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Shakes an imaging expert that leads a strong deep learning, artificial intelligence (AI) focused research team interested in medical image analysis and signal/image processing applied to many areas of science and medicine. He received his Ph.D in Theoretical Physics from Monash University, Melbourne and has been involved in applying machine learning in medical imaging for over a decade.

Shakes’ past work has involved developing shape model-based algorithms for knee, hip and shoulder joint segmentation that is being developed and deployed as a product on the Siemens syngo.via platform. More recent work involves deep learning based algorithms for semantic segmentation and manifold learning of imaging data. Broadly, he is interested in understanding and developing the mathematical basis of imaging, image analysis algorithms and physical systems. He has developed algorithms that utilise exotic mathematical structures such as fractals, turbulence, group theoretic concepts and number theory in the image processing approaches that he has developed.

He is currently a Senior Lecturer and leads a team of 20+ researchers working image analysis and AI research across healthcare and medicine. He currently teaches the computer science courses Theory of Computation and Pattern Recognition and Analysis.

Jen Jen Chung is an Associate Professor in Mechatronics within the School of Information Technology and Electrical Engineering at The University of Queensland. Her current research interests include perception, planning and learning for robotic mobile manipulation, algorithms for robot navigation through human crowds, informative path planning and adaptive sampling. Prior to working at UQ, Jen Jen was a Senior Researcher in the Autonomous Systems Lab (ASL) at ETH Zürich from 2018-2022 and was a Postdoctoral Scholar at Oregon State University researching multiagent learning methods from 2014-2017. She completed her Ph.D. on information-based exploration-exploitation strategies for autonomous soaring platforms at the Australian Centre for Field Robotics in the University of Sydney. She received her Ph.D. (2014) and B.E. (2010) from the University of Sydney.

Shane Culpepper is Professor of Artificial Intelligence at the University of Queensland in St. Lucia, Australia. Before joining the University of Queensland in 2023, Professor Culpeper held a continuing academic position at RMIT University in Melbourne, Australia. He received his PhD in Computer Science from the University of Melbourn in 2008. His research focuses primarily on building better Search and Recommendation Systems. Over his 16 year career, Professor Culpepper has supervised 19 PhD students and co-authored more than 120 peer reviewed papers with 127 different research collaborators on problems such as algorithm efficiency and scalability, new machine learning algorithms for search and recommendation systems, and evaluating search and recommendation engine quality. Professor Culpepper is also an active member in the international research community. In the last 5 years, he 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 Princpal 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 reseach grants totalling ~$3.5 Million AUD. For more information, see his personal hoomepage.

Dr Trish Gilholm is a dedicated data scientist, statistician and early-career researcher within the Children's Intensive Care Research Program, Child Health Research Centre. Her research focuses on the application of advanced statistical methods and machine learning techniques to paediatric critical care. With a PhD in Statistics from Queensland University of Technology, her research focuses on personalised predictive modelling to improve outcomes for critically ill children. Dr Gilholm has made significant contributions through her work on projects that include the evaluation of sepsis screening tools, the use of adaptive designs in paediatric critical care clinical trials, and machine learning models to predict long-term educational and developmental outcomes for PICU survivors.

Dr. Gilholm's work has been recognised with numerous awards, including the Executive Dean Commendation for Outstanding Doctoral Thesis Award and Best Oral Presentation at the 2022 and 2021 Children’s Health Research Symposiums. She has published her work in leading journals including Intensive Care Medicine and Pediatric Critical Care Medicine. She is actively involved in mentoring, teaching, and supervision of research students.

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.

Dr Jessica Korte is passionate about the ways good technology can improve lives. To ensure technology is “good”, she advocates involving end users in the design process; especially when those people belong to “difficult” user groups - which usually translates to “minority” user groups. Her philosophy for technology design (and life in general) is that the needs of people who are disempowered or disabled by society should be considered first; everyone else will then benefit from technology that maximises usability. Her research areas include Human-Computer Interaction, Machine Learning, and Participatory & Collaborative Design.

Jessica was drawn to research by a desire to explore some of the ways technology and design can empower and support people from marginalised groups. She has worked with Deaf children and members of the Deaf community to create a technology design approach, and successfully organised and run international workshops on Pushing the Boundaries of Participatory Design, leading to the World’s Most Inclusive Distributed Participatory Design Project.

Jessica has recently been awarded a TAS DCRC Fellowship to create an Auslan Communication Technologies Pipeline, a modular, AI-based Auslan-in, Auslan-out system capable of recognising, processing and producing Auslan signing.

Jessica is currently looking to recruit research students with an interest in exploring topics in an Auslan context, including machine learning, natural language processing, chatbots, video GAN, or procedural animation.

