2026 CDSAI Workshop on AI/Machine Learning and Applications

1st July, 2026- Victoria University of Wellington, New Zealand

📅 Date: 1st July 2026

📍 Location: KKLT301, Kelburn Campus, Victoria University of Wellington

Morning Session

09:00–09:20 — Welcome and Opening Remarks – Prof Mengjie Zhang
09:20–10:20 — Keynote Talk: Prof. Wolfgang Banzhaf (Michigan State University)
Phenotypic Entropy and Simplicity Bias - Toward an Entropic Law for Evolving AI Systems
10:20–10:50 — ☕ Morning Tea / Coffee Break
10:50–11:25 — Talk 1: Prof Yun Sing Koh (The University of Auckland)
Beyond the Algorithm: AI for Social Impact
11:25–12:00 — Talk 2: Dr Caitlin Owen (University of Otago)
From Fishy Reference Intervals to Explainable AutoML
12:00–13:00 — 🍽 Lunch Break

Afternoon Session

13:05–13:40 — Talk 3: Dr Hercules Konstantopoulos (Malaghan Institute of Medical Research)
What we did instead: Getting a small, independent research institute AI-ready
13:45–14:20 — Talk 4: A/Prof Sydney Shep (Victoria University of Wellington)
Evolutionary Translation and Crisis Communication
14:20–14:55 — Talk 5: Dr Lujia(Luke) Liu (Victoria University of Wellington)
Machine-Learning Accelerated Predictions for Porous Materials for Practical Methane Capture
14:55–15:25 — ☕ Afternoon Tea / Coffee Break
15:25–16:00 — Talk 6: Prof Grant Dick (University of Otago)
Algorithm Baselines and Agentic PhD students in Symbolic Regression
16:00–16:45 — Panel Discussion: AI and the Future of Work, Research, and Society
Moderator: Prof Yi Mei
16:45–16:50 — Closing Remarks

Our Speakers

Prof Yun Sing Koh (The University of Auckland)

Talk Title: Beyond the Algorithm: AI for Social Impact

Bio: Professor Yun Sing Koh is a Professor of Computer Science at the University of Auckland, where she serves as Deputy Head of School and Director of the Centre of Machine Learning for Social Good. Her research is driven by a simple question: how can artificial intelligence help solve some of society's most pressing challenges? She develops adaptive AI systems that can learn continuously from evolving data and operate effectively in dynamic real-world environments. Working at the intersection of AI and societal impact, her research spans biodiversity conservation, climate resilience, environmental sustainability, and public health. Through collaborations with scientists, government agencies, industry, iwi, and communities, she translates cutting-edge AI research into practical solutions that deliver measurable real-world impact. Professor Koh has published more than 160 peer-reviewed papers in leading AI and machine learning conferences and journals, including ICLR, AAAI, IJCAI, and SIGKDD. Her research has been recognised with numerous international awards, including the Best Open-Source Paper Award at ACM Multimedia 2023, the Best Demo Paper Award at IEEE ICDE 2023, and Best Research Paper Awards at AJCAI 2022 and DSAA 2022. She has secured major competitive research funding from the Royal Society Te Apārangi Marsden Fund, the United States Office of Naval Research Global, MBIE Endeavour, and MBIE Catalyst programmes. Professor Koh also plays a leading role in the international AI community, serving as AI for Good Track Co-Chair at IJCAI, General Co-Chair of the IEEE International Conference on Data Mining (ICDM 2021), and General Co-Chair of the Australasian Data Mining Conference (2022 and 2023).

Dr Caitlin Owen (University of Otago)

Talk Title: From Fishy Reference Intervals to Explainable AutoML

Bio:Dr Caitlin Owen is a Lecturer in the School of Computing, previously a postdoctoral research fellow on the MBIE Data Science for Aquaculture project. She is also a 2025 Royal Society Te Apārangi Mana Tūāpapa Future Leader Fellowship recipient. She received a Bachelor of Science degree majoring in information science and computer science in 2014 and a Master of Business Data Science degree (with Distinction) in 2016 from the University of Otago. She also completed a PhD degree (Error Decomposition of Evolutionary Machine Learning) in 2021 from the University of Otago, receiving a Business School Exceptional PhD Thesis award. Her current research interests include explainable artificial intelligence, evolutionary computation and error decomposition.

Dr Hercules Konstantopoulos (Malaghan Institute of Medical Research)

Talk Title: What we did instead: Getting a small, independent research institute AI-ready

Bio:

A/Prof Sydney Shep (Victoria University of Wellington)

Talk Title: Evolutionary Translation and Crisis Communication

Bio:Sydney Shep is Reader in Book History and The Printer at Wai-te-ata Press, Te Herenga Waka – Victoria University of Wellington. She focuses on the interdisciplinary study of transnational and cross-cultural book history and print culture in the contexts of the history of empire, history of technology, and the history of reading. Technological convergence is an additional platform for research and practice, bringing both historic and contemporary media into creative conversation though explorations into the digital handmade, generative computer art, and typographically-situated augmented reality experiences. Her current research focuses on AI and Translation as well as big cultural data and collaborative kaupapa Māori approaches, all grounded in the theories, methods, and practices of digital humanities, spatial history, and cultural informatics. Sydney is also a practising letterpress printer, exhibiting book artist, and designer bookbinder who undertakes creative research commissions at Wai-te-ata Press.

Dr Luke Liu (Victoria University of Wellington)

Talk Title: Machine-Learning Accelerated Predictions for Porous Materials for Practical Methane Capture

Bio:Dr Luke Liu is a Senior Lecturer at the School of Chemical and Physical Sciences at VUW. He is also a Principal investigator at the MacDiarmid Institute for Advanced Materials and Nanotechnology, where he leads the Catalytic Architectures Research Program. Luke's research is in porous materials, including Metal-organic frameworks and Covalent organic frameworks. He is interested in high-throughput computational screening, materials design and synthesis for applications such as gas separations.

Prof Grant Dick (University of Otago)

Talk Title: Algorithm Baselines and Agentic PhD students in Symbolic Regression

Bio: Prof Grant Dick is Head of the School of Computing at the University of Otago. He is an active teacher and researcher in the areas of Data Science and Artificial Intelligence. He is currently co-director of the Data Science programme at Otago, which he helped introduced in 2018. Grant is currently a named researcher on the MBIE-funded Data Science for Aquaculture programme. His main research interests are in evolutionary computation methods, with a current focus on symbolic regression. Example's of Grant's work have appeared in top-ranked outlets such as IEEE Transactions of Evolutionary Computation and the Genetic and Evolutionary Computation Conference (GECCO).