Center for Mathematical Modeling and Data Science,
I originally specialized in applied mathematics. However, rather than immersing myself in mathematics, I prefer to work with engineers and other professionals from industry.
Once I was involved in research on artificial blood vessels used in dialysis. Connecting an artificial blood vessel with a natural one often caused thinning of the vein and the formation of blood clots. Physicians were troubled with this phenomenon because they did not know the cause. My calculations revealed that this phenomenon was due to the blood flowing too fast and creating a vortex in the blood vessel. Namely, walls of such veins got sucked into the vortex of blood, causing the blood vessels to narrow. Thus, we undertook a collaborative research project to design an artificial blood vessel suitable for dialysis. We gathered researchers in bioengineering, fluid engineering for blood flow analysis, design engineering, supercomputing for high-precision calculations, and mathematics to create a new mathematical framework. We looked for the best shape of artificial vessels to prevent blood clots from forming. We discovered that an asymmetrical shape between the upstream and downstream sides of the blood vessel was less likely to cause blood clots.
I first experienced the joy of working with people from other fields when I was in Finland for graduate school. I studied fluid simulations at an institute similar to the National Institute for Environmental Studies, Japan. At the Finnish institute, experts from various fields, including chemistry, biology, statistics, and simulations of water circulation in lakes, worked as a team to explore pollution control measures for rivers and lakes. Through research meetings, I learned about fascinating research projects where people from various fields work collaboratively.
A strength of MMDS is that we have faculty members with a variety of specialties. For example, Professor Wataru Takano specializes in robotics, Professor Shigeo Matsubara in AI, and Professor Nobuhiko Asakura in cognitive science.
At other universities, I believe that most of the faculty members who teach data science come from a statistics background. On the other hand, at MMDS, the faculty members have diverse specialties. Consequently, a single data science course can include a wide variety of content. Even in a course for first-year undergraduate students, we talk about robots and brain measurements with electrodes to capture and analyze visual and auditory signals. In my class, I talk about fluid calculations.
I think the fact that each MMDS faculty member makes use of their specialty and does not limit their lectures to a general introduction to statistics makes MMDS data science courses more attractive than those at other universities. In addition to liberal arts education for undergraduates, MMSD is also involved in a wide range of activities, including high school-university connection initiatives, education for graduate students, and recurrent education for working professionals. These endeavors also set MMSD apart from other universities.
PBL (Problem Based Learning) is offered not only by Osaka University, but in collaboration with national, public, and private universities in the Kansai, Chugoku, and Shikoku regions. PBL provides opportunities to experience a path to solve a given task (problem) through group work using mathematical, data, and AI-related knowledge and techniques. We prepare every session with close attention so that students can address a task, which is a real-world example provided by industry. We are fortunate because many experts from participating universities cooperate in PBL. In 2021, we had about 130 participants, including faculty members from 7 universities.
The participating students are from different undergraduate departments and years. For each session, we prepare about three tasks according to their level of proficiency in programming and machine learning. A task is assigned on the first day of the session. On the final day of the session, students give a presentation to all participants about their conclusions. Participating universities and faculty are free to schedule and structure their working sessions as they prepare for the final presentations.
There seems to be a lot of interest in what other universities are doing to approach the task and how the approaches differ. We have also received requests for opportunities to exchange human resources and information to inform each other about the progress on the task. I would like to increase the number of such opportunities as they provide lively interactions.
PBL is more of a learning opportunity for undergraduate students. For graduate students, we also have study groups (joint research with companies), where faculty members, professionals from private companies, and graduate students work together to examine a problem that is more difficult than one considered in PBL. Hence, it is often unclear where to begin to resolve such a problem.
Similar to PBL, study groups utilize knowledge and techniques of mathematics, data, and AI. However, the content is more practical. By combining new technologies (technological seeds), which are created every day at universities with unresolved problems (technological needs), in the real business world, we aim to provide participating graduate students with the opportunity to explore solutions to practical issues. Such opportunities are difficult to experience at universities using academic methods. We also hope that this study group will deepen the relationship between universities and companies.
It's indeed hard to coordinate participants in PBL and study groups, but I try my best to make it beneficial for everyone. After enough preparatory efforts and conversations with people not only from universities but also from industry, I look forward to observing what happens when a lot of different people get together and collaborate.
Interview as of January 2022
*Interview and photography were conducted with countermeasures against COVID-19.