|Machine Learning and AI|
| Integrated Data Analysis on Multiple Institutions
In recent years, as data collection and accumulation have become easier, various companies and institutions have been accumulating their own data and working on analysis using artificial intelligence (AI). To improve the performance of AI analysis, it is necessary to collect a sufficient amount of data. Therefore, it is expected that highly accurate analysis will be possible if data held by multiple institutions can be integrated for analysis. However, it is difficult to share the original data containing personal information and trade secrets across institutions. Therefore, there is a need for technology that enables integrated analysis across corporate and institutional boundaries without sharing the original data.
|Optimization and Evolutionary Computation
Optimization is the task of finding the most appropriate set of numerical values to solve a certain problem. Optimization has a wide range of applications, such as finding the optimal design of objects (such as antennas, car parts, protheses, etc), finding the optimal parameters of a mathematical model (such as a weather model, subsurface model, etc), or, more recently, finding optimal parameters for Machine Learning models (such as weights and hyper-parameters).
Numerical Computation Techniques in Highly Parallel
Computer simulations are required in broad areas in industry and science from nano-level to cosmic scales, such as the development of new materials, functional analysis of protein and DNA, efficient design and development of products for vehicles, accurate weather prediction, supernova explosion, and so on. This computational group studies high-performance algorithms and develops computer programs, collaborating with researchers such as in life science.