Gayan Lankeshwara is a postdoctoral research fellow in the Power, Energy and Control Engineering research group at The University of Queensland, Australia. He received the B.Sc.Eng. (Hons.) and M.Sc.Eng. degrees in electrical and electronic engineering from the University of Peradeniya, Sri Lanka, in 2016 and 2021, and a Ph.D. degree in electrical engineering from The University of Queensland, Brisbane, QLD, Australia, in 2023. His research interests include grid integration of distributed energy resources, active distribution network management and machine learning applications in power systems.

Dr Kai Li Lim is the inaugural St Baker Fellow in E-Mobility at the UQ Dow Centre for Sustainable Engineering Innovation. Specialising in data science, engineering, and emerging technologies, Dr Lim focuses on real-time vehicle telematics, infrastructure management, and computer vision-based autonomous driving.

At UQ, Dr Lim's research centres on electric vehicle (EV) usage and charging patterns to inform adoption policies and strategies. His work includes examining trends for incentive design and assessing the environmental and economic impacts of EVs. Dr Lim's current focus is on charging reliability and addressing EV drivers' pain points. His research has been featured in academic, industry, and media publications, facilitating discussions with various stakeholders.

Dr Lim has published a range of articles, book chapters, and conference papers in reputable venues. He has delivered invited talks and appeared in media outlets such as ABC, Courier Mail, and The Conversation. Collaborating with various UQ schools, including Civil Engineering, Electrical Engineering and Computer Science (EECS), Economics, and Environment, Dr Lim has secured funding for projects on topics like carbon emissions offset after EV uptake and evaluating price incentives for EV charging using real-time data.

In addition to his work at UQ, Dr Lim collaborates closely with the UC Davis Electric Vehicle Research Center, where he recently completed a six-month visiting fellowship on EV charging. He engages in speaking events and networking opportunities centred on sustainability and transportation innovation, delivering keynote speeches at conferences and industry roundtables.

Dr Lim holds a BEng (Hons) degree in electronic and computer engineering from the University of Nottingham, an MSc degree in computer science from Lancaster University, and a PhD degree from The University of Western Australia, supported by the Australian Government under the Research Training Programme.

Dr Moni holds a PhD in Artificial Intelligence & Data Science in 2014 from the University of Cambridge, UK followed by postdoctoral training at the University of New South Wales, University of Sydney Vice-chancellor fellowship, and Senior Data Scientist at the University of Oxford. Dr Moni then joined UQ in 2021. He also worked as an assistant professor and lecturer in two universities (PUST and JKKNIU) from 2007 to 2011. He is an Artificial Intelligence, Computer Vision & Machine learning, Digital Health Data Science, Health Informatics and Bioinformatics researcher developing interpretable and clinical applicable machine learning and deep learning models to increase the performance and transparency of AI-based automated decision-making systems.

His research interests include quantifying and extracting actionable knowledge from data to solve real-world problems and giving humans explainable AI models through feature visualisation and attribution methods. He has applied these techniques to various multi-disciplinary applications such as medical imaging including stroke MRI/fMRI imaging, real-time cancer imaging. He led and managed significant research programs in developing machine-learning, deep-learning and translational data science models, and software tools to aid the diagnosis and prediction of disease outcomes, particularly for hard-to-manage complex and chronic diseases. His research interest also includes developing Data Science, machine learning and deep learning algorithms, models and software tools utilising different types of data, especially medical images, neuroimaging (MRI, fMRI, Ultrasound, X-Ray), EEG, ECG, Bioinformatics, and secondary usage of routinely collected data.

• I am currently recruiting graduate students. Check out Available Projects for details. Open to both Domestic and International students.

Dr. Morteza Namvar is a senior lecturer at UQ Business School, specializing in Business Information Systems. With a background in computer science and IT engineering, he brings valuable expertise to his research on Machine Learning (ML) and Natural Language Processing (NLP) in business settings. Morteza is passionate about exploring the applications of ML, NLP and LLM in organizational contexts. In his research program, he mentors several PhD and HDR students in leveraging these technologies to drive innovation and efficiency across various business domains. He has successfully secured funding for multiple ML and NLP projects and has disseminated his findings through publications in esteemed journals and conferences in IS and computer science. Dedicated to cultivating the next generation of ML enthusiasts, Morteza’s teaching focuses on ML development using Python, equipping students with the skills and confidence needed to thrive in the dynamic field of ML.

