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.

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.

Medical and Engineering Interdisciplinary Industry

Optimizing of Energy-Efficient Federated Learning

AIと教育を掛け合わせた研究を行っております。現在はLLMと洋書を用いた英単語学習法を開発しております。エビングハウスの忘却曲線という人間の記憶の時間の経過と記憶の関係について掛け合わせた曲線に沿って複数回単語を洋書内に登場させることにより、洋書を楽しみながら単語を学習できるものとなっています。このシステムを利用することで洋書を読み切るまでにその中で出会った分からない単語を、洋書のストーリーも楽しみながら覚えることができるようにすることを目標としています。

I hope to contribute to the clinical detection and treatment of cardiovascular diseases worldwide through research activities. In terms of career choice, I am very eager to work in research and technical positions in universities, hospitals, or research institutions at home and abroad.

交通分野におけるアクチュエータ研究，機械産業における設計、解析職を志望

My research focuses on developing multimodal AI systems that can infer user intent from implicit behavioral cues such as gaze, voice, and interaction history. Specifically, I explore segmentation frameworks in 2D and 3D environments that enable AI to naturally interpret human attention and goals. This work has broad applications in fields such as medical image analysis, robotics, and human–computer interaction.
In the future, I aim to develop technologies that foster intuitive and collaborative interaction between humans and AI, enhancing both safety and productivity in society. During my doctoral studies, I seek to strengthen my international research presence and translate my findings into real-world applications, pursuing a career as a researcher contributing to the advancement of science and technology.

本研究では、データ科学的手法であるマテリアルズ・インフォマティクスを活用し、広大な探索空間の中から高エネルギー密度な有機正極を発見する。膨大な実験数を減らすため効率的な探索手法を立案し、抽出化合物の電極性能の向上により電池正極としての応用を目指す。博士課程修了後は、実験とデータ科学の両面から新材料設計を推進する研究者を目指している。