Denzil Fiebig is Professor in the School of Economics at UNSW Business School since 2001, with prior appointments including chair positions in econometrics at the University of Sydney. His international engagements include visiting roles at the University of Florida, University of Southern California, Tilburg University, Victoria University of Wellington, University of York, and Erasmus University. His research focuses on econometric methodology and applied health economics, particularly healthcare utilization and policy analysis. Recent publications demonstrate methodological innovation in analyzing healthcare systems, insurance markets, and economic behavior during health shocks. His work employs advanced econometric techniques including panel data models and discrete choice experiments to study physician behavior, health financing, and socioeconomic disparities in healthcare access.
Thomas Suesse is a Senior Lecturer at the School of Mathematics and Applied Statistics, University of Wollongong, with a career spanning institutions in Germany, New Zealand, and Australia. His academic journey includes a M.Sc. in Mathematics from Friedrich-Schiller-University Jena (2003) and a PhD in Statistics from Victoria University of Wellington (2008). Education: M.Sc. in Mathematics (FSU Jena, 2003), PhD in Statistics (VUW, 2008) His research focuses on Statistics, particularly Categorical Data Analysis, Social Network Modeling, and Spatial Statistics. Recent work explores variational Bayes inference for spatial autoregressive models, environmental extreme value detection algorithms, and the impact of missing data in statistical models. His publications span topics in public health, environmental science, and educational methodology. Key trends in his 15 most recent articles (2021-2025) include advancements in spatial statistics, applications of social network analysis to finance, and educational research comparing laboratory teaching objectives. He has also contributed to understanding pandemic effects on child health and activity patterns. Scientific Awards: 2008 VUW PhD thesis submission award; 2005-2008 VUW Postgraduate Scholarship. Thomas co-supervises PhD students on topics like spatial autoregressive modeling with missing data and variance estimation in mixture models. He has secured internal grants for computational infrastructure upgrades and studies on child movement behaviors. His professional affiliations include the Statistical Society of Australia.
Roger Fulton is a Professor of Medical and Preclinical Imaging at the University of Sydney and a Principal Nuclear Medicine Physics Specialist at Westmead Hospital's Department of Medical Physics. He is part of the Brain and Mind Centre and leads the Physics and Biomodelling team. His roles include Conjoint Professor in Medical Radiation Sciences, RHD student supervision, and coordination of the MRTY5134 (Computed Tomography Theory) and MRTY2107 (Imaging Technology 2) units of study. He holds a PhD in Applied Physics. His career has spanned collaborations with institutions like the Research Center Julich (Germany), Massachusetts General Hospital (USA), and the International Atomic Energy Agency (Austria). Professional recognitions include Fellow of ACPSEM, Senior IEEE Membership, and Chair of the Nuclear Medical Imaging Sciences Council (IEEE). Research interests focus on motion tracking/correction for SPECT, PET, and CT imaging, including patent innovations and leadership in radiation dose reduction techniques. He has secured $9.2M in competitive grants since 2004 and contributed to global standards through IAEA initiatives. Grants and advising: Over $9.2M in grants since 2004, advising PhD student Bader ALOUFI on liver imaging kinetics. His work bridges hardware engineering (e.g., Intel RealSense depth cameras) with software solutions (neural networks, Bayesian methods) to enhance imaging accuracy and reduce patient motion artifacts. Scientific Awards: US Patent (2020) ARC College of Experts (2016–2018) ACPSEM Fellowship IEEE Senior Membership IEEE Nuclear and Plasma Sciences Society leadership Labs/Teams: Physics and Biomodelling team at the Brain and Mind Centre, collaborating with international groups on preclinical imaging systems and awake-animal PET methodologies.
Dr. Xinyu Zhang is a Research Fellow at the Australian Institute for Machine Learning (AIML), University of Adelaide's Faculty of Sciences, Engineering and Technology. Her research bridges computer vision and machine learning, focusing on image/video generation, self-supervised learning, and multimodal retrieval for human-centric AI applications. Zhang's current investigations include: Causal representation learning and multimodal integration Bayesian deep learning frameworks Video generation with temporal consistency Lightweight detection transformers Unsupervised person re-identification Analysis of recent publications reveals strong emphases on generative modeling innovations (especially video synthesis), efficient transformer architectures for real-time applications, and self-supervised representation learning. Her work frequently addresses the alignment between latent representations and human perception across vision-language tasks. Dr. Zhang co-supervises graduate students on projects involving multi-agent 3D scene generation and knowledge transfer in low-supervision learning. She serves as conference reviewer for premier venues including CVPR, ICCV, and NeurIPS, contributing to the advancement of computer vision research.
