Find Academic Supervisors

• 3D Computer Vision
• Machine Learning
• VR and AR
• +4 more

Professor Hongdong Li is a Tenured Professor at the School of Computing, Australian National University (ANU), within the College of Engineering and Computer Science. His research focuses on 3D Computer Vision, Machine Learning, and their applications in dynamic environments. He has held visiting roles at Carnegie Mellon University and has contributed to significant projects like the Australia Bionic Eyes initiative. Education: PhD (Electrical Engineering). Research Interests : 3D Computer Vision fundamentals and applied AI systems Learning-based 3D perception for plant sciences Robot navigation in unfamiliar environments Awards : Marr Prize Honourable Mention CVPR Best Paper Award Advising & Grants : Supervised 40+ PhD students, with funding from ARC, CSIRO, Microsoft, and firms like OPPO/Tencent. Active in projects such as bushfire detection via video analytics and sign language translation systems. Labs/Teams : Co-founder of the Australian Centre for Robotic Vision (ACRV). Collaborates globally on cross-view localization and autonomous systems.

Scott SissonView profile

Professor

• Computational Statistics
• Bayesian Statistics
• Big Data
• +4 more

Professor Scott Sisson is Director of the UNSW Data Science Hub (uDASH) and Professor of Statistics and Data Science at the University of New South Wales, School of Mathematics and Statistics. His research focuses on computational statistics, particularly solving 'intractable' statistical problems through Bayesian methods, big data techniques, simulation algorithms, and extreme value theory with environmental applications. Education includes a PhD in Statistics from Bristol University (2002), MSc in Environmental Statistics from Lancaster University (1997), and BSc in Mathematics and Statistics from Lancaster University (1996). Research interests span: Bayesian statistics and uncertainty quantification Big data analytics and scalable algorithms Machine learning integration with statistical methods Extreme value modeling for climate/environment Computational techniques for intractable problems Recent publications (2022-2025) demonstrate strong emphasis on Bayesian computation, spatiotemporal modeling, and interdisciplinary applications in materials science, oncology, quantum computing, transportation policy, and ecology. Methodological innovations include likelihood-free inference, modular Bayesian analyses, and symbolic data modeling. Awards and honors: 2020 Service Award (Statistical Society of Australia) 2017 ARC Future Fellowship 2015 G. N. Alexander Medal (Engineers Australia) 2011 Moran Medal (Australian Academy of Science) 2010 J.G. Russell Award (Australian Academy of Science) 2010 Queen Elizabeth II Research Fellowship As Director of uDASH, he leads data science initiatives across UNSW. He maintains sustained ARC funding and supervises students in computational statistics, Bayesian methods, extreme value theory, and machine learning. Professional service includes editorial roles for Statistics and Computing and past presidency of Statistical Society of Australia.

Massimo PiccardiView profile

Professor

• Natural Language Processing
• Computer Vision
• Machine Learning
• +3 more

Massimo Piccardi is a Professor of Natural Language Processing (NLP), Computer Vision, and Machine Learning at the University of Technology Sydney (UTS) , where he has been since 2002. He currently serves as the Head of the School of Electrical and Data Engineering and leads the Big Data Analytics program at the Global Big Data Technologies Centre. His research focuses on advancing NLP, machine learning applications in healthcare, and cybersecurity in IoT systems. He has authored over 200 journal papers and conference proceedings, secured significant ARC and CRC grants, and holds the IEEE Computer Society Distinguished Contributor Award (2022). Education & Professional Roles: Joined UTS in 2002, progressing from Associate Professor (2002–2007) to Professor (2008–present). Serves as Associate Editor for IEEE Transactions on Big Data and Editor for Artificial Intelligence in Medicine. Active in professional societies including IEEE, ACL, and ALTA (President, 2023–2024). Research Interests: Core areas include NLP (translation, summarization, adversarial attacks), healthcare informatics (clinical NLP, health service analysis), and cybersecurity (IoT security, privacy-preserving systems). Cross-cutting themes include generative models, cross-lingual systems, and ethical AI. Grants & Projects: Principal Investigator on ARC Discovery/Linkage projects and CRC grants. Recent projects include controllable machine translation (Amazon), privacy-preserving digital agriculture, and STEM innovation (ASTRID project with NBN Co). Labs & Collaborations: Leads the UTS Global Big Data Technologies Centre, collaborating on projects like adversarial NLP attacks, medical machine translation, and secure IoT frameworks.

