جست‌وجوی استاد و پژوهشگر

Robert Clarkمشاهده پروفایل

دانشیار

• Statistical Ecology
• Survey Sampling Methods
• Applied Statistics
• +۳ مورد دیگر

Robert Clark is an Associate Professor at The Australian National University (ANU) in the Research School of Finance, Actuarial Studies and Statistics. He specializes in survey methodology, statistical ecology, and applied statistics, with over 30 years of experience in consulting, research, and teaching. Clark holds a PhD and is an Accredited Statistician (AStat) and Elected Member of the International Statistical Institute. He has delivered keynote lectures, including the Ken Foreman Lecture, and serves as an associate editor for major journals like the Journal of the Royal Statistical Society Series A. Education: PhD, MStat, BSc from the University of Queensland, ANU, and University of Wollongong. Research focuses on sample design optimization, ecological statistics (particularly koala behavior and habitat), and biosecurity surveillance. His work includes projects on post-fire ecological recovery, group testing for pest detection, and intergenerational income mobility. Key awards include recognition from the Statistical Society of Australia and leadership in official statistics research (e.g., Statistics New Zealand grants). Clark has secured major grants, including two ARC Linkage Grants and a Statistics New Zealand contract, supporting projects like koala nutritional studies and biosecurity sampling methods. He collaborates internationally and advises on household surveys and ecological sampling frameworks. Labs/Teams: His research integrates with ANU’s ecological and statistical networks, contributing to interdisciplinary projects such as the 'Assessing landscape nutritional quality for koalas' initiative.

Allan McRaeمشاهده پروفایل

دانشیار

• Systems Genomics
• Genetic Epidemiology
• Epigenetics
• +۳ مورد دیگر

Associate Professor Allan McRae is a Group Leader in Systems Genomics at the University of Queensland's Institute for Molecular Bioscience (IMB). His research focuses on understanding biological mechanisms of human variation using integrated omics data, particularly investigating genomic regulation and its effects on complex traits. He leads a team of researchers and students, including PhD candidates and visiting scholars, exploring areas like epigenetics, neurodegenerative diseases, and metabolic disorders. McRae's work spans genetic epidemiology, leveraging large-scale genomic and epigenetic datasets to identify biomarkers and risk factors for conditions such as Alzheimer’s, ALS, and endometriosis. His team employs machine learning, GWAS, and advanced statistical methods to analyze complex biological systems. Notable collaborations include studies across Europe, Australia, and the UK, focusing on longitudinal health data and predictive modeling. His research has led to breakthroughs in understanding sex-specific epigenetic differences, CNV impacts on neurodevelopmental disorders, and metabolic pathways in liver function. McRae actively contributes to interdisciplinary initiatives at IMB, including the Population and Disease Genomics Research Centre. He mentors a diverse group of students and postdoctoral researchers, emphasizing training in computational biology and translational genomics. McRae’s publications highlight innovative approaches to integrating multi-omics data, including methylation risk scoring, inflammatory biomarker prediction, and epigenetic clock analysis. His work underscores the importance of systems-level thinking in addressing multifactorial diseases, bridging basic science with clinical applications.

Dilki Jayasingheمشاهده پروفایل

پژوهشگر

• Dermatology
• Medical Imaging
• Artificial Intelligence in Healthcare
• +۳ مورد دیگر

Dilki Jayasinghe is a Postdoctoral Researcher at the Centre for Health Services Research, The University of Queensland. Their work focuses on melanoma diagnosis, medical imaging technologies, and applications of artificial intelligence in healthcare. They lead research projects involving 3D total-body photography for early melanoma detection and have contributed to studies analyzing the impact of the pandemic on cancer diagnosis patterns. Research interests include dermatological risk assessment, automated lesion detection, and population health surveillance. Their interdisciplinary approach combines clinical data with advanced imaging and machine learning techniques to improve early cancer detection methods. Key projects include randomized clinical trials evaluating 3D photography efficacy and systematic reviews of nevus-counting methodologies. Publications highlight innovations in automated skin surface phenotyping, BMI estimation from 3D imagery, and statistical modeling of naevus distribution. This work bridges clinical practice with technological advancements, aiming to enhance diagnostic accuracy and reduce melanoma mortality rates. No scientific awards are listed, but their contributions to melanoma imaging research are recognized through collaborative projects with the Australian Centre of Excellence in Melanoma Imaging & Diagnosis (ACEMID). Grant-funded research includes ACEMID initiatives and a 2021 study on melanoma risk factors using 3D photography. Advising activities are not explicitly documented, though their work involves supervising research teams in imaging and data analysis projects. Laboratory affiliations include the Centre for Health Services Research facilities at UQ, focusing on translational medical imaging research.

