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Nanda AryalView profile

Research Fellow

• Biostatistics
• Health Analytics
• Clinical Trials
• +5 more

Dr. Nanda Aryal is a Research Fellow at the Centre for Health Systems and Safety Research within the Australian Institute of Health Innovation at Macquarie University. With extensive experience in health research settings, Dr. Aryal brings strong expertise in statistical theory, programming, and clinical trial design and analysis. His educational background includes a PhD in Statistics from The University of Melbourne and a Master of Science in Applied Mathematics with Modern Applications from The University of York. Prior to his current role, he worked for four years as a biostatistician at NHMRC Clinical Trial Centre, University of Sydney, and held positions at the School of Mathematics and Statistics, The University of Melbourne, and the School of Economics, The University of Adelaide. Dr. Aryal's research focuses on developing and evaluating statistical methods for analyzing complex health data. His work spans biostatistics, health analytics, clinical trials, Approximate Bayesian Computation, statistical learning, and health economics. He has conducted significant research using linked health information systems data to identify patterns and trends that inform health policy and decision-making. His recent work has particularly emphasized sepsis outcomes, surgical volume-outcome relationships, medication safety through health informatics, and emergency department care processes. His publication record shows a strong trajectory with 28 research outputs, including numerous high-impact articles in 2024-2025. These publications demonstrate expertise across health services research, critical care medicine, health informatics, and implementation science, with particular emphasis on statistical methodologies applied to real-world health data. Active researcher with 28 total publications Extensive collaboration across Australian healthcare institutions Research cited in multiple academic and social media platforms Work picked up by news outlets and academic discussion forums Dr. Aryal has collaborated extensively with researchers across multiple institutions, with notable work at major Australian healthcare research centers. His research has received attention through social media platforms (X, Bluesky), academic platforms (Mendeley), and news outlets, indicating the relevance and impact of his research in both academic and broader contexts.

Dzmitry BadziahinView profile

Associate Professor

• Diophantine Approximation
• Analytical Number Theory
• Badly approximable sets
• +7 more

Dzmitry Badziahin is an Associate Professor in the School of Mathematics and Statistics at the University of Sydney. His academic career spans institutions across Europe and Australia, with previous positions as Lecturer and Reader at Durham University, UK, and research assistant at the University of York. Badziahin received his BSc in Applied Mathematics and Computer Science from Belarusian State University (1999-2004), followed by a PhD from the University of York, UK (2005-2007). His research primarily focuses on Diophantine Approximation, a rapidly developing area of Analytic Number Theory. His work explores the structure of badly approximable sets in R n and complete metric spaces, as well as the approximational properties of Mahler numbers and functions. Badziahin's research connects with diverse mathematical fields including combinatorics on words, homogeneous dynamics, measure theory, and ergodic theory. He has made significant contributions to understanding the Littlewood conjecture and its analogues, particularly in p-adic settings. Analysis of Badziahin's recent publications reveals a consistent focus on Diophantine approximation problems, with particular emphasis on badly approximable sets, Schmidt games, and connections to dynamical systems. His work bridges pure number theory with geometric and measure-theoretic approaches, often yielding new insights into long-standing conjectures like the Littlewood conjecture. A notable trend in his research is the interplay between p-adic and real approximation theories, demonstrating deep connections across different mathematical domains. EPSRC First Grant (2013-2015): "Theory of Badly Approximable Sets" (£99,997) LMS Undergraduate Research Bursary (2015): Summer project on "Checking p-adic Littlewood conjecture" (£1,080) LMS funding (2014): For "Dynamics and Analytic Number Theory" meeting (£7,500) LMS Nuffield Undergraduate Research Bursary (2013): Summer project on "p-adic Littlewood conjecture" (£1,080) Multiple awards in Belarusian and international mathematical and programming olympiads (1998-2003) Badziahin serves as an Associate Editor for the Journal of the Australian Mathematical Society and has mentored undergraduate research projects through LMS funding. He directs the MaPS (Mathematical Problem Solving) correspondence program for NSW high school students and previously organized the Tournament of the Towns in Sydney (2018-2021). His computational interests include participation in projects like GIMPS (searching for Mersenne primes) and finding Aliquot cycles. As AMOC State Director for New South Wales, Badziahin plays an active role in promoting mathematical competitions and nurturing young talent. His engagement with the mathematical community extends beyond research to educational outreach and computational mathematics projects.

