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We aim to solve societal issues by seamlessly integrating the physical and virtual worlds, thus moving toward the realization of a super-smart society. Leveraging Artificial Intelligence (AI), mathematical optimization, graph analytics, and high-performance computing, we develop practical applications through industry-academia collaboration.
In collaboration with numerous companies, we have successfully developed practical Cyber-Physical System (CPS) applications, such as smart factory implementations and optimized logistics systems.
At the ISC15 international conference (June 2015), our custom software achieved the world’s top rank in the Graph500 benchmark, which measures big data processing performance on supercomputers. Utilizing Japan's supercomputers "Fugaku" and "K," we maintained first-place rankings in the Graph500 benchmarks during the 8th, 10th to 18th, and 20th to 29th editions.
Our significant research into solving complex real-world problems using mathematical and computational methods was recognized through prestigious awards, including the Minister of Education, Culture, Sports, Science and Technology's Science and Technology Award (Research Category, 2017) and the Outstanding Professor in Smart Factory Award by IEOM Society International (2024).
Semidefinite Programming (SDP) is a prominent optimization problem with wide-ranging applications in combinatorial optimization, system control, data science, financial engineering, and quantum chemistry. Our team has developed efficient and robust algorithms for SDP, enabling large-scale parallel computations on supercomputers. We are actively advancing research in cooperation with academic institutions and industry partners.
Global initiatives are accelerating efforts toward achieving a super-smart society (Society 5.0) by combining cutting-edge technologies. Enhanced ICT capabilities have made it possible to pre-model real-world phenomena computationally and perform simulations and optimizations in response to environmental changes, effectively realizing Cyber-Physical Systems (CPS).
Together with industry partners, we are actively developing a CPS-based Mobility Optimization Engine (CPS-MOE). This engine aims to foster new industries, reduce costs and waste, and improve optimal scheduling of transportation services.
CPS-MOE specifically advances the following three mobility technologies:
Information Mobility: User clustering based on web access data and latent user interests.
Mobility of People and Objects: Deep learning-based position detection and tracking, congestion detection, optimization, and visualization of flows.
Transportation Mobility: Route and logistics optimization, along with optimized introduction and operation of Mobility as a Service (MaaS), including bike-sharing systems.