Hello! I am Yuncheng Yuan from Guiyang, China.

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I completed my master’s degree in Artificial Intelligence and Engineering Systems at Eindhoven University of Technology, Netherlands from 2022 to 2024, where my research primarily focused on machine learning, deep learning and signal processing, specifically improving forward error-correction codes through neural network and Transformer-based architecture.

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After graduating, I continued at the same institution for 7 months as an independent research assistant, where I worked on refining machine learning-based decoders for publication and exploring advanced robotics control algorithms.

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During my studies and research experience, I published papers in international conferences such as the 14th International ITG Conference , developed panoptic segmentation models using High-Performance Computing , and implemented robotic manipulation programs for industrial logistics. These experiences helped me develop a robust hands-on understanding of AI/machine learning algorithms, signal processing, and collaborative scientific communication.

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As part of the TeraWireless project, I am currently a PhD candidate at the University of Piraeus, Greece. My research focuses on Rule-based ML fast-channel acquisition and prediction for U-MIMO THz systems, including embedding random shape models and spherical propagation models into rule-based machine learning frameworks to study efficient channel estimation in the presence of blocking objects. I will also leverage physics-based ML solutions to create robust and explainable-by-design implementations, while spending 18 months at Nokia Paris Bell Lab to integrate these developed schemes into open-access link-level simulators to support the project goals.

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Currently, I am working on fast-channel acquisition methods, investigating narrow beamwidth THz channels and network deployment models for large-size blockages. This research aims to enable wireless connectivity at extremely high data rates with deterministic performance for future 6G networks.

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Being part of the TeraWireless project provides a unique opportunity to apply my background in machine learning and signal processing to the frontier of Terahertz communications while collaborating internationally with top-tier industrial partners Nokia, while contributing to the theoretical and algorithmic foundations of next-generation wireless systems.