Keynote Speaker

Yun Sing Koh

Yun Sing Koh
Associate professor
School of Computer Science
University of Auckland, New Zealand
y.koh AT

Title: Developing Machine Learning Solutions for Environmental Science Challenges.

The effects of climate change are increasingly visible, from storms, droughts, forest fires to flooding. Addressing climate change problems involves mitigation and adaptation. Current advances in machine learning and artificial intelligence present an opportunity to build better tools and solutions to help address some of the most pressing environmental challenges and deliver positive impact. In this talk, I will focus on machine learning research we have carried out to combat some of these environmental challenges, explicitly discussing two case studies in monitoring air and water pollution. I will discuss the key challenges in applying machine learning algorithms to tackle environmental problems.

Yun Sing Koh is an Associate Professor at the School of Computer Science, The University of Auckland, New Zealand. Her research is in the area of machine learning and artificial intelligence. Within the broad research realm, she is currently focusing on several strands of research: data stream mining, continual learning and adaptation, transfer learning and anomaly detection. Yun Sing is passionate about using machine learning for social good, and her research has been applied to interdisciplinary applications in environment and health domains. She has published 100+ peer-reviewed publications in top conferences and journals, including IJCAI, IEEE ICDE, IEEE ICDM, Machine Learning Journal and Journal of Artificial Intelligence. Yun Sing has been active in the research community, including serving as the General Co-Chair at the IEEE International Conference on Data Mining 2021, Workshop Co-Chair at ECML/PKDD conference 2021, Program Co-Chair of the Australasian Data Mining Conference 2018 and the Workshop Co-Chair for the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining.

Important dates

  • Paper submission due: June 1, 2022
  • Acceptance Notification: July 31, 2022
  • Camera Ready due: August 15, 2022
  • Conference date: October 16, 2022


IEEE logo