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.

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.

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。

私は貝殻の構造から学んだ知見をもとに、材料開発に取り組んでいます。バイオミネラルは、身近な元素から環境に優しい条件で合成され、優れた機能を示します。その模倣は、将来のカーボンニュートラル社会に向けた材料開発に大きく貢献し得るものです。しかし現状では、バイオミネラルの解析が中心であり、模倣材料の開発例はまだごく限られています。
そこで私は、生物から学ぶという視点を活かし、社会課題の解決につながる新たな材料開発を進めていきたいと考えています。将来は国際的に通用する研究者となり、社会に役立つ材料に関わる研究職に就くことを目指しています。