Mareike DresslerView profile

Lecturer

• Real and Computational Algebraic Geometry
• Polynomial and Convex Optimization
• Matrix and Tensor Computation
• +3 more

Dr. Mareike Dressler is a Senior Lecturer (Assistant Professor, tenure-track) at the School of Mathematics and Statistics, UNSW Sydney. She holds a PhD in Mathematics from Goethe University Frankfurt (2018) and has held postdoctoral positions at Max Planck Institute for Mathematics in the Sciences (Leipzig) and University of California, San Diego. Education: PhD (Mathematics, 2018), M.Sc. (2013), B.Sc. (2010) from Goethe University Frankfurt Prior Affiliations: Postdoctoral researcher at MPI MiS (Leipzig), Stefan E. Warschawski Assistant Professor at UCSD, Semester Postdoctoral Fellow at Brown University (ICERM) Her research focuses on Real and Computational Algebraic Geometry, Polynomial and Convex Optimization, Matrix and Tensor Computation, and their applications in Data Science and Machine Learning. She has developed optimization techniques using sums of nonnegative circuit polynomials and explored connections between convex geometry and algebraic structures. Her recent publications span topics including multivariate Chebyshev polynomials, matrix completion ranks, SONC cone duality, and applications of semidefinite programming. Collaborations include work with Venkat Chandrasekaran, Bernd Sturmfels, and Roland Lasserre, with methodological contributions to polynomial optimization and tensor analysis. At UNSW Sydney, she teaches undergraduate and postgraduate courses including MATH 1131 (Mathematics 1A, Algebra) and MATH 5185 (Special Topic: Nonnegativity and Polynomial Optimization). Her research group engages with both theoretical and applied problems in optimization and algebraic methods.

Daniel MansfieldView profile

Senior Lecturer

• Babylonian Mathematics
• Trigonometry
• History of Mathematics
• +3 more

Dr Daniel Mansfield is a Senior Lecturer at the School of Mathematics & Statistics , UNSW Australia. He is renowned for his expertise in Babylonian mathematics and ancient trigonometry, combining historical research with mathematical rigor. Academic Rank: Senior Lecturer University: UNSW Australia School: School of Mathematics & Statistics Email: daniel.mansfield@unsw.edu.au Location: Room 4070, The Red Centre, UNSW, Sydney His research focuses on the historical development of trigonometry, particularly through the analysis of ancient Mesopotamian tablets like Plimpton 322. He has contributed to understanding pre-Greek mathematical practices and their implications for modern education. Dr Mansfield’s publications span Babylonian mathematics, geometric analysis, and applied mathematical modeling. His work often bridges historical inquiry with contemporary mathematical theory. Scientific Awards 2021 - Science Staff Excellence Awards (Highly Commendable, Communication Impact) 2018 - Australian Mathematical Society Teaching Award (Early Career) 2017 - KPMG Most Inspiring Lecturer in First Year As an educator, he has gained recognition for innovative teaching methods and public engagement, including media appearances on the BBC, TikTok, and radio programs discussing the origins of mathematics and its philosophical implications.

