[CFAR Distinguished Professor Lecture Series 1]
From Fundamental Machine Learning to Data-Driven Science and AI in Society: Research at RIKEN-AIP by Professor Masashi Sugiyama
09 Mar 2022 | 10:15am (Singapore Time)
Artificial intelligence became an indispensable tool to advance science and industry, and machine learning is one of the main driving forces to boost this movement.
In this talk, Prof Masashi Sugiyama will first introduce the activities of the RIKEN Center for Advanced Intelligence Project (RIKEN-AIP). RIKEN is Japan’s largest and most comprehensive research organisation for basic and applied science, and AIP is working on advancing fundamental artificial intelligence technologies (machine learning, optimisation, etc.), their applications in accelerating scientific research (cancer, material, etc.) and solving socially critical problems (natural disaster, elderly healthcare, etc.), and discuss social aspects of artificial intelligence (ethical guidelines, personal data management, etc.).
He will give an overview of RIKEN's recent research achievements on reliable machine learning. In modern applications of machine learning, it becomes increasingly important to consider robustness against various factors such as data bias (caused by changing environments, privacy concerns, etc.), insufficient and inaccurate information (due to weak supervision, label noise, etc.).
Masashi Sugiyama received the PhD degree in Computer Science from Tokyo Institute of Technology, Japan, in 2001. After experiencing assistant and associate professors at the same institute, he became a professor at the University of Tokyo in 2014. Since 2016, he has concurrently served as Director of RIKEN Center for Advanced Intelligence Project, leading the groups of fundamental AI technologies, AI applications, and social issues of AI.
He (co)-authored machine learning monographs including Machine Learning in Non-Stationary Environments (MIT Press, 2012), Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012), Statistical Reinforcement Learning (Chapman and Hall, 2015), Introduction to Statistical Machine Learning (Morgan Kaufmann, 2015), Variational Bayesian Learning Theory (Cambridge University Press, 2019), and Machine Learning from Weak Supervision (MIT Press, 2022).