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Quoc Thong Le GiaView profile

Associate Professor

• Numerical Analysis
• Approximation Theory
• Partial Differential Equations
• +5 more

Quoc Thong Le Gia is an Associate Professor in the School of Mathematics & Statistics at the University of New South Wales (UNSW), Sydney. He holds a PhD in Mathematics from Texas A&M University (2003), an MS in Mathematics from Texas A&M University (2000), and a BSc in Mathematics and Computer Science from UNSW (1998). His research focuses on Numerical Analysis , Approximation Theory , Partial Differential Equations , and Stochastic Processes , with particular expertise in problems on spherical domains. His work bridges theoretical mathematics with practical applications in computational science, data science, and machine learning. Le Gia's recent publications demonstrate a strong focus on numerical methods for PDEs on spheres, stochastic analysis, and machine learning applications. His work shows consistent progression from theoretical foundations to practical implementations, with increasing interdisciplinary applications in recent years. L. F. Guseman Prize in Mathematics, Texas A&M University (2003) As a dedicated academic mentor, Le Gia has supervised numerous PhD, Master's, and Honours students across computational mathematics and data science topics. He has secured significant research funding through ARC Discovery Projects including DP220101811 (2022-2024) and DP180100506 (2018-2020). Professionally, he serves as External Associate Editor for Frontiers in Applied Mathematics and Statistics , Secretary for ANZIAM's Computational Mathematics Group, and Co-chair of Mathematics of Computation and Optimisation (AustMS Special Interest Group).

Hongsheng HuView profile

Lecturer

• Data Privacy
• Machine Unlearning
• Trustworthy Machine Learning
• +5 more

Dr. Hongsheng Hu is currently a Lecturer in the School of Information and Physical Sciences at the University of Newcastle, Australia, specializing in the Data Science and Statistics focus area. Prior to this position, he served as a Postdoc Research Fellow at CSIRO's Data61 from October 2022 to August 2024. His academic journey includes a Doctor of Philosophy in Computer Systems Engineering from the University of Auckland in New Zealand, establishing his foundation in advanced computing systems. Dr. Hu's research centers on enhancing the trustworthiness of machine learning systems, with particular emphasis on identifying critical privacy vulnerabilities within machine learning models and developing robust defensive strategies. His work spans several key domains including adversarial machine learning (30% focus), statistical data science (30% focus), and data and information privacy (40% focus). He investigates membership inference attacks, machine unlearning techniques, and privacy-preserving mechanisms in federated learning environments. His research addresses fundamental challenges in AI security, exploring how machine learning models can be compromised through sophisticated privacy attacks and developing methods to mitigate these vulnerabilities while maintaining model utility. Analysis of Dr. Hu's publication record reveals a strong research trajectory focused on machine learning security and privacy. His work consistently addresses vulnerabilities in machine learning systems, particularly examining membership inference attacks, machine unlearning mechanisms, and privacy-preserving techniques in federated learning. The research spans top-tier venues including IEEE Security & Privacy, USENIX Security, NDSS, NeurIPS, IJCAI, AAAI, and WWW, demonstrating both technical depth and recognition by the research community. His publications show an evolving focus from foundational privacy attacks to developing more sophisticated unlearning techniques and robust defense mechanisms, with increasing citation counts indicating growing impact in the field. Active Program Committee member for USENIX Security, NDSS, ICLR, IJCAI, WWW, ICDM, ECML, and PKDD Invited reviewer for IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Pattern Analysis and Machine Intelligence (IPAMI), and ACM Computing Surveys (CSUR) Dr. Hu currently serves as Course Coordinator for STAT6020 and STAT2020 Predictive Analytics at the University of Newcastle. As an academic supervisor, he co-supervises one PhD student working on 'Identifying and Mitigating Vulnerability in Recommender Systems' at Macquarie University. His research collaborations span multiple countries, with significant publication counts in Australia (18), China (12), New Zealand (10), and the United States (7), reflecting an active international research network focused on AI security challenges.