Mohsen KalantariView profile

Associate Professor

• Geospatial Engineering
• Construction Engineering
• Building Information Modelling (BIM)
• +7 more

Associate Professor Mohsen Kalantari is a Geospatial Engineering academic at the University of New South Wales (UNSW) School of Civil and Environmental Engineering , with concurrent roles as co-founder of the startup Faramoon . His career spans roles at the University of Melbourne's Department of Infrastructure Engineering and Victorian government's land administration initiatives through DELWP. Education : PhD in Geomatics Engineering (2008, University of Melbourne), Master of GIS Engineering (2004), Bachelor of Surveying Engineering (2001) His research bridges geospatial engineering with construction automation , focusing on 3D cadastre , BIM-GIS integration , and smart cities . Recent publications show trends in underground land administration , digital twins , and LADM standard implementations . Scientific Awards : National educational recognition (2019), Victorian educational grants (2018), and prestigious fellowships (2012) As a supervisor , he guides PhD candidates in topics ranging from BIM for waste management to underground cadastral systems . His industry engagement includes partnerships with the United Nations , Open Geospatial Consortium , and Singapore Land Authority .

Yang SongView profile

Associate Professor

• Computer Vision
• Deep Learning
• Multimodal AI
• +7 more

Yang Song is an ARC Future Fellow and Scientia Associate Professor at the School of Computer Science and Engineering , University of New South Wales (UNSW) . She serves as Associate Head of School (Research) and Co-Director of iCinema , focusing on AI and Computer Vision applications for social good. Education: BEng in Computer Engineering (Nanyang Technological University, Singapore), PhD in Computer Science (UNSW, 2013) Research Areas: Biomedical image analysis, human-centred AI, graph data modeling, neuro-symbolic learning, and AI trustworthiness. Her work develops domain-specific deep learning models for radiological segmentation, histopathology cancer analysis, and 3D reconstruction. Recent projects address explainability in LLMs, fairness in AI, and human-robot interaction frameworks. With over 200 peer-reviewed publications in top venues like CVPR , MICCAI , and NeurIPS , her research spans biomedical imaging, robotics, and general multimodal AI. Scientific Awards include: 2024: ARC Industrial Transformation Research Hub for Human-Robot Teaming 2023: Google Inclusion Research Award 2022: NHMRC Ideas Grant for computational brain imaging 2021: UNSW Engineering Research Excellence Award 2020: Scientia Fellowship (UNSW) 2019: ARC Future Fellowship She supervises 24 current PhD/MPhil students and has graduated 15 advisees, including placements at Harvard University and Siemens Healthineers. Her grants include collaborations with Surf Life Saving Australia and industry partnerships for AI-driven solutions.

• Computer Vision
• Machine Learning
• Robotics
• +6 more

Dr. Feras Dayoub is a Senior Lecturer at the School of Computer and Mathematical Sciences (Faculty of Sciences, Engineering and Technology) at the University of Adelaide , specializing in Embodied AI and Robotic Vision within the Australian Institute for Machine Learning (AIML) . He co-directs the CROSSING French-Australian laboratory for human-autonomous agent teaming and holds an Adjunct position at the Queensland University of Technology (QUT) , serving as an Associate Investigator at its Centre for Robotics . Previously, he was a Chief Investigator at the ARC Centre of Excellence for Robotic Vision . His research focuses on advancing reliable deployment of computer vision and machine learning on mobile robots in real-world environments. Applied projects include agricultural automation , environmental conservation , and autonomous infrastructure monitoring . He has published extensively on topics like object detection , domain adaptation , 3D representation learning , and vision-language navigation , with a particular emphasis on robustness in dynamic and partially observed environments. Dr. Dayoub is also an educator specializing in programming , computer vision , and robotic perception . He contributes to open-source robotics research through tools like AARK (Autonomous Racing Toolkit) and has led teams developing solutions for precision agriculture (e.g., Deepfruits fruit detection system) and environmental monitoring (e.g., Crown-Of-Thorns starfish detection ). Key Collaborations : CROSSING Lab, QUT Centre for Robotics Research Themes : Embodied AI, Robust Perception, Domain Adaptation

