent No.: 12,007,760
      Issue Date: Jun 11, 2024
    • Title: "Multi-Band Wi-Fi Fusion for WLAN Sensing"
      Inventors: Wang, Pu; Yu, Jianyuan; Koike-Akino, Toshiaki; Wang, Ye; Orlik, Philip V.
      Patent No.: 11,902,811
      Issue Date: Feb 13, 2024
    • Title: "Apparatus and Method for Anomaly Detection"
      Inventors: Wang, Ye; Kim, Kyeong-Jin; Wang, Xiao
      Patent No.: 11,843,623
      Issue Date: Dec 12, 2023
    • Title: "System and Method for Manipulating Two-Dimensional (2D) Images of Three-Dimensional (3D) Objects"
      Inventors: Marks, Tim; Medin, Safa; Cherian, Anoop; Wang, Ye
      Patent No.: 11,663,798
      Issue Date: May 30, 2023
    • Title: "Non-Uniform Regularization in Artificial Neural Networks for Adaptable Scaling"
      Inventors: Wang, Ye; Koike-Akino, Toshiaki
      Patent No.: 11,651,225
      Issue Date: May 16, 2023
    • 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: Gorrachategui, Ivan Sanz; Pajovic, Milutin; Wang, Ye
      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; Shimizu, Rina; Hirano, Takato; 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; Xie, Qian; Rane, Shantanu D.
      Patent No.: 9,246,978
      Issue Date: Jan 26, 2016
    • Title: "Privacy Preserving Statistical Analysis for Distributed Databases"
      Inventors: Wang, Ye; Lin, Bing-Rong; Rane, Shantanu D.
      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
    See All Patents for MERL