• Cosmology
• Extragalactic Astronomy
• General Relativity
• +5 more

Krzysztof Bolejko is an Associate Professor in Physics at the School of Natural Sciences, University of Tasmania. He specializes in theoretical cosmology, focusing on cosmic structures, dark matter, and dark energy. He holds fellowships from the Astronomical Society of Australia and the Australian Institute of Physics, and is the President of the Australasian Society for General Relativity and Gravitation. Bolejko's research employs exact inhomogeneous models and numerical cosmology, with significant contributions to understanding cosmic voids and light propagation effects like Doppler magnification. He leads the use of LSST data to map cosmic voids and has secured grants totaling over $3 million for cosmological projects. **Education**: Doctor of Science (Jagiellonian University), PhD (Polish Academy of Sciences), Master of Science (University of Warsaw), and additional educational certifications in Sydney and Tasmania. His academic journey includes roles at The University of Sydney, University of Oxford, and institutions in South Africa and the U.S. **Research Interests**: Cosmic voids, dark sector physics, general relativity, gravitational lensing, and cosmological simulations. His work bridges theoretical models with observational data, particularly leveraging LSST for void mapping. **Recent Articles**: Focus on gravitational lensing, redshift analysis, and cosmic expansion dynamics. His 2025 paper highlights challenges in distributed scientific collaboration, while 2023-2022 studies explore void detection and lensing effects. **Awards & Roles**: Teaching Merit and Student Appreciation Certificates, leadership in academic societies, and roles as Graduate Research Coordinator at UTAS. He coordinates physics courses (KYA101, KYA428) and oversees the Master of Applied Science in Physics program. **Grants & Collaborations**: Major projects include the LSST access initiative and studies on general relativistic light propagation. He actively engages in public outreach and international conferences, advocating for STEM education and cosmology research.

Thang BuiView profile

Lecturer

• Probabilistic Modelling
• Machine Learning
• Monte Carlo Methods
• +3 more

Thang Bui is a Lecturer (equivalent to tenure-track Assistant Professor) in Machine Learning at the School of Computing, Australian National University (ANU) since July 2022. Previously, he was a Lecturer at the University of Sydney (2018–2022) and spent two years (2019–2020) at Uber AI. He holds a PhD from the Cambridge Machine Learning Group at the University of Cambridge, supervised by Richard Turner and advised by Carl Rasmussen. Research Interests: His work focuses on probabilistic modeling and inference, Monte Carlo methods, distributed/continual learning, and model-based reinforcement learning. Current projects include uncertainty estimation in Gaussian processes and neural networks, adaptive models for changing environments, and interpretable machine learning techniques. Awards: Best Paper Award, ACL Workshop on Information Extraction from Scientific Publications (2023) Best Paper Award, NeurIPS workshop on Deep Learning through Information Geometry (2020) Best Paper Award, NIPS Workshop on Advances in Approximate Bayesian Inference (2015) Advising & Grants: Actively recruiting PhD/MPhil students and postdoctoral fellows. His research has been supported by grants focusing on Bayesian methods and scalable machine learning. He has advised students on topics ranging from Gaussian processes to federated learning and continual learning. Labs/Teams: Leads a research group exploring uncertainty quantification, adaptive learning systems, and probabilistic AI at ANU's College of Engineering and Computer Science.

Luca MaestriniView profile

Lecturer

• Methodological and Computational Statistics
• Statistical Theory
• Variational Approximations
• +4 more

Dr. Luca Maestrini is a Lecturer of Statistics at the Research School of Finance, Actuarial Studies & Statistics at the Australian National University (ANU), Canberra. His research focuses on methodological and computational statistics, with expertise in variational approximations, generalized linear mixed models (GLMMs), and statistical theory. He previously held postdoctoral positions at ANU (2022–2024) under Prof. Francis Hui and Prof. Alan Welsh, and at the University of Technology Sydney (2018–2022) under Prof. Matt Wand. He completed his PhD in Statistics at the University of Padova, Italy (2015–2019), studying under Prof. Nicola Sartori and co-supervisors Prof. Alessandra Salvan and Prof. Matt Wand. He also served as a Research Fellow at the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). His research interests span computational statistics, Bayesian inference, and applications in forensic science, ecology, and finance. Notable collaborations include work on inverse problems, multivariate abundance data analysis, and stochastic variational inference for GARCH models. He currently chairs the Australian Capital Territory branch of the Statistical Society of Australia. Key research trends in his articles include advancements in variational inference frameworks, asymptotic improvements to GLMMs, and statistical methods for postmortem interval estimation using lipid degradation biomarkers. His work bridges theoretical developments with practical applications in diverse fields. He has advised students through ANU’s research programs and contributed to ACEMS initiatives. His lab focuses on computational methods and applied statistical modeling, emphasizing interdisciplinary collaboration.

