New AI Detects Scientific Breakthroughs in Papers | AcademicJobs
A compelling summary of the revolutionary AI tool from Binghamton and UVA researchers that maps 55M papers to identify true scientific disruptions.

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Sadamori Kojaku is an Assistant Professor in the School of Systems Science and Industrial Engineering at the State University of New York at Binghamton. He earned his BS, MS, and PhD in computer science from Hokkaido University, completing his doctorate in 2015 under the supervision of Professor Mineichi Kudo. Prior to his current appointment, Kojaku served as a postdoctoral fellow at Indiana University Bloomington, collaborating with Professor Yong-Yeol Ahn on representation learning applied to the science of science. He also worked as a research associate at the University of Bristol, focusing on network science under Professor Naoki Masuda.
Kojaku’s research centers on complex systems, network science, computational social sciences, the science of science, and representation learning. His contributions include developing methods for detecting core-periphery structures in networks, analyzing scientific migration trajectories, and creating metrics for identifying breakthroughs in science. Recent projects have explored how simpler models can challenge assumptions in artificial intelligence and advanced tools for mapping scientific evolution. He maintains an active publication record with works appearing in journals such as Nature Physics, Nature Communications, and Physical Review E.
A compelling summary of the revolutionary AI tool from Binghamton and UVA researchers that maps 55M papers to identify true scientific disruptions.