Debra Bernhardtمشاهده پروفایل

استاد

• Statistical Mechanics
• Computational Chemistry
• Nonequilibrium Systems
• +۲ مورد دیگر

Debra Bernhardt is Professor and ARC Laureate Fellow at the University of Queensland, holding joint appointments in the School of Chemistry and Molecular Biosciences and Australian Institute for Bioengineering and Nanotechnology (AIBN). She directs the Centre for Theoretical and Computational Molecular Science. Research develops theoretical frameworks for nonequilibrium statistical mechanics and applies computational methods to energy storage systems. Current projects investigate ionic transport in supercapacitors, electrocatalytic conversion mechanisms, and nanomaterials design. Pioneered fluctuation theorems validating transport coefficient positivity through molecular dynamics. Honors include Australian Academy of Science Fellowship and Association of Molecular Modellers Medal. Leads multiple ARC grants including Laureate Fellowship on nonequilibrium thermodynamics and LIEF infrastructure funding. Supervises doctoral candidates in computational materials science and statistical mechanics. International collaborations include NASA, Politecnico di Torino, and University of Tokyo through Australia-Japan Studies grants.

• Immunotherapy
• Melanoma Research
• Medical Imaging
• +۵ مورد دیگر

Dr. Nurudeen Adegoke is an Honorary Senior Lecturer affiliated with the Melanoma Institute Australia , FMH Translational Research Collective , and the University of Sydney's Faculty of Medicine and Health . Immunotherapy response prediction in melanoma Medical imaging and machine learning applications Multi-omics and spatial profiling of tumor microenvironments Public health epidemiology (obesity, vaccination trends) His recent work focuses on developing deep learning frameworks and adaptive algorithms for analyzing multiplex immunofluorescence images to predict immunotherapy responses. He contributes to the Global Burden of Disease Study , modeling obesity prevalence and vaccination coverage trends. Publications from 2022–2025 highlight collaborations with multidisciplinary teams in melanoma research , computational pathology , and public health analytics .

• Neuroimaging
• Medical Imaging
• Data Analysis
• +۳ مورد دیگر

Dr. Thomas Close is a National Imaging Facility Informatics Fellow at the School of Biomedical Engineering, University of Sydney . His work focuses on neuroimaging analysis, computational neuroscience, and developing reproducible research tools. He holds a PhD and has contributed to advancements in medical imaging technologies like MRI/PET motion correction and multimodal data integration. Education: PhD (details not specified). Affiliations: National Imaging Facility, School of Biomedical Engineering, and collaborations with institutions like Monash University. Research Interests: Dr. Close specializes in neuroimaging methodologies, including motion correction algorithms, multimodal imaging (fMRI/PET), and open-source software frameworks for reproducible neuroscience. His work bridges computational techniques and clinical applications, with a focus on cerebellar function and neurodegenerative diseases such as Friedreich ataxia. Publications: His recent research includes Neurodesk (an open science tool for neuroimaging), cortico-cerebellar architecture studies, and advancements in diffusion MRI tractography. His work emphasizes reproducibility, open science practices, and collaborative platforms like Brainhack. Grants/Advising: No specific grants or advisees listed in the provided texts. However, his research collaborations suggest engagement with interdisciplinary teams. Labs/Teams: Associated with the National Imaging Facility, where he contributes to informatics initiatives, and the University of Sydney’s Biomedical Engineering labs focusing on neuroimaging technologies.