Peter VamplewView profile

Professor

• Multi-Objective Reinforcement Learning (MORL)
• Human-Aligned Artificial Intelligence
• AI Safety
• +4 more

Professor Peter Vamplew is a leading academic in Artificial Intelligence at Federation University’s Mt Helen campus. He holds a B.A., B.Sc. (Hons) from Flinders University and a PhD from the University of Tasmania. As a former Associate Dean (Research) and leader in multiple research initiatives, he has pioneered multi-objective reinforcement learning (MORL) since 2008, focusing on ethical AI alignment and safety. His roles include co-founding the Australian Responsible Autonomous Agents Collective (ARAAC) and membership in the Future of Life Institute’s AI Safety Research Community. His teaching spans computer games design, programming, AI, and industry engagement through professional experience courses. Research interests include MORL applications in human-aligned AI, explainable systems, and cybersecurity. He has supervised over 20 PhD/Masters students on topics like AI ethics, malware analysis, and game classification. Key collaborations include the Centre for Smart Analytics (CSA) and Health Innovation and Transformation Centre (HITC). Research funding exceeds $4.2M, including projects on adversarial environments, autonomous systems safety, and fraud detection. Notable publications address MORL algorithm development, AI apology frameworks, and ethical decision-making. His work bridges theoretical AI advancements with real-world deployment challenges in autonomous systems.

• Sustainable Energy Materials
• Thermoelectrics
• Advanced Manufacturing
• +7 more

Professor Zhigang Chen is a Capacity Building Professor of Energy Materials at Queensland University of Technology (QUT), holding appointments in the Faculty of Science and School of Chemistry & Physics. He serves as founding director for the ARC Research Hub in Zero-emission Power Generation for Carbon Neutrality (ZeroPC), Academic Research Lead, and ARC Future Fellow. Previously, he held professorships at the University of Southern Queensland (USQ) and University of Queensland (UQ), where he was an ARC Australian Postdoctoral Fellow, QLD Smart Future Fellow, and Honorary Professor. His research program spans sustainable functional materials, thermoelectrics, advanced manufacturing, and advanced microscopy for energy applications. Professor Chen's research focuses on developing high-performance thermoelectric materials for energy conversion and waste-heat recovery through nanostructure engineering and band engineering. His work has led to world-record figure of merit (ZT) in several thermoelectric systems. He has pioneered cost-effective manufacturing processes like solvothermal and microwave-assisted methods for low-toxic thermoelectric materials achieving energy-conversion-efficiency above 15%. His research integrates advanced microscopy techniques to establish structure-property links in energy materials, resulting in significant scientific breakthroughs. His publications demonstrate a strong focus on sustainable energy materials, particularly thermoelectrics, with recent work emphasizing flexible thermoelectric devices, hybrid photovoltaic-thermoelectric systems, and novel material systems like SnTe-based compounds. The research spans fundamental materials science to practical applications, with increasing attention to industry implementation and commercialization potential. Many publications address the challenge of balancing high thermoelectric performance with cost-effective manufacturing. Fellow, Royal Society of Chemistry QUT Research Excellence Award, 2023 ARC Future Fellowship (Level 3) 2022 Clarivate Web of Science Highly Cited Researchers 2020-2021 USQ Research Excellence Award 2020 Mendeley Global Top 2% Researcher 2019-2023 Professor Chen has secured approximately A$50 million in research funding as lead Chief Investigator or Chief Investigator, including 7 ARC Discovery grants (5 as lead CI), 2 ARC Research Hubs, 4 ARC Linkage grants, and numerous industry investments from HBIS groups, NQ Minerals, Cook Medicals, BHP Billiton, and Defense Science and Technology Group. He has successfully supervised numerous HDR students and established significant research collaborations with institutions including CalTech, UCLA, and Shanghai Institute of Ceramics. His research program maintains strong industry connections focused on practical applications of energy materials. Professor Chen leads multiple research initiatives including the ARC Research Hub in Zero-emission Power Generation for Carbon Neutrality (ZeroPC) and has established advanced materials characterization facilities at QUT. His research team collaborates with international partners at CalTech, UCLA, and Chinese Academy of Sciences, maintaining a strong focus on translating fundamental materials research into practical energy solutions with commercial potential.

