This presentation explores machine learning (ML) integration with animal behavior studies and its transformative applications across neuroscience, human mobility analysis, and engineering. At the core of our research is the affinity between the complex behaviors observed in animal society and the predictive capabilities of ML algorithms.
In neuroscience, we introduce the development of robotic microscopes and feedback projections, offering insights into animal behavior at macroscopic and microscopic levels. This foundation supports our further applications in diverse fields such as retail analytics, human relationships estimation, and mobility service design. In the engineering domain, our work extends to preventive maintenance (condition-based maintenance: CBM) in manufacturing and transportation, drawing from the predictive nature of ML to foresee and mitigate equipment failures. Moreover, we introduce ML for digital twinning to create dynamic virtual models of physical systems.
This array of applications highlights the critical role of integrating computer vision and ML into problem-solving workflows across various production plants. This presentation emphasizes the essential need for interdisciplinary collaboration, bridging the gap between biologists, data scientists, and engineers.
Professor Koichi Hashimoto is a professor at Tohoku University and vice dean of the Graduate School of Information Sciences. He is the leader of the Grant-in-Aid for Scientific Research on Innovative Areas: Systems Science of Bio-navigation and the Grant-in-Aid for Transformative Research Areas: Hierarchical Bio-navigation Integrating Cyber-physical Space. He was awarded the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology (Research) in 2021. His interests include visual servo, 3D point cloud, and movement analysis.
Clean water and clean air are vital for public health. This project focuses on developing high-efficiency and environmentally sustainable filters for removing harmful air/water pollutants. The team has developed novel architectures and functionalities for the filters to achieve high permeance, high removal efficiency, and excellent reusability.
