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About

Koji Tsuda stands at the forefront of AI-driven materials discovery and automated laboratory science, pioneering the integration of machine learning, robotics, and experimental chemistry to accelerate scientific exploration. His most influential contribution, "Black-Box Optimization for Automated Discovery" (2021, 132 citations), established a foundational framework for formulating materials and chemical design as optimization problems, enabling systematic, data-driven experimentation in place of intuition-guided trial and error. Building on this vision, Tsuda developed NIMS-OS—later evolved into NIMO—an open-source orchestration platform that closes the loop between AI algorithms and robotic experiments without human intervention, effectively realizing self-driving laboratories. His work spans an impressive breadth of applications: from AI-guided molecule generation targeting specific excitation energies, to automated Rietveld crystallographic refinement, autonomous optimization of organic synthesis reactions, and even machine-learning-powered odor blending. Together, these contributions demonstrate Tsuda's remarkable ability to translate abstract computational principles into working laboratory systems across chemistry, materials science, and beyond. His research has collectively garnered nearly 180 citations, reflecting a growing recognition that autonomous, AI-integrated experimentation represents the future of scientific discovery.