• Applied Mathematics
• Electrochemical Systems
• Multiscale Porous Media Modeling
• +4 more

Professor Troy Farrell is the Executive Dean of the Faculty of Science at Queensland University of Technology (QUT). He holds a PhD (QUT) and B.Sc (Hons) from the University of Newcastle. His expertise lies in applied mathematics and physical chemistry, focusing on industrial systems like batteries, solar cells, and biomass processing. He leads major research projects in electrochemical nano-diodes, metal-air batteries, and coal seam gas modeling. Professor Farrell has secured over $2.8M in external funding and led national initiatives such as the Mathematics in Industry Study Group (MISG) and the ATN Industry Doctoral Training Centre. He is a Fellow of the Queensland Academy of Arts and Sciences and has received prestigious awards for his research and teaching. His teaching focuses on mathematical modeling, partial differential equations, and calculus, emphasizing student-centered pedagogy and innovation. Key achievements include developing multiphase models for food drying, phase-field models for lithium-ion batteries, and population balance models for biomass pretreatment. Research Projects: Mathematical modeling of biofuel production from cellulosic materials Electrochemical nano-diodes using Poisson-Nernst-Planck models Optimization of lithium-air batteries for secondary power Multiscale modeling of porous materials with hybrid continuum/particle methods Agrochemical uptake in plant cuticles Awards: ANZIAM Mid-Career Research Award (2015) QUT Vice-Chancellor's Award for Partnerships (2013) Australian Government Citation for Teaching Excellence (2006) Teaching & Leadership: Professor Farrell pioneered innovative teaching methods recognized by the Carrick Institute, coordinating undergraduate and postgraduate programs in Mathematical Sciences. He has mentored over 13 doctoral students and actively bridges academia-industry collaboration through leadership roles in MISG and the ATN IDTC. His work emphasizes translating mathematical models into real-world solutions for energy, agriculture, and environmental sectors. Labs & Teams: He oversees interdisciplinary teams at QUT specializing in electrochemical systems, porous media modeling, and industrial mathematics. Current collaborations include projects with sugar cane industries, battery manufacturers, and coal seam gas operators to address challenges like biomass storage safety and energy storage optimization.