Jian ZhangView profile

Professor

• Computer Vision
• Pattern Recognition
• Data Analytics
• +3 more

Prof. Jian Zhang is a Professor in the School of Electrical and Data Engineering at the University of Technology Sydney (UTS), specializing in computer vision, pattern recognition, and multimedia signal processing. He leads the Multimedia Data Analytics Lab at the Global Big Data Technologies Centre, focusing on agri-food sector applications such as livestock monitoring and AI-driven solutions for agricultural efficiency. Education : PhD, School of Information Technology and Electrical Engineering, University of New South Wales, 1999 MSc, The Flinders University of South Australia, 1994 BSc, East China Normal University, 1982 Research Interests : His work spans 2D/3D computer vision, large-scale image/video analytics, and cross-disciplinary projects in agriculture and remote sensing. He has pioneered AI systems for livestock counting, poultry welfare monitoring, and fish quality assessment, funded by organizations like Meat & Livestock Australia and Australian Eggs. Grants & Projects : Current projects include AI-based hen health monitoring ($5M+ funding since 2011) Collaborations with industry partners like Sydney Fish Market and Fremantle Port Students & Academic Leadership : Supervised 19 PhD graduates and 5 research fellows Recruiting new PhD candidates in computer vision and data analytics Labs & Teams : Director of the Multimedia Data Analytics Lab, collaborating with global experts through UTS's Distinguished Visiting Scholars program.

Maxime CordeilView profile

Senior Lecturer

• Data Visualisation
• Immersive Analytics
• Virtual Reality
• +5 more

Dr. Maxime Cordeil is a Senior Lecturer in Human Centred Computing at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on Virtual and Augmented Reality technologies for data interaction, interactive visualization systems, and AR interfaces for industry applications. He has authored over 60 publications in top-tier venues such as ACM CHI and IEEE VR, and was nominated as Australia's Field Leader in Computer Graphics in 2021 and 2022. Research Interests: Data visualization, immersive analytics, medical imaging, collaborative systems, and human-computer interaction. Current Projects: Includes embedded visualizations for sports performance, interactive machine learning in 3D environments, and mixed-reality applications in forensic science. PhD Supervision: Actively guiding students in topics like immersive gesture exploration, AR for digital health, and collaborative VR systems. His work bridges theory and practice, with tools like IATK (Immersive Analytics Toolkit) and the MADE-Axes hardware system. He collaborates with industry partners like Raytracer and CSIRO, focusing on applications in space exploration, underwater training, and remote operations. Awards: Multiple best paper recognitions at ISS and CHI, plus industry-driven research grants. Labs: Leads the Immersive Analytics research group at UQ, specializing in embodied interaction and spatial computing.

Xiaotian ZhengView profile

Assistant Professor

• Spatial Statistics
• Spatio-Temporal Statistics
• Point Process Modeling
• +5 more

Xiaotian Zheng is an Assistant Professor of Statistics at the University of Georgia. Previously, they were a Postdoctoral Research Fellow with the Australian Research Council Special Research Initiative Securing Antarctica's Environmental Future at the University of Wollongong, working under Professor Noel Cressie and Associate Professor Andrew Zammit-Mangion. They earned their Ph.D. in Statistical Science from the University of California, Santa Cruz, advised by Professors Athanasios Kottas and Bruno Sansó. Their research focuses on developing statistical and machine learning methods for analyzing complex, dependent data, particularly in ecological and environmental contexts. Key areas include spatial/spatio-temporal statistics, probabilistic downscaling, data integration, transfer learning, and statistical deep learning. Xiaotian's publications reflect their work on mixture transition distribution models, nearest-neighbor mixture models, and geostatistical frameworks for discrete-valued processes. These contributions emphasize Bayesian inference, computational efficiency, and real-world applications in environmental science and biodiversity modeling.

