IS24 - Mathematical modeling and causal inference applied to industrial and societal problems
In this session, reports of several collaborative experiences between the mathematics
community and industry will be presented. Their broadly diverse techniques, ranging
from mathematical modelling using computational simulations to causal inference using
machine learning, constitute fundamental components of these collaborations to elucidate
and resolve real-world problems. The topics addressed in this session include the
following.
• Materials design and materials informatics
• Human network analysis
• Medical sciences and clinical practice
• Future transportation problems
• Disaster controls and evacuation planning
In addition to individual efforts undertaken to resolve difficulties in different fields of
application, mathematical approaches sometimes provide novel perspectives for
apparently different problems. Moreover, not only are interactions between researchers
from academia and industry improved; the fostering of researchers from younger
generations also fills an important developmental role in these fields. Such activities will
also be reported in this session.