In the Computational Science Laboratory, we pursue applications of mathematics to various fields using computers. In recent years, our projects have included developing an algorithm for the automatic classification of singularities using comprehensive Gröbner basis systems, and using this algorithm to perform a differential topological classification of feasible solution sets (i.e. the sets of solutions that satisfy the constraints of optimization problems). We have also analyzed chemical reaction networks using discrete group theory, and in collaboration with industry have worked on developing multi-objective bandit theory for risk evaluation, among other endeavors.

In recent years, fields of research go in and out of vogue very quickly. For example, machine learning – which was largely ignored just a decade ago – has suddenly become a focal point as computer performance has improved, and I believe that new technologies such as quantum computing will continue to emerge in the coming years. On the other hand, what you are studying right now might become outdated just a few years down the line. However, no matter what new technology appears ten years from now, if you have mastered the fundamental mathematics that underpins these innovations, it will give you the power to continue learning new technologies and to survive in an era of rapid change even after you graduate from the university.