Welcome to Green and Responsible AI Day!

Join us for an exciting day dedicated to Green and Responsible AI. Our day kicks off with an inspiring keynote speaker session, where you’ll gain invaluable insights into the intersection of AI and environmental sustainability. Our expert-filled Responsible AI Panel session will explore real-world applications and emerging trends in the field, discussing the role AI plays in building a greener and more responsible future. Following the panel session, our spotlight talks will highlight innovative projects and practical applications that showcase the positive impact of green and responsible AI. To top it off, we have a series of application paper presentations, as well as an interactive tutorial session where experts share their research and findings. Join us to discover how AI is contributing to a sustainable future.

Keynote Session:

TAIAO – Green AI in Green Aotearoa

Albert Bifet

Professor Albert Bifet is the Director of the Te Ipu o te Mahara AI Institute at the University of Waikato and Co-chair of the Artificial Intelligence Researchers Association (AIRA). His research focuses on Artificial Intelligence, Big Data Science, and Machine Learning for Data Streams. He is leading the TAIAO Environmental Data Science project and co-leading the open source projects MOA Massive On-line Analysis, StreamDM for Spark Streaming and SAMOA Scalable Advanced Massive Online Analysis. He is the co-author of a book on Machine Learning from Data Streams published at MIT Press. He is one of the winners of the best paper award at the ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT) 2023, and he will be the general co-chair of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2024.

Responsible AI Panel

Panel Speakers:

Spotlight Talk:

Navigating the Academic Odyssey: Insights and Advice for Early Career Researchers

Mingming Gong

Dr. Mingming Gong is a Senior Lecturer in Data Science at the University of Melbourne, and an affiliated associate professor at Mohamed Bin Zayed University of Artificial Intelligence. His research focuses on modeling the causal generative process of real-world complex data by connecting graphical models and deep learning, with applications to computer vision, healthcare, etc. He has authored and co-authored 80+ research papers on top venues such as ICML, NeurIPS, ICLR, CVPR, ICCV, AAAI, IJCAI. He has been invited to serve as area chairs for top conferences and action editors for prestigious journals.  He received the ARC Discovery Early Career Researcher Award in 2021 and the Australasian AI Emerging Research Contribution Award in 2022. 

Spotlight Talk:

Machine Learning for Social Good in Aotearoa New Zealand

Gillian Dobbie

Professor Gillian Dobbie is widely recognised for her research in database systems and artificial intelligence. She holds a PhD in Computer Science from the University of Melbourne, where she specialized in database theory and design. Her research interests encompass a wide range of topics, including conceptual modeling, knowledge representation, query optimization, data privacy, data stream mining, continual learning, and adversarial learning. She has published over 160 papers in top-tier conferences and journals, such as SIGCSE, IJCAI, ICDM, SIGIR, CIKM, ICDE, SIGMOD, TODS, ACM Computing Surveys. She was awarded the DASFAA 10+ Year Best Paper Award for her research contribution with Prof Ling Tok Wang and Prof Mengchi Liu. Professor Dobbie is a Fellow of the Royal Society of New Zealand and Chair of the Marsden Fund Council.

 Throughout her career Professor Dobbie has been a catalyst for collaboration and interdisciplinary work, leading the development of projects such as Precision Driven Health, which received the MinterEllisonRuddWatts Research & Business Partnership Award. She continued to build bridges between academia and industry through her leadership of the Auckland ICT Graduate School.

 Beyond her academic pursuits, Professor Dobbie is actively engaged in promoting diversity and inclusivity in STEM fields. She is passionate about encouraging underrepresented groups to pursue careers in computer science, fostering an environment where everyone can thrive.

Data Privacy: Access and Consent Management using Personal Online Datastores – a Hand’s on Primer

Anushka Vidnage, Jessica Moore, Graham Williams

Dr Anushka Vidanage is a research fellow at the Software Innovation Institute (SII) in the ANU School of Computing, with over 6 years of experience research fields of data privacy. His primary research interests include data privacy and security through PODs, privacy-preserving record linkage, and distributed machine learning.

Dr Jess Moore is an applied data scientist in the ANU School of Computing and Chief Operating Officer of the Software Innovation Institute (SII), with over 15 years post PhD experience. Her research interests are in consent management, analytics and engineering with PODs.

Professor Graham Williams is Chief Scientist of the Software Innovation Institute, ANU School of Computing. He returned to the ANU after a career as Microsoft’s Director of Data Science, Lead Data Scientist for the Australian Government’s Data Analytics Centre of Excellence, and Principle Research Scientist with CSIRO Australia. His current research interest is in AI and ML in a privacy based world.