Dr. Elliot Carr is a Senior Lecturer in the School of Mathematical Sciences at Queensland University of Technology (QUT), Faculty of Science. He holds a PhD in Mathematics from QUT and has been a faculty member since 2015, progressing from Lecturer to his current rank. His research and teaching focus on applied and computational mathematics, with strong interdisciplinary applications. Education: PhD in Mathematics, Queensland University of Technology, 2009–2012 Bachelor of Applied Science (Honours) in Mathematics, QUT, 2008 Bachelor of Mathematics, QUT, 2005–2007 Elliot Carr's research lies at the intersection of applied mathematics and real-world physical systems. His work centers on developing and analyzing mathematical models of advection, diffusion, and reaction processes, particularly in heterogeneous media. He employs both deterministic (PDE-based) and stochastic (random walk) frameworks, contributing to analytical solutions, multiscale modeling, surrogate models, and numerical methods such as finite volume and Newton-Krylov techniques. His research has been applied to diverse fields including groundwater contamination, drug delivery, heat transfer, and tumor spheroid modeling. The latest publications reflect a consistent focus on transport phenomena in complex geometries and heterogeneous environments. Key themes include dual-grid mapping for contaminant transport, analytical modeling of drug release from spherical capsules, thermal diffusivity in shell geometries, and stochastic models of biological systems. His methodological contributions span analytical, numerical, and statistical approaches, demonstrating versatility across applied mathematics. Scientific Awards and Recognitions: JH Michell Medal, ANZIAM (2022) ARC DECRA Fellowship (2015) QUT Outstanding Doctoral Thesis Award (2012) University Medal, QUT (2008) Dean’s Award for top graduate in both Honours and Bachelor programs Keynote and plenary speaker at major conferences including ANZIAM and Forum “Math-for-Industry” Dr. Carr actively supervises PhD and Masters students, with completed and ongoing projects on diffusive transport, tumor modeling, and sports analytics. He has secured competitive research funding, including an ARC Discovery Project on multiscale modeling. His teaching includes computational mathematics, linear algebra, and differential equations, with a focus on MATLAB-based implementation. He is a member of the Australian Mathematical Society (AustMS) and ANZIAM. Research Labs and Teams: While not explicitly tied to a named lab, Carr is part of the broader Applied Modelling and Computation research environment at QUT. He collaborates extensively with researchers such as Ian Turner, Matthew Simpson, and Chris Drovandi, contributing to interdisciplinary teams in mathematical biology, environmental modeling, and statistical computation.
Michael Biercuk is a Professor and Director of the Quantum Control Laboratory at the University of Sydney. He holds a dual role as founder and CEO of Q-CTRL, a quantum technology company. His academic work focuses on quantum control, quantum firmware, and trapped ion systems, with applications in quantum computing, quantum metrology, and quantum simulation. Biercuk earned his undergraduate degree from the University of Pennsylvania and his Master's and PhD from Harvard University. He has held research fellowships at NIST Boulder and advised agencies like DARPA. Education: BA (University of Pennsylvania), MSc/PhD (Harvard University) Research interests include developing quantum control techniques to suppress errors in qubits, engineering quantum firmware for scalable systems, and exploring trapped ion-based quantum sensors. His lab combines theory and experiment, leveraging ultra-high-vacuum systems and precision lasers to study quantum coherence. Awards include the 2021 Australian Financial Review 'Most Innovative Companies' recognition, 2015 Eureka Prize for Outstanding Early Career Researcher, and multiple innovation accolades. His work bridges academia and industry, with collaborations spanning Tsinghua University, MIT, and NIST. Key Projects: Quantum Control & Firmware, Quantum Simulation of Many-Body Systems, Quantum Metrology with Ions
Robin Harper is an academic staff member at the University of Sydney , affiliated with the Sydney Nanoscience Hub . His research focuses on quantum computing , particularly in quantum error correction , noise characterization , and Bayesian tomography . Recent work includes: Developing scalable methods for quantum noise estimation (2025) Advancing non-Markovian process characterization (2025) Optimizing error correction circuits (2025) His publications highlight machine learning applications in quantum systems and pulse engineering for silicon qubits. Awards include the Gold Human-Competitive Award at GECCO 2017 . Harper contributes to experimental quantum research with grants like the 2023 Entangled HEX project, aiming to build superconducting logical qubits on a heavy hexagonal lattice.
