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AiThority Interview with Masanobu Inubushi, Associate Professor at Tokyo University of Science

Hi, welcome to AIThority Interview Series. You come from a very enriching background in the data science technology industry. Please tell us a little bit about your journey and what inspired you to start in the AI field.

When I was an undergraduate student, I was surprised by the unpredictability of chaotic dynamics in classical mechanics such as the double pendulum. Although modern physics uncovers the mystery of black holes and elementary particles, it cannot predict the future state of the double pendulum. It is natural for me to ask how about AI?

The last two years have accelerated digital transformation for businesses of all sizes and stature. What has been the biggest lesson for you that helped you stay on top of your AI research? Would you like to share your pandemic experience on how you managed to continue your AI research during the uncertain times?

The pandemic has not affected my research, because I can continue my research (calculation and discussion with co-researchers) if computers are available.

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Please tell us a little bit about your latest research on the Reinforcement Learning algorithm for fluid mixing processes?

This is just the first step. I hope this study opens the way to utilize Reinforcement Learning in a variety of industrial fields.

How is your RL algorithm different from other techniques? Why did you choose MDP for your research?

Reinforcement Learning is suitable for global-in-time optimization problems, flexible, and mathematically guaranteed, which is superior compared to other techniques.

Do you think it would be relatively easier for you to extend the scope of this AI ML research to turbulent flow for fluid mixing processes?

There exists a large difference between turbulent mixing and laminar mixing, and thus, it is not easy to extend our method to turbulent mixing as is. However, in my opinion, our findings give some hints for that.

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Please tell us more about your other AI research studies. What are your scientific learning from using AI and Machine Learning for industrial applications?

Applied mathematics will play an important role more and more in the future. Regarding my interests, theory of differential equations, dynamical system theory (chaos theory), and fluid mechanics. They have a long history and rich theoretical contents. I am interested in AL and ML research based on applied mathematics. For instance, as I wrote in a Book chapter ( ), reservoir computing (recurrent neural nets; RNN) can be seen as a “data-driven” dynamical system, and we can use many of mathematical tools to analyze the RNN dynamics.

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An advice to every data science / AI professional looking to start in this space:

Not advice but I enjoy at the intersection of traditional applied math and the new data-driven methods. I am looking forward to seeing new findings in the intersection!

Thank you, Masanobu! That was fun and we hope to see you back on soon.

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Masanobu Inubushi is currently an Associate Professor at the Tokyo University of Science, Japan. He obtained his undergraduate degree in 2008 from the Tokyo Institute of Technology, Japan. He then obtained his PhD in Mathematics from the Research Institute for Mathematical Sciences (RIMS) at Kyoto University Graduate School in 2013. After working at NTT, Communication Science Laboratories from 2013-2018, he joined Osaka University as Assistant Professor in 2018. Dr. Inubushi has over 25 published research works that have been cited over 400 times. His research interests include fluid mechanics, chaos theory, and mathematical physics, and machine learning.

The Tokyo University of Science Logo

Tokyo University of Science (TUS) is a well-known and respected university, and the largest science-specialized private research university in Japan, with four campuses in central Tokyo and its suburbs and in Hokkaido. Established in 1881, the university has continually contributed to Japan’s development in science through inculcating the love for science in researchers, technicians, and educators.

With a mission of “Creating science and technology for the harmonious development of nature, human beings, and society”, TUS has undertaken a wide range of research from basic to applied science. TUS has embraced a multidisciplinary approach to research and undertaken intensive study in some of today’s most vital fields. TUS is a meritocracy where the best in science is recognized and nurtured. It is the only private university in Japan that has produced a Nobel Prize winner and the only private university in Asia to produce Nobel Prize winners within the natural sciences field.

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