Cheng Soon Ong. Cheng Soon Ong is an Associate Science Director at Data61, CSIRO and a senior principal research scientist at the Statistical Machine Learning Group. He works on extending machine learning methods and enabling new approaches to scientific discovery, and has led the machine learning and artificial intelligence future science platform at CSIRO. He supervises and mentors many junior scientists, collaborates with scientists on problems in genomics and astronomy, advocates for open science, and is an adjunct Associate Professor at the Australian National University. He is co-author of the textbook Mathematics for Machine Learning, and his career has spanned multiple roles in Malaysia, Germany, Switzerland, and Australia.

Jason Jingshi Li. Dr Jason Jingshi Li is the founder of Learning Machines. He is a data scientist with more than a decade of experience in artificial intelligence research and development, including years of leading the technical delivery of numerous AI and Natural Language Processing solutions for IBM in Australia and Asia-Pacific region, and serving as a member of the ABC Advisory Council between 2019-2022 on providing strategic, technological, and programming advice and recommendations to ABC board and management. He is active in advising, architecting, and delivering enterprise AI solutions to Australian financial and public sectors.

Peike Li. Peike Li is a Research Scientist dedicated to building the next generation of general-purpose artificial intelligence. His research focuses on the core challenge of real-time multimodal reasoning, aiming to create foundation models that can natively understand, reason about, and generate information across a seamless spectrum of modalities, including streaming video, audio, and text. At Google, he has developed and scaled novel, efficient multi-modal generative models and large language models, pushing the boundaries of low-latency multimodal perception and creative generation. He holds a PhD in Computer Science from the University of Technology Sydney.

Rahul Shome. He is a tenure track lecturer in the School of Computing at The Australian National University. He is a researcher in Robotics & AI and dreams of a future with intelligent robots that are not only useful but good. He earned his Ph.D. and M.S. degrees at the Department of Computer Science, Rutgers University, advised by Prof. Kostas E. Bekris. He has worked with Prof. Lydia E. Kavraki as a Fellow of the Rice Academy at Rice University. He has published one book chapter in the Encyclopedia of Robotics and several invited articles. His publications have garnered a best paper award at IEEE MRS and a nomination for best paper in automation award at IEEE ICRA, a nomination for best paper at the Transactions on Robotics, among other recognitions. He serves on the organizing, chairing, and editorial boards of several major international robotics conferences and journals.