Dr. Michael Burke is a Senior Lecturer and Deputy Graduate School Coordinator at the Department of Electrical and Computer Systems Engineering, Monash University. He specializes in robotics, focusing on probabilistic machine learning and computer vision for autonomous systems. His roles include research supervision, curriculum development, and fostering graduate culture. Education: PhD in Statistical Signal Processing, University of Cambridge (2012–2016) MSc in Electronic Engineering, Stellenbosch University (2009–2011) BEng in Electronic Engineering, University of Pretoria (2005–2008) Research Interests: Robot learning and control Probabilistic machine learning for robotics Interpretable robotics systems and hybrid control architectures Robot perception and visuomotor control His work bridges robotics with machine learning to develop safe, adaptive autonomous systems for applications like surgical robotics and field robotics. Awards and Recognition: Best Paper Award at AMDO 2014 Runner-Up for Best Paper at CoRL 2019 Advising & Grants: Supervising over 20 PhD and Master’s students in robotics and related fields Principal Investigator for ARC-funded projects on robotic pharmaceutical formulation and human-robot interaction (2024–2027) Labs and Teams: Past leadership of the Mobile Intelligent Autonomous Systems group at CSIR, South Africa (2009–2018) Current collaborations with researchers at the University of Edinburgh and University of Melbourne
Grant Hamilton is a Professor in Ecology at Queensland University of Technology (QUT), affiliated with the School of Biology & Environmental Science within the Faculty of Science. His research focuses on ecological analytics, conservation science, and the application of unmanned aerial systems (UAVs) for wildlife surveillance, invasive species management, and agricultural systems. He holds a Doctor of Philosophy from QUT and is a member of the Ecological Society of Australia and the Modelling and Simulation Society of Australia and New Zealand. Research interests include detection and surveillance technologies, spatial analytics, and the integration of artificial intelligence with ecological monitoring. Notable projects include the Yurol Ringtail State Forest Koala Baseline and Monitoring Project, Agri-Intelligence in Cotton Production Systems, and automated wildlife detection using drones. His work bridges ecological theory with practical applications in conservation, agriculture, and biosecurity. Publications span topics like UAV-based wildlife detection, statistical modelling for pest surveillance, and Bayesian networks for environmental management. He has advised multiple PhD and master’s students on projects related to pest invasions, agricultural systems, and biodiversity monitoring. Current grants include Australian Competitive Grants focusing on digital agriculture and cotton production systems.
Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).
Surya Nurzaman is a Senior Lecturer at Monash University Malaysia, specializing in soft robotics, embodied intelligence, and bio-inspired systems. He holds a PhD from Osaka University (2011) and has held research fellowships at ETH Zürich and the University of Cambridge. His work bridges robotics engineering with biomedical applications, emphasizing interdisciplinary collaboration. He teaches courses such as Dynamics II, Electromechanics, and Engineering Design. Research focuses on soft robotics for industrial and biomedical applications, including soft grippers, exoskeletons, and adaptive control systems. Projects include aerial robotics for oilfield inspection and AI-driven sensor frameworks. Nurzaman has received awards like the ITEX 2021 Gold Medal and the 2024 School of Engineering Excellence Award. He is actively involved in editorial roles for journals like IEEE Robotics & Automation Magazine and Frontiers in Robotics and AI. His contributions span over 50 publications, with recent work addressing tremor prediction, soft sensor modeling, and cross-domain learning. Collaborations include international partners in Japan, Switzerland, and the UK. Nurzaman’s research aligns with UN SDGs, particularly in advancing sustainable industry solutions and health innovations.