• Stochastic Optimization
• Robust Optimization
• Data-Driven Decision-Making
• +3 more

Nam Ho-Nguyen is a Senior Lecturer in the Discipline of Business Analytics at the University of Sydney Business School. He holds a PhD from the Tepper School of Business at Carnegie Mellon University and an Honours degree in Mathematics from the Australian National University. His research focuses on stochastic, robust, and data-driven optimization techniques for decision-making under uncertainty, combining methods from statistics, machine learning, and traditional optimization. Education: PhD in Operations Research, Carnegie Mellon University (2019) Bachelor of Science (Honours) in Mathematics, Australian National University Research Interests: Nam’s work addresses decision-making in uncertain environments through optimization frameworks. He explores applications in finance, healthcare, transportation, and technology, emphasizing scalability and computational efficiency. Key areas include bilevel optimization, adversarial machine learning, and distributionally robust chance-constrained programming. Awards: INFORMS Optimization Society Young Researchers Prize (2022) Gerald L. Thompson Doctoral Dissertation Award (2019) Australian Research Council Discovery Early Career Researcher Award (DECRA) ($470K AUD) Teaching & Grants: Teaches QBUS1040, QBUS2310, BUSS4932, and QBUS6820 courses. Recipient of ARC grants for research on multi-stage optimization with contextual data and forecast reconciliation ($625K AUD). Supervises research students on scheduling problems and multi-agent systems. Lab/Team: His work involves collaborations on optimization tools for choice modeling, electronic trading, and solution design in IT services, supported by interdisciplinary teams at the University of Sydney and international institutions.

• Constrained Statistical Inference
• GARCH Models
• Extreme Value Theory
• +4 more

Mervyn J. Silvapulle is a Professor in the Department of Econometrics and Business Statistics at Monash University. He holds a B.Sc.(Honours) in Mathematics and a Ph.D. in Statistics from the Australian National University (ANU), awarded in 1982. His research focuses on econometric and statistical methodology, with current interests in constrained statistical inference, GARCH models, and extreme value theory. Past research includes robust statistics, generalized linear models, ridge regression, and minimum distance methods of inference. Education: B.Sc. (Honours) in Mathematics, Ph.D. in Statistics (ANU). Editorial Roles: Editor (Theory and Methods) of the Australian and New Zealand Journal of Statistics, guest editor for special issues on constrained statistical inference, and associate editor for multiple journals, including the Journal of Statistical Planning and Inference and Sankhya. His research spans collaborations with institutions like the Australian Research Council (ARC), Universität St. Gallen, and the European Centre for Advanced Research in Economics and Statistics. He has led or co-investigated projects such as 'Robust methods for heteroscedastic regression models for time series' (2015–2022) and 'Non-parametric estimation of forecast distributions in non-Gaussian state space models' (2009–2012). Grants & Advising: Accepting PhD students since 1995, with a history of securing ARC-funded projects.

• Trustworthy Machine Learning
• Automated Machine Learning
• Data-Driven Software Engineering
• +3 more

Dr. Huong Ha is a Senior Lecturer in Computer Science at RMIT University's School of Computing Technologies, located at the City Campus in Australia. Her research focuses on trustworthy machine learning, automated machine learning, and data-driven software engineering. She holds an ORCID ID (0000-0003-2463-7770) and is actively involved in supervising Masters and PhD students. Prior to her academic role, she worked as a Data Scientist at Freelancer International Pty Limited in Sydney from 2017 to 2018. Her research interests emphasize practical applications of machine learning in software systems optimization, including root cause analysis for microservices, Bayesian optimization techniques, and performance prediction for configurable software. She has published extensively in top conferences such as ICSE, AAAI, and NeurIPS, addressing challenges in high-dimensional optimization, system monitoring, and algorithmic efficiency. Current supervision projects include topics like Explainable AI in Human-Agent Planning, Fraud Detection on Blockchain networks, and High-dimensional Bayesian Optimization via Evolutionary Computation. Her work bridges theoretical advancements with real-world software engineering problems, contributing to robust and scalable solutions in AI-driven systems.

