Ryosuke Kojima

The University of Tokyo, Kyoto University of Education

Papers

Total Citations

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About

Ryosuke Kojima is a researcher whose work spans two remarkably distinct yet equally impactful fields: biomedical imaging and machine learning for dynamical systems. In the biomedical realm, Kojima has contributed to the advancement of intraoperative fluorescent imaging techniques, most notably developing a fluorogenic probe targeting γ-glutamyltranspeptidase for the rapid visualization of thymoma and thymic carcinoma. This work directly addresses a critical clinical challenge — ensuring adequate surgical margins during minimally invasive thoracoscopic and robotic procedures — with meaningful implications for patient prognosis and oncological outcomes. Simultaneously, Kojima has pursued foundational research in deep learning applied to dynamical systems, tackling the rigorous problem of guaranteeing dissipativity in neural network models trained on time-series data. This contribution bridges control theory and modern machine learning, offering robust frameworks for stability assurance in learned system representations. Though his published work remains in early citation stages, accumulating three citations each across both domains, the interdisciplinary breadth of Kojima's research portfolio signals a uniquely versatile scientific profile with strong potential for growing influence across surgical oncology and computational engineering communities.