Hugh Durrant-Whyte is a Professor at the University of Sydney and Director of the Centre for Translational Data Science. He holds a BSc (Eng) from the University of London, an MSE, and a PhD from the University of Pennsylvania. Previously, he served as CEO of National ICT Australia (NICTA) and Director of the Australian Centre for Field Robotics (ACFR). His research focuses on robotics, autonomous systems, and data fusion, with over 350 publications and four successful startups. Education: BSc (Eng) in Engineering, University of London MSE in Robotics, University of Pennsylvania PhD in Robotics, University of Pennsylvania Affiliations: Director, Centre for Translational Data Science Former CEO, NICTA (2010-2014) Former Director, ACFR (1995-2010) His research interests span robotics, autonomous systems, and sensor networks. Notable contributions include foundational work on SLAM (Simultaneous Localization and Mapping) and decentralized data fusion. He has pioneered applications in mining automation, autonomous vehicles, and environmental modeling. His work emphasizes practical real-world systems, with projects like autonomous straddle carriers for container terminals and terrain mapping for mining operations. Awards & Honors: NSW Scientist of the Year (2010) Fellow of the Royal Society (FRS), Australian Academy of Science (FAA), and IEEE (FIEEE) Recipient of multiple IEEE Best Paper awards Grants & Labs: Leadership in securing multi-million-dollar grants for robotics and data science initiatives Centre for Translational Data Science: Focuses on translating data science into real-world impact He advises on numerous government and industry projects, bridging academic research with industrial applications. His research teams have developed influential algorithms for autonomous navigation and multi-agent systems.
Dr. Shan Huang is a Researcher at the School of Engineering, University of Newcastle. She holds a BE in Civil Engineering from Hunan University of Science and Technology, an MS in Road and Railway Engineering from Central South University, and a PhD in Civil Engineering from the University of Newcastle. Her research focuses on computational geomechanics and probabilistic geotechnics, particularly in soft soil consolidation and geotechnical risk assessment. Dr. Huang's work integrates numerical simulation and advanced probabilistic methods, with notable contributions to Bayesian back analysis for settlement prediction and parameter calibration in soft soils. Her research has been published in journals such as Computers and Geotechnics , ASCE Journal of Geotechnical and Geoenvironmental Engineering , and Soils and Foundations . Key projects include the analysis of embankments in Ballina, Australia, where she applied Bayesian methods to predict long-term settlements using monitored data. Her work emphasizes computational efficiency and practical applications in geotechnical engineering.
Anton Westveld is a Senior Lecturer in the Department of Statistics at the Australian National University (ANU), within the Research School of Finance, Actuarial Studies & Statistics (RSFAS). He also serves as an Affiliate Associate Professor at Virginia Commonwealth University since August 2023. His research focuses on Bayesian methodology, network analysis, game theoretic data, and statistical causality, with notable contributions to ecological modeling and agent-based stochastic simulations. Westveld holds a Bachelor’s in Economics and Political Science from the University of Michigan (Ann Arbor), a Master’s in Applied Economics and Statistics from the same institution, and a PhD in Statistics from the University of Washington. His work has been published in prestigious journals like the Annals of Applied Statistics and Proceedings of the National Academy of Sciences . His research interests span Bayesian inference, relational data analysis, and causal modeling, with applications in ecological and health sciences. Recent work includes developing Bayesian methods for ecological drivers in marine viral communities and latent socioeconomic health indices for policy evaluation. Notable articles include analyses of menstrual disorder surveys using Gaussian copulas, ecological metagenomics studies, and Bayesian-optimized bootstrap techniques for uncertainty quantification. His interdisciplinary collaborations bridge statistics with environmental science, public health, and economics.
Dr. Tom James Stindl is a Lecturer and statistician at the School of Mathematics and Statistics, UNSW Sydney. His work centers on point process models, particularly renewal Hawkes processes, with applications across finance, seismology, crime analysis, and bushfire modeling. He earned his Ph.D. in statistical inference for self-exciting point processes under Dr. Feng Chen at UNSW Sydney. Supervises PhD, MRes, and Honours students (e.g., Jason Lambe, Zhe Han) Research focuses on computational statistics, Hawkes processes, and Bayesian/non-parametric methods Key grant: "Inference for Hawkes processes with challenging data" (Australian Research Council, 2024-2026) His recent publications analyze statistical inference techniques for point process models, though specific titles aren't listed here. Teaching includes courses like Statistical Inference and Statistical Modelling and Computing . Contact: t.stindl@unsw.edu.au
