Worked in research and development at an electronics manufacturer before assuming the current position.

Data Science, Machine Learning, AI (Artificial Intelligence)

Introduction to Data Science A

Introduction to Data Science B

Data Visualization

Introduction to Artificial Intelligence

Practical Data Science

As expressed by the words "information flood" and "information explosion", our surroundings are overflowing with data and information. The spread of the Internet and the Internet of Things (IoT) has accelerated this trend. In addition, the technological progress of machine learning and AI (Artificial Intelligence) in the last few decades has been remarkable, and society as a whole is rapidly changing, whether it is favorable or not. I hope that by acquiring knowledge and skills in data science, you will be actively involved in the realization of a better world.

I am interested in building environments and frameworks for humans and AI to cooperate and coexist. This is a very challenging research field that requires approaches from various aspects, not only engineering and technology but also management and sociology, etc.

• Cooperation and coexistence between humans and AI
• Building a machine learning model that balances the tradeoff between accuracy and interpretability
• Application of machine learning to project management
• Application of machine learning to organizational management
• Quality assurance of AI systems

We aim to build a complementary relationship between humans and AI, in which each side can utilize its strengths to cover the areas where the other side is weak.

The first approach from a technological perspective is to build a machine learning model that satisfies both prediction accuracy and interpretability. Traditionally, there has been a trade-off between prediction accuracy and interpretability. We have constructed a new machine learning model called SNB (superposed naive Bayes) that balance the tradeoff between prediction accuracy and interpretability by generating an ensemble model of naive Bayes classifier to improve prediction accuracy and then executing a linear approximation of the ensemble model to convert it back to a simple naive Bayes. It is expected to be applied to Explainable AI (XAI), etc.

We are also researching the application of AI to project management and organizational management, and the quality assurance of AI systems. They are affected not only by the interface between humans and AI, but also by interactions and communication between humans. In addition to technical aspects, approaches from various aspects including management and sociology such as behavioral economics, transaction cost theory, and process orientation are required.

• Mori, T., & Uchihira, N. (2021). Machine-in-the-Loop Process in Project Risk Management. In The 16th International Conference on Knowledge, Information and Creativity Support Systems (KICSS2021).
• Mori, T., & Uchihira, N. (2019). Balancing the trade-off between accuracy and interpretability in software defect prediction. Empirical Software Engineering, 24(2), 779-825.
• Mori, T. (2015, December). Superposed naive bayes for accurate and interpretable prediction. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (pp. 1228-1233). IEEE.
• Mori, T., Tamura, S., & Kakui, S. (2013, October). Incremental estimation of project failure risk with Naive Bayes classifier. In 2013 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (pp. 283-286). IEEE.
• Mori, T., Ishii, K., Kondo, K., & Ohtomi, K. (1999, September). Task planning for product development by strategic scheduling of design reviews. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 19722, pp. 115-126). American Society of Mechanical Engineers.

Data Science, Statistics, Bayesian Statistics, Machine Learning, AI (Artificial Intelligence), Data Visualization, Project Management, Risk Management, Software Engineering, AI Quality Assurance