Ye Wang
- Phone: 617-621-7521
- Email:
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Position:
Research / Technical Staff
Senior Principal Research Scientist -
Education:
Ph.D., Boston University, 2011 -
Research Areas:
External Links:
Ye's Quick Links
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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.
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Recent News & Events
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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 WangAbstract
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 InteractionBrief- 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.
- 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.
See All News & Events for Ye -
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Awards
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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 ProcessingBrief- 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.
- 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.
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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 InteractionBrief- 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
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MERL Publications
- , "Joint Software-Hardware Design for Green AI", International Midwest Symposium on Circuits and Systems (MWSCAS), August 2023.BibTeX TR2023-096 PDF
- @inproceedings{Ahmed2023aug,
- author = {Ahmed, Md Rubel and Koike-Akino, Toshiaki and Parsons, Kieran and Wang, Ye},
- title = {Joint Software-Hardware Design for Green AI},
- booktitle = {International Midwest Symposium on Circuits and Systems (MWSCAS)},
- year = 2023,
- month = aug,
- url = {https://www.merl.com/publications/TR2023-096}
- }
- , "AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs", International Midwest Symposium on Circuits and Systems (MWSCAS), August 2023.BibTeX TR2023-097 PDF
- @inproceedings{Ahmed2023aug2,
- author = {Ahmed, Md Rubel and Koike-Akino, Toshiaki and Parsons, Kieran and Wang, Ye},
- title = {AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs},
- booktitle = {International Midwest Symposium on Circuits and Systems (MWSCAS)},
- year = 2023,
- month = aug,
- url = {https://www.merl.com/publications/TR2023-097}
- }
- , "Synthesizing Building Operation Data with Generative Models: VAEs, GANs, or Something In Between?", ACM e-Energy Conference, DOI: 10.1145/3599733.3600260, June 2023.BibTeX TR2023-072 PDF
- @inproceedings{Salatiello2023jun,
- author = {Salatiello, Alessandro and Wang, Ye and Wichern, Gordon and Koike-Akino, Toshiaki and Yoshihiro, Ohta and Kaneko, Yosuke and Laughman, Christopher R. and Chakrabarty, Ankush},
- title = {Synthesizing Building Operation Data with Generative Models: VAEs, GANs, or Something In Between?},
- booktitle = {ACM e-Energy Conference},
- year = 2023,
- month = jun,
- doi = {10.1145/3599733.3600260},
- url = {https://www.merl.com/publications/TR2023-072}
- }
- , "DeepEAD: Explainable Anomaly Detection from System Logs", IEEE International Conference on Communications (ICC), May 2023.BibTeX TR2023-050 PDF
- @inproceedings{Wang2023may,
- author = {Wang, Xinda and Kim, Kyeong Jin and Wang, Ye and Koike-Akino, Toshiaki and Parsons, Kieran},
- title = {DeepEAD: Explainable Anomaly Detection from System Logs},
- booktitle = {IEEE International Conference on Communications (ICC)},
- year = 2023,
- month = may,
- url = {https://www.merl.com/publications/TR2023-050}
- }
- , "Tandem Neural Networks for Electric Machine Inverse Design", IEEE International Electric Machines and Drives Conference (IEMDC), May 2023.BibTeX TR2023-040 PDF
- @inproceedings{Xu2023may2,
- author = {Xu, Yihao and Wang, Bingnan and Sakamoto, Yusuke and Yamamoto, Tatsuya and Nishimura, Yuki and Koike-Akino, Toshiaki and Wang, Ye},
- title = {Tandem Neural Networks for Electric Machine Inverse Design},
- booktitle = {IEEE International Electric Machines and Drives Conference (IEMDC)},
- year = 2023,
- month = may,
- url = {https://www.merl.com/publications/TR2023-040}
- }
- , "Joint Software-Hardware Design for Green AI", International Midwest Symposium on Circuits and Systems (MWSCAS), August 2023.
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Downloads
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Videos
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MERL Issued Patents
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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
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Title: "System and Method for Manipulating Two-Dimensional (2D) Images of Three-Dimensional (3D) Objects"
