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About

Tomohiro Masuda is pioneering the automation of cell culture and regenerative medicine through the integration of robotics and artificial intelligence. His research focuses on eliminating the reliance on expert tacit knowledge in experimental biology, particularly in the skill-intensive process of induced differentiation. Masuda’s major contribution is the development of a robotic AI system that employs a batch Bayesian optimization algorithm to autonomously search for and establish optimal cell culture conditions—a task that traditionally requires years of experience. His most cited work, "Robotic search for optimal cell culture in regenerative medicine" (2022), has garnered 96 citations, reflecting its significant impact on the field. Additionally, his 2020 paper on a variable scheduling maintenance culture platform for mammalian cells (28 citations) addresses the scalability and reproducibility challenges in cell biology. By creating systems that can autonomously induce differentiation and maintain high-quality cell cultures, Masuda is not only accelerating research timelines but also democratizing advanced cell culture techniques, making them accessible beyond specialized laboratories. His work stands at the intersection of robotics, machine learning, and biomedical engineering, offering a transformative path toward scalable, reproducible, and less labor-intensive regenerative medicine.