Sydney AI Meetup 2025
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Stephen Gould is a Professor of Computer Science at the Australian National University (ANU). He is a former Australian Research Council (ARC) Future Fellow, ARC Postdoctoral Fellow, Microsoft Faculty Fellow, Contributed Researcher at Data61, Principal Research Scientist at Amazon Inc, Director of the ARC Centre of Excellence in Robotic Vision, and Amazon Scholar. Stephen received his BSc degree in mathematics and computer science and BE degree in electrical engineering from the University of Sydney in 1994 and 1996, respectively. He received his MS degree in electrical engineering from Stanford University in 1998. He then worked in industry for several years where he co-founded Sensory Networks, which later sold to Intel in 2013. In 2005 he returned to Stanford University and was awarded his PhD degree in 2010. In November 2010, he moved back to Australia to take up a faculty position at the ANU. Stephen has broad interests in the areas of computer and robotic vision, machine learning, deep learning, structured prediction, and optimization. He teaches courses on advanced machine learning, research methods in computer science, and the craft of computing. His main research focus is on automatic semantic, dynamic and geometric understanding of images and videos.

Dr. Hung Le is an ARC DECRA Fellow and a Lecturer at Deakin University, specializing in deep reinforcement learning. He leads PhD research in machine learning, reinforcement learning and large language models at the Applied Artificial Intelligence Initiative (A2I2). His work focuses on developing neural memory-augmented agents, with applications in health, robotics, dialogue systems, and natural language processing. Dr. Le regularly publishes in top AI venues such as NeurIPS, ICLR and ICML.

Dr. Jianglin Qiao received a Bachelor of Electrical Engineering from the University of Sydney. He holds a dual-award PhD in Computer Science from Western Sydney University and Universitat Autònoma de Barcelona (UAB). He is currently a research fellow at the University of South Australia, working on a SmartSat CRC-funded project about real-time satellite tasking and resource allocation. His research mainly focuses on multi-agent systems, multi-agent reinforcement learning, and game theory with application domains in space and transportation.

Dr. Mingjie Li is a postdoctoral scholar at the Laboratory of Artificial Intelligence in Medicine and Biomedical Physics at Stanford University. His research focuses on medical generative modeling, with the goal of developing clinically meaningful and controllable image synthesis methods to support diagnosis and treatment planning. He received his Ph.D. from the University of Technology Sydney. His work has been published in top-tier venues including T-PAMI, CVPR, NeurIPS, and ECCV. He also serves as a regular reviewer for leading journals and conferences such as IEEE TNNLS, IEEE ToMM, CVPR, and NeurIPS.

Suneeta is passionate about solving real-world problems with engineering, data, science, and machine learning. She's a PhD in applied science with a computer science and engineering background. She has extensive distributed, scalable computing and machine learning experience from IBM Software Labs, Expedita, USyd, Nearmap and more recently harrison.ai. She currently leads the AI Engineering division of harrison.ai, a clinician-led artificial intelligence medical technology company tackling some of the biggest issues in healthcare causing inequitable diagnosis today. She believes in lifelong learning and is passionate about knowledge sharing. She is also an author for O'Reilly and writes technical blogs in her spare time.

Shirui Pan received his Ph.D. in Computer Science from the University of Technology Sydney (UTS) and is a Professor in the School of Information and Communication Technology at Griffith University, Australia. His research focuses on data mining and machine learning and has been published in top venues, including Nature Machine Intelligence, KDD, and ICLR, among others. He has received several prestigious awards, including the 2024 IEEE CIS TNNLS Outstanding Paper Award, the 2020 IEEE ICDM Best Student Paper Award, the 2024 AI’s 10 to Watch recognition, and the 2024 IEEE ICDM Tao Li Award. He is also an ARC Future Fellow.

Jianfei Cai is a Professor at Faculty of IT, Monash University, where he had served as the inaugural Head for the Data Science & AI Department. Before that, he was Head of Visual and Interactive Computing Division and Head of Computer Communications Division in Nanyang Technological University (NTU). His major research interests include computer vision, deep learning and multimedia. He has successfully trained 40+ PhD students with three getting NTU SCSE Outstanding PhD thesis award and one getting Monash FIT Graduate Research Student Excellence Award. Many of his PhD students joined leading IT companies such as Meta, Apple, Amazon, Adobe and TikTok or become faculty members in reputable universities. He is a co-recipient of paper awards in ACCV, ICCM, IEEE ICIP and MMSP, and a winner of Monash FIT’s Dean's Researcher of the Year Award. He serves or has served as an Associate Editor for TPAMI, IJCV, IEEE T-IP, T-MM, and T-CSVT as well as serving as Area Chair for CVPR, ICCV, ECCV, IJCAI, ACM Multimedia, ICME, ICIP and ISCAS. He was the Chair of IEEE CAS VSPC-TC during 2016-2018. He had served as the leading TPC Chair for IEEE ICME 2012 and the best paper award committee chair & co-chair for IEEE T-MM 2020 & 2019. He is the leading General Chair for ACM Multimedia 2024, and a Fellow of IEEE.