Work experience as a researcher at a general electric manufacturer.

Data Science, Machine Learning, AI (Artificial Intelligence)

Introduction to Data Science A

Introduction to Data Science B

Introduction to Artificial Intelligence

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 realizing a future where humans can cooperate and coexist with AI without losing their humanity. To achieve this, I believe that an approach from not only a technological perspective but also a humanities perspective will be important.

• Machine learning models that balance predictive accuracy and interpretability
• Intervention and counterfactuals based on causal inference
• A framework for cooperation and coexistence between humans and AI

Toward the coexistence and cooperation of humans and AI, I aim to build a complementary relationship in which AI can cover areas where humans are weak while retaining their humanity, and humans can cover areas where AI is weak. To achieve this, “Explainable AI (XAI),” a technology that can clearly explain AI behavior, is important. I’m currently building a new general-purpose white-box model that combines predictability and explainability, and is working to apply this to predictions in areas where accountability is required and also explainable AI. Furthermore, intervention and counterfactuals based on causal inference are difficult to achieve using AI alone, which lacks real-world experience, and are therefore important areas where collaboration between humans and AI is necessary.

• 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., & 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. (2015). 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). 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). 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, Machine Learning, AI (Artificial Intelligence), Software Engineering, Project Management