• Machine Learning
• Cybersecurity
• Internet of Things (IoT)
• +3 more

Firas Al-Doghman is a Lecturer at the School of Computer Science, University of Technology Sydney. With expertise in Machine Learning, Cybersecurity, and Internet of Things (IoT), he teaches subjects like iOS Applications, Networking, and Data Engineering while conducting research on smart algorithms and their cybersecurity applications. PhD in Computer Engineering and Data Management (UTS, 2020) Teaching since 2017 at UTS Former Associate Researcher/Lecturer at UNSW ADFA Research focuses on: Machine Learning integration with cybersecurity Blockchain applications in real estate Edge computing security frameworks Consensus-based data aggregation Publications highlight advancements in: Cybersecurity for supply chains Fractional NFTs in property ownership Carbon credit price prediction models Secure microservices orchestration Key collaborations include researchers from UNSW and industry partners.

• Econometrics
• Statistics
• Statistical Inference
• +4 more

Rami Tabri is a Senior Lecturer in the Department of Econometrics & Business Statistics at Monash University. His research focuses on econometric methodologies, statistical inference, and their applications in poverty analysis, health economics, and social sciences. He has contributed to advancements in stochastic dominance testing, moment inequality models, and bootstrap methods. His work often addresses challenges in survey nonresponse and data imputation. Key collaborations include studies on public institutions in Lebanon and poverty evaluation in Australia. He has published in top journals such as the Journal of Econometrics and Health Economics . Notable contributions include frameworks for testing health concentration curves and methodologies for restricted stochastic dominance. Tabri’s media engagement includes an article analyzing the 2022 Lebanese Elections, which reached multiple platforms. His research aligns with UN Sustainable Development Goals, particularly addressing inequality and economic prosperity.

Jack JewsonView profile

Senior Lecturer

• Bayesian Inference
• Model Misspecification
• Robust Statistics
• +7 more

Jack Jewson is a Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University’s Faculty of Business and Economics, a position he has held since April 2024. He previously conducted postdoctoral research and held a Juan de la Cierva Research Fellowship at Universitat Pompeu Fabra, Barcelona. He is actively accepting PhD students and supervising research in advanced statistical methodologies. Education: PhD in Statistics, University of Warwick (awarded June 2020), in collaboration with the University of Oxford via the Oxford-Warwick Statistics Programme (OxWaSP). Integrated Master’s in Mathematics, Operational Research, Statistics, and Economics, University of Warwick (awarded July 2015). His research focuses on Bayesian inference under model misspecification, particularly in the M-open world, where no true model is assumed to exist. He develops robust computational methods for variable and model selection, and investigates statistical inference under differential privacy constraints. His work integrates loss functions into Bayesian updating and explores graphical and structural modeling applications in economics and biology. These interests are driven by the need for reliable inference in complex, real-world scenarios where models are inherently imperfect. His recent publications span high-impact journals such as The Annals of Applied Statistics , Biometrics , and Bayesian Analysis , as well as top machine learning venues like NeurIPS. The research demonstrates a strong trend toward robust, privacy-aware Bayesian methods with applications in biostatistics, signal processing, and network modeling. His work bridges theoretical statistics with practical computational solutions for modern data challenges. Scientific Awards: No specific awards or fellowships mentioned in the provided text. Jack Jewson has not received explicit mention of grants or funding in the text, but his prior Juan de la Cierva Fellowship indicates competitive research support. He is actively mentoring PhD students and expanding his research group at Monash. His work involves collaboration with leading statisticians such as David Rossell, Piotr Zwiernik, Jim Q. Smith, and Chris Holmes. While no formal lab name is provided, his research group focuses on robust Bayesian computation and privacy-preserving inference.

Kate HarborneView profile

Research Fellow

• Galaxy Dynamics
• Galaxy Evolution
• Stellar Kinematics
• +3 more

Dr. Kate Harborne is a Research Fellow at the International Centre for Radio Astronomy Research (ICRAR) at the University of Western Australia. Her work focuses on numerical simulations and galaxy dynamics, specializing in understanding galaxy formation and evolution through stellar kinematic signatures. She completed her PhD in computational astrophysics at UWA, developing the SimSpin tool for generating mock integral field unit (IFU) kinematic data cubes from simulations. Her research also explores the reliability of simulations, particularly regarding velocity structures and galactic disk evolution. She holds an MSci Physics with Astronomy degree from the University of Nottingham. Affiliations: ICRAR/UWA, Astro3D Education: PhD in Computational Astrophysics (UWA, 202X) MSci Physics with Astronomy (University of Nottingham, 2016) Research Interests: Galaxy dynamics and evolution, mock observables, stellar kinematics, numerical simulations, and tool development for observational comparisons. Key projects include the MAGPI Survey and improving simulation-realism assessments via SimSpin . Grants: Co-Investigator on the 'Constraining the physics of galaxies and dark matter with AI-accelerated statistical inference' project (2025-2025).