• Computer Science
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Dr. John Stavrakakis is a Lecturer at the School of Computer Science within the University of Sydney. His research focuses on machine learning , graph algorithms , and 3D graphics optimization , particularly in developing efficient computational methods for spectral clustering , deep learning architectures , and network-aware visualization systems . Current research emphasizes random projection techniques for graph convolutional networks and topology-aware clustering methods Specializes in 3D graphics streaming and distributed visualisation environments Collaborates on network security behavior through computational models of protection motivation His recent publications highlight innovations in graph construction , k-NN search efficiency, and parameter-free optimization frameworks. Dr. Stavrakakis supervises PhD students including Michael Ashshiddiq Podbury, with expertise spanning data mining , network security , and interactive visualization . Contact: john.stavrakakis@sydney.edu.au

• Machine Learning
• Time Series Analysis
• Statistics
• +۵ مورد دیگر

Dr. Pablo Montero-Manso is a Lecturer in the Discipline of Business Analytics at the University of Sydney. His research focuses on Machine Learning and Statistics for Time Series Analysis, particularly in clustering, classification, and forecasting. He has developed methodologies for automatic forecasting on large datasets and applied his work to pandemic modeling during the 2020–2021 pandemic. His open-source tools (e.g., tsfeatures , TSclust ) are widely used in academia and industry. Education : PhD from the University of A Coruña, Spain (pre-Sydney postdoc at Monash University). Research Interests : Time series forecasting, machine learning applications in energy and public health, automated ensemble models, and clustering algorithms for large datasets. His work bridges theoretical advancements with practical tools for real-world challenges. Recent articles include contributions to renewable energy scheduling, pandemic forecasting, and AI-driven decision-making in business. He leads projects like the Augmented Judgments grant exploring AI-human collaboration in product launches. Labs/Teams : Maintains active collaborations through open-source projects and academic networks in statistics and data science.

• High Dimensional Statistical Inference
• Machine Learning
• Data Science
• +۱۱ مورد دیگر

Professor Bala Rajaratnam is a leading academic in Business Analytics at the University of Sydney Business School . His research spans high-dimensional statistical inference , machine learning , and data science , with applications in economics , finance , biomedical sciences , and environmental modeling . Key focus areas include graphical models , network analysis , and Bayesian methodology . His recent work examines partial correlation screening , covariance estimation , and climate field completion . Publications highlight interdisciplinary collaborations across climate science , financial engineering , and biomedical research . He received a 2019 Australian Research Council Discovery Project grant for Principled statistical methods for high dimensional correlation networks .

Andrew Backمشاهده پروفایل

دانشیار مدعو

• Human Centred Computing
• Neural Networks
• Information Theory
• +۴ مورد دیگر

Dr Andrew Back is an Adjunct Associate Professor in the School of Electrical Engineering and Computer Science at the University of Queensland. His research focuses on Human Centred Computing, Neural Networks, Information Theory, and Machine Learning, with applications in signal processing and natural language analysis. His research interests span Human Centred Computing, with a focus on applying Information Theory and Machine Learning to analyze natural sequences, linguistic patterns, and biomedical signals. His work integrates computational methods for entropy estimation, statistical manifold analysis, and the development of assistive technologies, such as Auslan communication systems. He explores interdisciplinary areas including speech analysis for dementia detection and synthetic language modeling. Recent publications emphasize entropy estimation methodologies, information topology in synthetic languages, and computational approaches to speech and language analysis. Key themes include improving automated analysis of natural sequences and applying machine learning to solve complex problems in linguistics and healthcare. Dr. Back has contributed to collaborative research in healthcare technology, speech processing, and assistive communication systems. His work bridges computational methods with real-world applications, particularly in improving accessibility and medical diagnostics.

Thang Buiمشاهده پروفایل

مدرس

• Probabilistic Modelling
• Machine Learning
• Monte Carlo Methods
• +۳ مورد دیگر

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.