Andrew EberhardView profile

Professor

• Optimization Theory
• Machine Learning
• Convex Analysis
• +7 more

Professor Andrew Eberhard holds the position of Deputy Head of Department (Research and Innovation) in the Department of Mathematical and Geospatial Sciences (MGS) at RMIT University's School of Science. He has also served as Deputy Director of the Australian Mathematical Sciences Institute (AMSI) and currently sits on its board. His academic journey includes visiting professorships at institutions such as the University of Perpignan (France), Dalhousie University (Canada), and Blaise Pascal University (France). He has contributed significantly to optimization theory and applications, including roles as Co-chair of the AustMS special interest group (MoCaO) from 2018–2024 and Program Leader of the Platform Technologies Research Institute (2012–2015). Professor Eberhard's research focuses on nonsmooth analysis, convex functions approximation, optimization algorithms, and their applications in control theory, signal processing, and economic modeling. He has applied his theoretical work to real-world challenges like utility function estimation, air traffic flow management, and blood inventory optimization. His recent work includes advancements in stochastic optimization with integer variables and machine learning integration for enhancing algorithm performance. He has been an invited speaker at prestigious events such as the International Conference on Applied Mathematics (2024, Vietnam), Variational and Nonsmooth Analysis (2024, Hong Kong Polytechnic University), and ANZIAM's plenary session (2012, Australia). His teaching interests emphasize nonsmooth analysis and its interdisciplinary applications. Professor Eberhard has supervised numerous research projects, including those on hyperparameter optimization in machine learning, MIMO signal processing, and asset protection routing. His grants include leadership roles in developing algorithms for stochastic integer programming and multi-criteria decision-making under uncertainty. He collaborates actively through the Platform Technologies Research Institute and is affiliated with multiple academic journals as an editor or reviewer.

• Control Engineering
• System Identification
• Bayesian Estimation
• +5 more

Adrian Wills is an Associate Professor in the School of Engineering (Mechatronics) at the University of Newcastle, Australia. He leads the Mechatronics Engineering program and holds academic appointments since July 2015. His research focuses on Bayesian estimation, system identification, and control engineering, with applications in robotics and mechatronics. Wills has collaborated with institutions globally, including Linköping University, Uppsala University, and the University of British Columbia. He earned his B.E. (Elec.) and Ph.D. from the University of Newcastle in 1999 and 2003, respectively. Teaching expertise includes delivering advanced courses in estimation and optimization within the Mechatronics Engineering program. Administrative roles include program convenor for Mechatronics Engineering. Research highlights include contributions to state-space models, nonlinear system identification, and model predictive control. His work bridges theoretical advancements with practical applications in engineering systems and healthcare. Key collaborations involve Professors Lennart Ljung, Thomas Schön, and Bhushan Gopaluni, among others. His technical contributions span MATLAB toolboxes (e.g., UNIT), FPGA/ASIC implementations for control systems, and interdisciplinary projects in strain measurement using neutron diffraction.

• Survey Methodology
• Multilevel Modelling
• Poststratification
• +3 more

Dr Lauren Kennedy is a Lecturer at the University of Adelaide's School of Computer and Mathematical Sciences, Department of Mathematical Sciences. Her research focuses on survey methodology, multilevel modeling, poststratification, causal inference, and Bayesian statistical techniques. She specializes in addressing challenges arising from non-representative data and improving inference in social sciences through advanced statistical methods. Her work emphasizes practical applications in public opinion analysis, epidemiological modeling, and policy evaluation. Dr Kennedy is actively involved in supervising postgraduate students in Masters and PhD programs, particularly as a co-supervisor. She maintains an office in 6.56 Ingkarni Wardli Building on North Terrace campus and can be contacted at lauren.a.kennedy@adelaide.edu.au . Key research contributions include innovations in Bayesian workflow, cross-validation methodologies, and the integration of machine learning with traditional survey techniques. Her recent publications highlight advancements in causal inference frameworks and hierarchical modeling approaches for large-scale datasets.