Karl WetteView profile

Research Fellow

• Gravitational Waves
• Neutron Stars
• Pulsars
• +3 more

Dr. Karl Wette is a Research Fellow at the Centre for Gravitational Astrophysics at the Australian National University (ANU). He holds a B.Sc. from the University of Auckland and a Ph.D. from ANU. His research focuses on gravitational waves, neutron stars, pulsars, and data analysis theory, with a particular emphasis on detecting and interpreting gravitational wave signals from compact objects like black holes and neutron stars. Dr. Wette is actively involved in the LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration, contributing to the analysis of gravitational wave data from the third observing run. His work bridges theoretical astrophysics with experimental data analysis, aiming to uncover insights into extreme astrophysical phenomena. Research Interests: Gravitational waves, neutron star dynamics, pulsar physics, and data-driven methods for gravitational wave astronomy. Dr. Wette's projects include studying postmerger remnants of black hole-neutron star binaries, constraining ultralight vector boson models, and developing advanced search algorithms for continuous gravitational waves. Collaborations: Engaged with international teams to analyze multi-messenger data from gravitational wave observatories and gamma-ray satellites like Fermi-GBM and Swift-BAT. His contributions have advanced techniques for detecting transient gravitational wave signals and interpreting their astrophysical implications. Education: B.Sc. (University of Auckland), Ph.D. (ANU).

Ruby WrightView profile

Research Fellow

• Astronomy
• Astrophysics
• Galaxy Evolution
• +5 more

Ruby Wright is a Dr Forrest Fellow at the International Centre for Radio Astronomy Research (ICRAR), part of The University of Western Australia, specializing in computational astrophysics and galaxy evolution through hydrodynamical simulations. Her research spans active galactic nuclei, stellar feedback, baryon physics, and star formation, with a focus on modeling galaxy formation processes. Key areas include supermassive black hole dynamics, elliptical galaxy evolution, and the interplay between stellar feedback and galactic structures, leveraging large-scale simulations to address fundamental questions in cosmology. Recent publications (2024–2025) highlight trends in supermassive black hole recoil events, Milky Way–Andromeda collision uncertainties, and quenched galaxy formation histories, emphasizing hydrodynamical modeling and multi-wavelength observational constraints. Scientific awards and honors: Dr Forrest Fellowship Dr. Wright serves as Co-Principal Investigator for the active grant project 'Constraining the physics of galaxies and dark matter with AI-accelerated statistical inference' (2025–2026) at The University of Western Australia, which has achieved extensive dissemination through 388 news outlets, 11 Wikipedia references, and significant social media engagement. She operates within the International Centre for Radio Astronomy Research (ICRAR), a premier global hub for radio astronomy and computational astrophysics, fostering international collaborations across institutions and leveraging high-performance computing resources for cutting-edge cosmological research.

Timothy MolloyView profile

Senior Lecturer

• Inference and decision making under uncertainty
• Inverse optimal control and dynamic game theory
• Quickest detection
• +3 more

