AAII Research Seminar Series | Seminar 9: Prof Masashi Sugiyama

The UTS AAII Research Seminar Series | Seminar 9

The UTS AAII Research Seminar series is dedicated to fostering an engaging, inclusive, and interdisciplinary environment for internal and external AI researchers to share research ideas and findings in person. Our goal is to promote cross-lab communications and achieve visionary collaborations. 

The series will encompass a wide range of AI topics, spanning from theoretical foundations to cutting-edge methodological development, cross-disciplinary applications, and insights from industry practices. 

With a monthly cadence, this seminar series will offer valuable opportunities to invite world-renowned visiting scholars to share their latest research frontiers, mentor AAII’s middle and early career researchers with the guidance for research leadership, and educate HDR students to hone their presentation and communication skills to excel in their academic journeys.

Topic: Machine Learning from Imperfect Information

Abstract: In recent machine learning applications, training data is often full of uncertainties. In this talk, Prof Sugiyama will give an overview of their research on reliable machine learning from imperfect information, including weakly supervised learning, noise-robust learning, and transfer learning. Then, he will discuss their recent challenges to integrate these approaches and develop a generic machine learning methodology with fewer modeling assumptions.

Speaker: Prof Masashi Sugiyama (Director, RIKEN Center for Advanced Intelligence Project,The University of Tokyo)

Prof Masashi Sugiyama received his Ph.D. in Computer Science from Tokyo Institute of Technology, Japan, in 2001. After serving as an assistant and associate professor at the same institute, he became a professor at the University of Tokyo in 2014. Since 2016, he has also served as the director of the RIKEN Center for Advanced Intelligence Project. His research interests include theories and algorithms of machine learning. He was awarded the Japan Academy Medal in 2017 and the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology of Japan in 2022.

 

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UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.