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Shane Culpepper is a Professor of Artificial Intelligence at the University of Queensland in St. Lucia, Australia. Before joining the University of Queensland in 2023, Professor Culpepper held a continuing academic position at RMIT University in Melbourne, Australia. He received his PhD in Computer Science from the University of Melbourne in 2008. His research focuses primarily on building better Search and Recommendation Systems and is primarily interested how to responsibly integrate efficient and scalable generative AI models for search, recommendation, and question answering. Professor Culpepper’s work has applications in a number of downstream applications for Legal, Health, real estate speculation. He has been instrumental in founding the AI Research Network and the Research Center for Enterprise AI at the University of Queensland.

Over his 17 year career, Professor Culpepper has supervised 19 PhD students and co-authored more than 140 peer reviewed papers with 132 different research collaborators on problems that range from core basic research, such as algorithm efficiency and scalability, to practical real world problems on building and deploying new machine learning algorithms for search and recommendation systems. While often technical, his work is always user-driven as humans are the main consumers of this technology. This user-centric research focus has led to several papers on controlled user studies which guide the development of better evaluation techniques which model human behaviour. In the last 5 years, Professor Culpepper has been a program co-chair for international conferences such as SIGIR and CIKM, and co-organized conferences such as WSDM and SWIRL. Professor Culpepper previously held an ARC DECRA fellowship in 2013 as well as an RMIT Vice-Chancellor's Principal Researcher fellowship in 2017. Before joining the University of Queensland. Professor Culpepper was the founding director of the Centre for Information Discovery and Data Analytics at RMIT University. In total, he has been a chief investigator on 11 research grants totalling ~$3.8 Million AUD.

I am a computational biologist with a centre-wide research role in the ARC Centre of Excellence for Plant Success in Nature and Agriculture, based here at UQ. I spend my time researching new computational techniques for predicting complex quantitative traits by integrating multiple layers of 'omics data (amongst dozens of other things!).

Areas of interest:

• Machine Learning, AI and high performance computing to learn and exploit functional connectivity in biological data
• Gene Expressions networks
• Multiplex networks, information propagation and perturbation
• Genomic Prediction

My goal is to aid crop and forestry breeders in selecting parental lines more accurately, which gives us a pathway to improving certain plant species. I also spend time developing new data analysis techniques that are being applied to human disease and conditions such as Autism and substance addiction.

David completed his PhD at Australian National University in 2017, focusing on the genome-wide basis of foliar terpene variation in Eucalyptus. He then undertook a postdoc at Oak Ridge National Laboratory, a US Dept of Energy lab with a focus on big data. After a stint as a staff scientist at Oak Ridge, David arrived at the Centre of Excellence in 2023 in the role of a Senior Research Fellow.

Hang Li is a Research Officer and graduating PhD candidate in IELab within the School of Information Technology and Electrical Engineering at the University of Queensland, Australia, where he works closely with Prof. Guido Zuccon, A/Prof. Bevan Koopman, and Dr. Ahmed Mourad. Prior to Ph.D, Hang received his Bachelor of Science degree in Computer Science at the University of Minnesota Twin-Cities in United States in 2016.

Hang works at the intersection of Information Retrieval (IR), Large Language Models (LLMs), Natural Language Processing (NLP), and Machine Learning (ML) applications, where he utilises different relevance feedbacks to empower the information retrieval system. His recent work seeks to address the gap between relevance feedback, deep language models, and information retrieval through different approaches that helps to improve the IR system effectiveness with minimal efficiency cost.

Hang publishes at premier academic venues in IR (e.g. SIGIR, ECIR, WSDM, WWW, TOIS, IJDL). His work is supported by Grains Research and Development Corporation, through the AgAsk project.

Research interest: Monitoring ecosystem health of coral reefs and seagrass habitats, integrating field and remote sensing image datasets, and the developing applied cost-effective mapping and monitoring approaches. Developed approaches have been adopted as standard practice globally, making a difference in conservation of these valuable habitats. The long term monitoring studies at Heron and Moreton Bay formed the basis for the development of mapping and monitoring over time and space at local to global scale. See here major research impact

Major projects:

• Long term monitoring of benthic composition at Heron Reef (2002-ongoing).
• Long term monitoring of seagrass composition and abundance in Moreton bay Marine Park (2000-ongoing).
• Smart Sat CRC Hyperspectral Remote Sensing of Seagrass and Coral Reefs 2023-2027.
• Developement of Underwater Field Spectrometry and Benthic Photo Collection and Analysis
• 3D GBR Habitat Mapping Project 2015 - ongoing:
• Global habitat mapping project 2019-2023 Allen Coral Atlas .

Current position: Associate Professior in Marine Remote Sensing leading the Marine Ecosystem Monitoring Lab. . Academic Director Heron Island Research Station and affiliated researchers with Centre for Marine Science and Centre for Biodiversity and Conservation Science

Capacity Building and Citizen Science: Capacity: under/post graduate courses; Msc/PhD supervision, workshops/courses; Remote Sensing Educational Toolkit, and online courses (e.g. TNC).Strong supporter of citizen science based projects, as trainer, organiser and advisor for Reef Check Australia, CoralWatch, Great Reef Census and UniDive.

Janet Wiles is a Professor in Human Centred Computing at the University of Queensland.

Her multidisciplinary team co-designs language technologies to support people living with dementia and their carers and social robots for applications in health, education, and neuroscience. She is currently developing a citizen science project which uses insights from neuroscience, AI and language technologies to explore the electrical characteristics of mycelial networks of symbiotic fungi in local ecosystems. She received her PhD in computer science from the University of Sydney and completed a postdoctoral fellowship in psychology. She has 30 years’ experience in research and teaching in machine learning, artificial intelligence, bio-inspired computation, complex systems, visualisation, language technologies and social robotics, leading teams that span engineering, humanities, social sciences and neuroscience. She currently teaches research methods for thesis and masters students, and is developing a new course in human-centred AI. Previous special interest courses include a cross disciplinary course ”Voyages in Language Technologies” that introduced computing students to the diversity of Indigenous and non-Indigenous languages, and state-of-the-art tools for deep learning and other analysis techniques for working with language data.

Featured projects

• Human-centred AI
• Florence communication technology
• For more on Human Centred Computing see the HCC projects page