Sean R. ConnollyView profile

Research Professor

• Marine Biology
• Biodiversity
• Coral Reef Ecology
• +5 more

Sean R. Connolly is a Research Professor at the Smithsonian Tropical Research Institute (STRI) in Panama, where he leads the Connolly Lab focused on coral reef ecology and biodiversity dynamics. He joined STRI in late 2019 after approximately 20 years of research on Central Indo-Pacific coral reefs, and now directs his research toward the coral reefs of the Tropical East Pacific and Caribbean regions. Dr. Connolly's research interests center on understanding the mechanisms that maintain biodiversity in species-rich systems, particularly coral reefs. His work investigates how large numbers of species coexist despite similar resource requirements, what determines the commonness or rarity of different species, how fishing and marine reserves impact reef ecosystems, and how coral reefs respond to climate change. His approach combines empirical field studies with mathematical and statistical modeling across multiple spatial and temporal scales. His publication record demonstrates a strong focus on coral reef community dynamics, biodiversity patterns, climate change impacts, and fisheries management. Recent work increasingly addresses the effects of multiple stressors on reef ecosystems, with growing attention to the Tropical Eastern Pacific as a comparative natural laboratory. His collaborative research spans multiple institutions globally, reflecting the international nature of coral reef science. Dr. Connolly received his B.A. from Earlham College in 1994 and his Ph.D. from Stanford University in 1999. His educational background provided the foundation for his interdisciplinary approach that bridges theoretical ecology with practical conservation applications. His research program currently investigates reef ecosystems on both sides of the Isthmus of Panama, taking advantage of the dramatically different reef conditions in the Tropical East Pacific (where one coral group dominates marginal reef conditions) and the Caribbean (where former community dominants have collapsed to a fraction of historical abundances). This comparative approach provides unique insights into the mechanisms of biodiversity maintenance and ecosystem resilience.

• Structural Health Monitoring
• Steel-Concrete Composite Structures
• Physics-Informed Machine Learning
• +6 more

Dr. Xinqun Zhu is an Associate Professor at the University of Technology Sydney (UTS) in the School of Civil and Environmental Engineering . He has held academic positions at Western Sydney University (2016-2017), University of Western Australia (2005-2009), and University of Manchester (2001-2005). His research spans structural health monitoring, steel-concrete composite structures, physics-informed machine learning, and advanced sensor systems.

• Cognitive Science
• Quantum Theory in Information Modeling
• Learning Analytics
• +6 more

Kirsty Kitto is an Associate Professor at the University of Technology Sydney in the Learning Design and Technology Unit. With a background in theoretical physics and computer science, her research focuses on developing quantum-inspired models of contextuality to understand complex human behavior in educational systems. She explores the intersection of Artificial Intelligence (AI) , Learning Analytics , and Educational Theory , seeking to bridge the divide between big data and pedagogical frameworks. Current Appointments : Associate Professor (2020-present), Senior Lecturer (2017-2020) Past Academic Roles : Senior Research Fellow (QUT), Senior Lecturer (QUT), Associate Lecturer (Flinders University) Her funded research includes projects on: Quantum models in cognitive systems (ARC Fellowship DP1094974) AI literacy in digital workplaces (IEEE Transactions study) Contextual learning analytics (DVC Education and Students Division) Skills passports for lifelong learning (UMAP 2024 paper) Kitto's recent work examines human-AI interaction in writing assessment, data storytelling for teacher-centered analytics, and causal modeling to strengthen educational theory-data connections. She actively supervises Masters and PhD students and contributes to policy debates through government submissions on generative AI in education. Her methodology combines mathematical formalism with sociotechnical analysis to address educational complexity.