Dr Sathish Periyasamy is a Research Fellow at a Queensland Brain Institute and Senior Scientist at Queensland Centre for Mental Health Research. He is currently building and focusing on Systems/Genomic Medicine and conducting research in the interface of systems genetics and psychiatry. He is involved in studying the mechanisms of (patho-)physiological processes in psychiatric disorders using a unique combination of educational experience coupled with over twenty-five years of computer programming and eleven years of computational biology experience in biomedicine. Over the past 20 years, his experience working in chemical, biological and medical domains has enabled him to focus on the interface of basic and clinical research and contribute to translational research. From 2011 to 2014, he was involved in cancer genetics research at King Abdullah International Medical Research Centre, KSA. As the bioinformatics lead, with interdisciplinary skills and expertise at the interface of computational intelligence, systems biology, and quantitative/psychiatric genetics, He has been contributing to psychiatric genetics research since 2014 in Professor Bryan Mowry’s lab.

His current research areas include:

• Bioinformatics, Systems Biology and Statistical Genetics - Developing and applying GWAS, post-GWAS bioinformatics, cross-population genetic association and systems genetics approaches.
• Psychiatric Genomics
  • Common and rare variant association studies in schizophrenia using data generated from DNA microarray and whole-exome/whole-genome sequencing technologies.
  • Post-GWAS bioinformatics approaches to characterise risk variants discovered in schizophrenia GWAS.
  • Cross-population genetic association approaches in schizophrenia.
• Computational Intelligence – Developing conventional and visible deep learning models for biomedicine.
• Developing genetic resources for Indigenous Oceanic populations

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.

Current projects:

1) Long term monitoring of benthic composition at Heron Reef (2002-ongoing). Annual photoquadrate surveys are being collected at Heron Reef, Southern Great Barrier Reef. Initiated to develop remote sensing mapping approaches and assess coral composition over time. The resulting Maps, photo quadrate and benthic data, spectral reflectance are accessible online.

2) Long term monitoring of seagrass composition and abundance in Moreton bay Marine Park (2000-ongoing). For Eastern Banks it included monitoring seagrass species, cover and biomass 15x times since 2004 using photoquadrate survey and satelite imagery and for Moreton Bay it included seagrass extent and cover (2004, 2009, 2015, 2021, 2022), all data accessible via Moreton Bay Research Station.

3) Smart Sat CRC Hyperspectral Remote Sensing of Seagrass and Coral Reefs 2023-2027. Collaborative effort with CSIRO, Adelaide University, DES Adelaide Coastal Waters.

4) 3D GBR Habitat Mapping Project 2015 - ongoing: Mapping and monitoring geomorphic zonation, bottom type and predicted coral type habitat for every Great Barrier Reef within the Marine Park.

5) Global habitat mapping project 2019-2023 Developed and implemention of global habitat mapping as part of the Allen Coral Atlas resulting in extent, geomorphic and benthic maps for reefs globally, funded through with Vulcan Philanthropies in partnership with; Planet; the Arizona State University and the National Geographic Society.

Other projects: Advisor for Reef Cloud Australian Institute of Marine Science and Coordinated Global Research Assessment of Seagrass System (C-GRASS).

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

Capacity building: under/post graduate courses; Msc/PhD supervision, workshops/courses; Remote Sensing Educational Toolkit, and online courses (e.g. TNC)

Citizen science: Strong supporter of citizen science based projects, as trainer, organiser and advisor for Reef Check Australia, CoralWatch, Great Reef Census and UniDive.

Dr Sen Wang is an ARC DECRA Senior Research Fellow and Senior Lecturer in computer science and data science at the School of Information Technology and Electrical Engineering at UQ. He is also a CI on several health data analytics research grants. Sen has an interest in ICU data and has clinical collaborations with RBWH and Children’s Hospital. Dr Wang received his PhD degree in 2014 and his research interest includes various topics on Feature Selection, Semi-supervised Learning, Deep Learning, Pattern Recognition, Data Mining, and Health Informatics. Since 2010, Dr Wang has published 80+ academic papers in top conferences and journals. Most were published in internationally renowned journals and conferences in the fields of data science, data mining, and machine learning, such as Algorithmica, TNNLS, TMC, TKDE, TCYB, TMM, WWWJ, Signal Processing, ACM TOMM, ACM MM, IJCAI, AAAI, SDM, CIKM, CVPR, ICCV, ICDM, ISWC, ECML-PKDD, PAKDD, ICONIP, ICPADS, and WISE, all CORE A/A* journals and conferences.

Janet Wiles is a Professor in Human Centred Computing at the University of Queensland and leads the Future Technologies Thread of the ARC Centre of Excellence for the Dynamics of Language (CoEDL).

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 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 a cross disciplinary course ”Voyages in Language Technologies” that introduces computing students to the diversity of the worlds 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