Timothy Molloy is a Senior Lecturer in Mechatronics at The Australian National University (ANU) within the ANU College of Systems & Society. Previously, he held research roles at Queensland University of Technology (QUT) and the University of Melbourne, focusing on optimal control, dynamic game theory, and robotics. His expertise includes inference under uncertainty, quickest detection, and autonomous systems design. He holds a B.Eng and Ph.D from QUT (2010, 2015), and has received notable awards including the QUT University Medal, QUT Outstanding Doctoral Thesis Award, and an Advance Queensland Early-Career Research Fellowship. His research spans theoretical and applied domains, with emphasis on inverse optimal control, dynamic games, and Bayesian methods for decision-making under uncertainty. Notable contributions include work on event-camera tracking, autonomous navigation systems, and minimax robust change detection algorithms. Recent projects address challenges in multi-object tracking, POMDP-based sensing strategies, and communication-efficient filtering in resource-constrained environments. Education: B.Eng (QUT, 2010), Ph.D (QUT, 2015) Awards: Advance Queensland Early Career Research Fellowship (2017) QUT Outstanding Doctoral Thesis Award (2015) QUT University Medal (2010) 2018 Boeing Wirraway Team Award Research Themes: Inference & decision-making under uncertainty Inverse optimal control and game theory Quickest detection methodologies Robotics and autonomous systems Current Activities: Leading research in mechatronics at ANU, supervising graduate students in control systems and robotics, and collaborating on projects involving autonomous navigation and sensor fusion. His recent publications emphasize theoretical advancements in POMDPs, Bayesian change detection frameworks, and practical implementations of inverse control methods in autonomous systems. Ongoing work explores the intersection of game theory and robotics, with applications to multi-agent coordination and safety-critical systems.

Jesse SharpView profile

Research Fellow

• Mathematical Biology
• Computational Statistics
• Ecosystem Modelling
• +2 more

Dr. Jesse Sharp is a Research Fellow and Co-leader of the Environment, Agriculture & Natural Systems Theme in QUT's Centre for Data Science. His research fuses applied mathematics, statistics, and data science to model ecological and agricultural systems, emphasizing sustainability and resilience. Projects involve collaborative industry partnerships to optimize environmental decision-making. Research domains include: Ecological dynamics under environmental change High-performance computational methods for biological systems Uncertainty quantification in complex models Publications demonstrate strong cross-disciplinary integration, with applications spanning coral reef recovery, cancer treatment optimization, and sustainable fisheries. Methodological innovations appear in parameter estimation techniques and optimal control frameworks. No awards are documented.

Connor BottrellView profile

Research Fellow

• Astrophysics
• Galaxy Formation
• Dark Matter Theory
• +7 more

Connor Bottrell is a Forrest Fellow at the International Centre for Radio Astronomy Research (ICRAR) at The University of Western Australia. His research focuses on galaxy assembly and dark matter using data-driven methods, including statistical analysis and machine learning applied to observational surveys and numerical simulations. He holds a PhD from the University of Victoria (2020) and previously was a Kavli/NSERC Fellow at the University of Tokyo's Kavli Institute. Research interests include galaxy formation, dark matter dynamics, star formation processes, and the application of mock galaxy catalogs to bridge observational and theoretical astronomy. His work leverages advanced techniques like convolutional neural networks to analyze galaxy properties from large datasets. Key achievements include over 1,076 citations and an h-index of 17. Awards include the Kavli Fellowship. Current research projects involve constraining galaxy-dark matter physics using AI-accelerated statistical inference, supported by a 2025 grant. He advises PhD students in astronomy and leads collaborations on topics like dual quasars, post-merger galaxy evolution, and supercluster searches using Subaru data.

Lachlan AstfalckView profile

Research Fellow

• Statistics
• Spectral Analysis
• Oceanography
• +3 more

Lachlan Astfalck is a Research Fellow at the University of Western Australia's School of Physics, Mathematics and Computing, working on the TIDE ARC ITRH project. He specializes in statistical methodologies for oceanography, hydrodynamics, and paleoclimate modeling. His research focuses on spectral analysis, uncertainty quantification, and developing statistical methods for environmental applications. He holds a PhD (2019) and Bachelor of Engineering in Mechanical Engineering (2014) from the University of Western Australia. Dr. Astfalck's publications demonstrate a strong focus on developing novel statistical approaches for environmental systems. His recent work spans spectral analysis techniques, ocean turbulence modeling, ice sheet reconstruction, and Bayesian methods for climate applications. He actively develops open-source statistical tools and maintains several GitHub repositories related to spectral estimation methods. He is accepting PhD students in statistics and oceanography and has capacity for research collaborations.