• Artificial Intelligence
• Image Analysis
• Computer Vision
• +7 more

Dr. Wenjing Jia is an Associate Professor at the University of Technology Sydney (UTS), affiliated with the School of Electrical and Data Engineering within the Faculty of Engineering and IT. She holds a PhD in Computing Sciences (UTS, 2007), Master's in Communications and Information Systems (Fuzhou University, 2002), and a Bachelor's in Communications Engineering (Jilin University, 1999). Her research focuses on image analysis, computer vision, and AI applications in healthcare, transport, and defense. Key areas include text detection in challenging environments, medical image super-resolution, and crowd surveillance systems. She leads projects with industry partnerships, securing over $900K in funding. Dr. Jia is also a recognized educator with 12+ years of teaching experience, specializing in internetworking subjects. She organizes international conferences (e.g., ICDAR2019, TrustCom-2017) and serves as a Cisco Certified Instructor Trainer. Awards include the Science and Technology Award and a finalist spot in the Cisco Women in IT Academia Award. Education: PhD in Computing Sciences, UTS (2007) MSc in Communications and Information Systems, Fuzhou University (2002) BEng in Communications Engineering, Jilin University (1999) Research Highlights: Developed algorithms for low-light text detection and medical image enhancement Advanced crowd counting and violence detection in surveillance systems Contributions to OCT image super-resolution and LiDAR point cloud analysis Teaching & Leadership: Lead CI of Teaching & Learning grants Legal Main Contact for UTS Cisco Networking Academy Deputy Head - Teaching and Learning (secondee) Awards: Excellent Thesis Award, Science and Technology Award (2019), and recognition in Women in IT Academia. Her work bridges academia and industry, with over 130 publications and active roles in conference organization and technology transfer.

Feng ChenView profile

Associate Professor

• Nonparametric and semiparametric statistics
• Point processes and self-exciting processes
• Statistical computing
• +3 more

Associate Professor Feng Chen is a faculty member at the School of Mathematics & Statistics, University of New South Wales, specializing in statistical methodology development and applications. His research bridges theoretical statistics and practical implementations across financial modeling, spatiotemporal processes, and public health analysis. PhD in Statistics from University of Hong Kong (2008) MSc in Applied Probability & Statistics from Lanzhou University (2004) BSc in Mathematics from Lanzhou University (2001) Research focuses include: Nonparametric and semiparametric statistical methods Point process modeling with emphasis on Hawkes processes Statistical computing and algorithm development Applications to financial data, earthquake analysis, and public health Recent publications demonstrate methodological advances in: Hawkes process estimation with complex data structures Renewal process applications in seismology GARCH modeling with missing data Spatiotemporal clustering analysis Scientific recognition includes: UNSW Science Staff Impact Award (2023) Professional roles: Director of Research Postgraduate Studies (2023--) Associate Editor for multiple journals Statistics Honours Coordinator (2013-2018) Active participant in statistical societies

Sahani PathirajaView profile

Lecturer

• Data Science
• Bayesian Inference
• Stochastic Analysis
• +7 more

Dr. Sahani Pathiraja is a Lecturer (tenure track assistant professor) at UNSW Sydney , specializing in Data Science . Her research bridges mathematical and statistical foundations with practical applications in environmental and biomedical sciences. Research Focus : Sequential Bayesian inference, Monte Carlo methods, stochastic analysis of non-linear filtering, uncertainty quantification, and real-time parameter estimation. Current Projects : Co-investigator in the ARC Industrial Transformation Training Centre: Data Analytics for Resources and Environment (DARE) and the Next Generation Graduate Program (NGGP) in Sports Data Science and AI . Research Supervision : Dr. Pathiraja supervises PhD students in areas including: Bayesian inference Stochastic differential equations Data assimilation Non-linear filtering Scientific Collaborations : Her work intersects with environmental science, biomedical applications, and machine learning. Projects include stochastic hydrology, SDEs, and operator learning for environmental systems. Contact Information : Email: s.pathiraja@unsw.edu.au Phone: +61 2 8065 0836 Office: Room 2070, Level 2, The Red Centre, UNSW Sydney