R&D職

私の研究は、遷移金属ナノクラスターの構造および機能を解明し、その触媒応用への展開を目指すものです。
ナノクラスターは、触媒活性や光学応答など多様な特性を示し、構成原子数を原子1個単位で制御できることから、理想的な反応場の創製が可能な次世代材料として注目されています。
本研究では、これらの特性発現の仕組みを明らかにし、酸素還元反応や酸化反応などの高効率化に資する設計指針を提示することで、ナノテクノロジーの発展に貢献することを目指しています。

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。

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.

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.

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.

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.

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。