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.

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.

My research focuses on the numerical and experimental study on dental mechanics from manufacturing to orthodontic treatment using polymeric aligners. The core of my methodology lies in the use of the Finite Element Method (FEM) to model the complex mechanical behaviors of PET-G aligners. Through collaboration with dentists, I aim to establish a quantitative evaluation method for clinical treatment.

I specialize in optimizing Federated Learning for wireless edge networks. My research addresses critical energy and latency bottlenecks, highlighted by the development of the “FedBDC” framework which significantly reduces power consumption for IoT devices. Moving forward, I aim to bridge the gap between theoretical algorithms and practical applications, expanding into fields like Smart Agriculture. My vision is to become a global innovator, delivering sustainable, cross-disciplinary AI solutions to solve real-world societal challenges.

I am specializing in microfluidic engineering and vascular mechanobiology. My research focuses on developing advanced microchannel-based vascular models to investigate how mechanical stimuli, particularly oscillatory shear stress (OSS), regulate venous valve formation. By integrating microfluidics, 3D printing, and groove-assisted sacrificial molding, I aim to construct biomimetic vascular geometries with controlled branching and diameter variation. Through international collaborations and interdisciplinary approaches, my long-term goal is to establish versatile in vitro platforms that bridge engineering and vascular biology, contributing to both fundamental and future biomedical applications.

修士課程の研究を基盤に、電磁気学と力学を融合させた「次世代知的サスペンションシステム」の開発に取り組んでいます。具体的には、リニアモータ等の電磁アクチュエータを用いたアクティブ制御、エネルギー回生、および電磁界解析を主たる研究対象としています。将来的には、これらの技術開発を通じて自動車産業の発展に貢献することを目指しています。

I am a doctoral researcher working on multimodal intent recognition based on implicit human signals. My research focuses on understanding human actions and intentions from biosignals, vision, and physiological cues, with the goal of enabling natural and unobtrusive human–machine interaction. Currently, I develop systems that infer hand motion and task intent from low-channel electromyography and wearable sensors, and extend them to real-world interactive environments. Through this work, I aim to bridge machine perception and human intuition, and to contribute to human-centered intelligent systems applicable to healthcare, daily assistance, and collaborative environments.

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。