• Sociology of Quantification
• Public Statistics
• Evidence & Public Policy
• +۳ مورد دیگر

Samantha Vilkins is a PhD candidate in science communication at the Australian National Centre for the Public Awareness of Science (CPAS) at the Australian National University (ANU). She holds a BSc in Computational Science and Mathematics from Queensland and an MSciComm from ANU. As a Sessional Academic, she has tutored the 'Science Communication and the Web' course since 2017. Her research focuses on quantified evidence's role in politics, the societal implications of statistical metrics, and new media's impact on public discourse. Education: BSc (Computational Science and Mathematics), Queensland MSciComm, Australian National University Research interests include the sociology of quantification, rhetoric of public statistics, and civic epistemologies. Her work explores how metrics shape policy decisions and what societal insights are marginalized by numerical frameworks. She also investigates digital media dynamics, including polarization in online spaces and the evolving role of influencers in local politics. Her visual design work has been exhibited at the National Museum of Australia, National Library of Australia, and Science Gallery Melbourne's 2018 PERFECTION exhibit. Publications span peer-reviewed journals (e.g., Scientometrics , Foodborne Pathogens and Disease ) and popular platforms like The Conversation and Lateral Magazine . Key themes in her recent articles include analyzing political polarization in digital contexts, trust in public data post-pandemic, and the interplay between technology hype and societal adoption (e.g., quantum computing). Awards: None explicitly listed. Teaching: Extensive tutoring experience in science communication courses. Visual practice: Integrates design into research communication through exhibitions and digital media projects.

• Robotics
• Artificial Intelligence
• Task and Motion Planning
• +۴ مورد دیگر

Rahul Shome is a tenure-track lecturer in the School of Computing at the Australian National University (ANU), specializing in Robotics & AI. His research focuses on planning algorithms for human-centric robotics applications, with expertise in task and motion planning, multi-robot systems, and collaborative automation. He holds a Ph.D. and M.S. from Rutgers University, advised by Prof. Kostas E. Bekris, and completed postdoctoral work at Rice University under Prof. Lydia E. Kavraki. Education: Ph.D. and M.S. in Robotics, Rutgers University (advisor: Prof. Kostas E. Bekris) Postdoctoral Research Associate, Rice University (advisor: Prof. Lydia E. Kavraki) Research Interests: Development of practical robotics solutions with theoretical guarantees Integration of AI techniques for real-world automation challenges Multi-robot coordination and manipulation in dynamic environments Human-centric robotics emphasizing privacy, ethics, and collaboration His publications span journals and conferences like IEEE Robotics and Automation Letters, with awards including the Best Paper Award at IEEE MRS. Shome actively serves on editorial and organizing boards for major robotics venues. Current research directions explore algorithmic foundations in stochastic decision-making, topology, and machine learning integration. Advising & Opportunities: Open PhD and research positions in robotics planning and collaborative automation Focus areas: AI-driven robotics, human-robot interaction, and perception systems

Nicholas Biddleمشاهده پروفایل

استاد

• Public Policy
• Indigenous Populations
• Social Sciences
• +۴ مورد دیگر

Professor Nicholas Biddle is the Head of the School of Politics and International Relations at The Australian National University. He holds a PhD in Public Policy from ANU, a Master of Education from Monash University, and a Bachelor of Economics (Hons) from the University of Sydney. Prior roles include Deputy Director of the ANU Centre for Social Research and Methods and Senior Research Officer at the Australian Bureau of Statistics. His research focuses on public policy, Indigenous populations, democratic resilience, and social wellbeing. Key projects include analyses of Closing the Gap policies, post-pandemic economic impacts, and gambling behavior trends. He is a Fellow of the Tax and Transfer Policy Institute and advises government bodies such as the Australian Statistics Advisory Council. Recent work examines democratic attitudes in Australia, generational shifts in political behavior across Asia, and the socioeconomic effects of the pandemic. Over 190 publications and 86 active or concluded research projects reflect his interdisciplinary approach, spanning policy evaluation, methodological innovation, and applied social science. Grants include leadership on studies like the Family Law Amendment Act 2023 survey and the Resilient Democracy Data Network. His research teams collaborate globally, with projects addressing issues from voting behavior to healthcare equity. Beyond academia, he maintains active engagement in public discourse through media commentary and community outreach.

Luca Maestriniمشاهده پروفایل

مدرس

• Methodological and Computational Statistics
• Statistical Theory
• Variational Approximations
• +۴ مورد دیگر

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.