155人の学生が見つかりました

理工学研究科 劉 欣慰

スマートヘルスケアのためのモバイル分散型センサ環境におけるロバストなマルチモーダル行動検出

将来的には、5G技術を活用して現在の通信分野における課題を解決し、日本および外資系の一流企業で研究開発職として活躍したいと考えております。

理工学研究科 楊 銘旋

エッジ無線ネットワークにおける資源配分・ビーム制御のためのメモリスタ強化 MARL

low-latency, energy-efficient, and reliable wireless resource allocation

理工学研究科 汝 志豪

Multimodal Integration for Comprehensive Behavior Recognition

My research aims to develop comprehensive behavior recognition by integrating observable actions with internal physiological states. By leveraging multimodal biosignals such as motion data, photo-reflective sensors, and neural and muscular activity, I seek to improve the interpretability of behavior understanding in naturalistic and interactive environments, particularly in virtual reality. In the long term, I aim to establish myself as a researcher at the intersection of human behavior analysis, physiological computing, and immersive systems, contributing to human-centered AI and interdisciplinary research bridging engineering, cognitive science, and human?computer interaction.

理工学研究科 万 宏

Lightweight and Generalizable Human Activity Recognition Based on Wireless Sensing and Deep Learning

My research develops lightweight and generalizable wireless sensing systems for human activity recognition. I aim to overcome domain shifts and resource limits, enabling robust, privacy-preserving applications in healthcare, smart homes, and intelligent infrastructure. In the future, I plan to pursue an academic or research-oriented career as a university faculty member or research scientist, contributing to both fundamental advances and real-world deployment of human-centric AI systems. I also aspire to mentor young researchers and foster interdisciplinary collaboration to bridge academia and industry.

理工学研究科 石 峰

分散推論

My research focuses on communication and distributed inference, aiming to design efficient and robust AI frameworks for edge–cloud collaborative systems. I plan to advance model partitioning and communication-aware optimization for large-scale deep learning deployment. In the long term, I aspire to become a university researcher, bridging theory and practice while collaborating with industry to promote real-world applications of distributed AI.

理工学研究科 彭 禧

人工知能による超音波熱伝達のための音響メタマテリアルの適応的最適化と性能解析

This research focuses on various phenomena induced by ultrasonic waves propagating through fluids, such as acoustic streaming, cavitation, and turbulence. The approach I employ involves integrating acoustic metamaterials into fluid systems, combined with AI-driven optimization design. By coupling experimental and simulation methods, I conduct in-depth investigations into the evolution and underlying mechanisms of these phenomena after the introduction of acoustic metamaterials. Through this work, I aim to uncover the physical principles governing the “ultrasound–fluid–metamaterial” coupled system, providing both theoretical and practical foundations for predictive modeling and enhanced heat transfer performance.
This research not only holds potential applications in areas such as high-efficiency heat transfer, electronic cooling, green energy, and microfluidics, but also promotes interdisciplinary integration of acoustic metamaterials and artificial intelligence. In the future, upon completing my doctoral studies, I plan to pursue a career as a university faculty member—engaging in cutting-edge research while mentoring the next generation of scientists. Through cross-disciplinary collaboration, I hope to gradually translate fundamental discoveries into intelligent acoustic and thermal management technologies that meet societal needs.

理工学研究科 黄 宥翔

Towards Intelligent and Privacy-Preserving Wireless Sensing

My research focuses on exploring next-generation wireless sensing technologies that enable non-invasive perception through the integration of Wi-Fi, millimeter-wave, and multimodal signals. By combining advanced deep learning with privacy-preserving frameworks, this work aims to advance applications in smart healthcare, elderly care, and intelligent environments. This research not only lays the foundational groundwork for AI-driven sensing technologies but also addresses real-world societal needs, contributing to the development of safer, smarter, and more human-centered technological systems.

理工学研究科 許 耘閣

異種モバイルエッジコンピューティングネットワークにおける通信効率を考慮した連合学習

My current research focuses on implementing secure and efficient federated learning in edge computing environments. Through collaborative training, my research aims to fully exploit the value of distributed edge data, promote deep integration between artificial intelligence and the Internet of Things, and support diverse applications in future intelligent societies across multiple sectors and use cases. In the future, I plan to work as a researcher in universities or technology companies, actively applying theoretical results to practical domains such as industry, transportation, healthcare, and public services, helping to bridge research and deployment.

理工学研究科 唐 懿

The mechanical behaviors of clear aligners during manufacturing to orthodontic treatment

Medical and Engineering Interdisciplinary Industry