ML-Based Processing of Sub-THz Sensing Data for Health Monitoring

Hello! I am Qianzhen Yang from China.

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I completed my Master’s degree in Electrical Engineering and Information Technology at the University of Stuttgart from 2021 to 2025, where my research focused on combining deep learning with wireless communications to reduce receiver-side complexity while maintaining or improving communication accuracy.

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During my studies, I gained hands-on experience in signal processing and machine learning workflows, including experiment design and evaluation.

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As part of the TeraWireless project, I am currently a PhD candidate at Nokia Bell-Labs in France. My research focuses on ML-based processing of sub-THz sensing data, leveraging data-driven signal representations and learning-based feature extraction to enable robust non-contact sensing in realistic environments. I will also develop and evaluate ML pipelines and experimental workflows to support the project goals.

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Currently, I am working on ML-driven processing of FMCW-based sub-THz sensing data, investigating how to reliably extract informative sensing signatures under practical conditions, particularly multi-target settings. This research aims to improve the robustness and scalability of sub-THz sensing for next-generation wireless applications.

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Being part of the TeraWireless project provides a unique opportunity to combine my background in signal processing and machine learning with cutting-edge sub-THz research, while contributing to intelligent sensing capabilities for future wireless systems.