Miles DavenportView profile

Professor

• Immunology
• Infectious Diseases
• HIV-AIDS
• +7 more

Professor Miles Davenport is Program Head of the Infection Analytics Program at the Kirby Institute, University of New South Wales (UNSW). With a background spanning clinical medicine, experimental immunology, and mathematical modeling, he directs a team of applied mathematicians and evidence specialists focusing on host-pathogen interactions in SARS-CoV-2, HIV, and malaria. His work integrates experimental data with computational approaches to understand infection dynamics and immunity. His educational background includes: Bachelor of Science (Medicine) from University of Sydney MB BS (Medicine and Surgery) from University of Sydney D.Phil (Doctor of Philosophy) from University of Oxford Professor Davenport's research spans immunology, infectious diseases, and mathematical modeling. His team applies statistical and computational approaches to investigate treatment and immunity to infectious diseases, with major focuses on HIV immunity and latency, antimalarial treatment and immunity, and neonatal immunity. He has made significant contributions to understanding immune correlates for protection from SARS-CoV-2 infection, which has informed numerous guidelines on vaccination and treatment. His work bridges the gap between clinical medicine, immunology, and quantitative analysis, creating a unique interdisciplinary approach to infectious disease research. His recent publications demonstrate a strong focus on SARS-CoV-2 variants, vaccine effectiveness, HIV reservoir dynamics, and immune responses across different populations. The work spans mathematical modeling, clinical studies, and laboratory-based immunological investigations, reflecting his team's integrated approach to understanding host-pathogen interactions. There's a clear trend toward addressing emerging infectious disease threats through quantitative analysis of immune responses and transmission dynamics. Professor Davenport has held significant editorial positions including Senior Editor at eLife (2020-2024) and Editorial Board member at Journal of Virology (2015-2023). He served as President of the Australasian Society for Immunology (2009-2010), demonstrating leadership in the immunology community. He actively supervises postgraduate students, particularly seeking those with backgrounds in mathematics, statistics, or other quantitative disciplines to join his team. His Infection Analytics Program collaborates with approximately 30 experimental and clinical groups worldwide, using modeling to understand the dynamics of infection and immunity. Current thesis topics include Dynamics of HIV latency, Immunity to SARS-CoV-2, and Evidence Assessment in vaccine development. The Infection Analytics Program at the Kirby Institute serves as his primary research base, bringing together mathematicians, statisticians, and immunologists to tackle complex questions in infectious disease dynamics. The program's interdisciplinary approach allows for innovative solutions to challenges in vaccine development, treatment strategies, and understanding immune responses across different pathogens.

Patrick LaubView profile

Senior Lecturer

• Actuarial Science
• Data Science
• Artificial Intelligence
• +3 more

Patrick Laub is a Senior Lecturer at the UNSW School of Risk and Actuarial Studies, where he teaches courses in artificial intelligence and machine learning with a focus on risk and insurance applications. His academic work bridges the gap between advanced computational methods and practical actuarial problems. Patrick holds a joint PhD in computational applied probability completed between the University of Queensland and Aarhus University. He also possesses degrees in software engineering and mathematics, providing him with a strong interdisciplinary foundation for his research. His research focuses on computationally challenging problems in actuarial data science, with particular emphasis on natural catastrophe modeling and artificial intelligence applications. Key areas of investigation include Hawkes processes for modeling contagion in insurance claims, Approximate Bayesian Computation for fitting complex insurance loss models, and Empirical Dynamic Modeling for analyzing complex temporal dependencies. His work addresses critical challenges in risk assessment, particularly for extreme events where traditional statistical methods may be inadequate. Analysis of Patrick's recent publications reveals a strong trend toward integrating advanced statistical methodologies with practical actuarial applications. His work spans from theoretical developments in point processes and Bayesian inference to practical implementations in computational environments. A notable theme is the application of machine learning techniques to traditional actuarial problems, particularly in the areas of catastrophe modeling and risk prediction. Patrick has developed and taught innovative courses since 2022, including 'Artificial Intelligence & Deep Learning and their Applications to Risk and Insurance' (ACTL3143 and ACTL5111) and 'Statistical Machine Learning for Risk and Actuarial Applications' (ACTL5110). These courses reflect his commitment to preparing students for the evolving landscape of data-driven risk management in the insurance industry. His research is supported by UNSW's strong infrastructure for computational research, though specific lab affiliations are not explicitly mentioned in the available information. Patrick maintains an active research program with numerous publications across statistics, actuarial science, and computational methods.