جستجوی استاد راهنمای دانشگاهی

Robert Lanfearمشاهده پروفایل

مدرس ارشد

• Molecular Evolution
• Phylogenetics
• Bioinformatics
• +۵ مورد دیگر

Robert Lanfear is a Senior Lecturer at the Australian National University (ANU), affiliated with the Division of Ecology and Evolution. He holds a PhD from the University of Sussex (2008) and has held postdoctoral positions at ANU and Macquarie University. His research focuses on molecular evolution, phylogenetics, and bioinformatics, with a particular emphasis on somatic mutations in plants and the development of phylogenetic software tools like PartitionFinder and IQ-TREE. Education: BSc and Honors in Ecology, MSc in Artificial Intelligence, PhD in Developmental Biology (University of Sussex). His work integrates comparative methods, genomics, and computational approaches to understand mutation dynamics, evolutionary processes, and the development of robust analytical tools for evolutionary studies. Research interests include plant somatic mutation accumulation, genomic evolution in plants, and statistical methods for phylogenetic analysis. He leads projects on scalable phylogenetic methods for large genomic datasets and collaborates on real-time genomic epidemiology. His software contributions include IQ-TREE, DecentTree, and RWTY, which are widely used for phylogenetic inference and analysis. Supervision: Actively recruiting students for research on plant somatic mutations and molecular evolution. Projects span fieldwork, genomic sequencing, and computational modeling. Labs/Teams: Associated with ANU’s Ecology and Evolution research groups, focusing on bioinformatics and genomics. Collaborations include software development for evolutionary analysis and pandemic genomic studies.

• Statistics
• Bootstrapping
• Model Selection
• +۳ مورد دیگر

Professor Michael Martin holds a position at the Australian National University (ANU) within the Research School of Finance, Actuarial Studies & Statistics. He earned a BSc from the University of Queensland and a PhD from ANU, and is a Fellow of the Higher Education Academy (PFHEA). His research focuses on advanced statistical methodologies including bootstrapping, model selection, and time series analysis, with applications in health economics, actuarial science, and environmental studies. Notable projects include developing robust statistical frameworks for financial markets and assessing air pollution impacts using modern model selection techniques. Education: BSc (Queensland University), PhD (ANU) Research interests span statistical theory and its practical implementations in interdisciplinary fields. Recent work addresses researcher safety protocols, maternal healthcare cost analysis, and pain trajectory studies in osteoarthritis. Collaborative projects include blockchain valuation mechanisms and retirement savings behavior modeling. Scientific contributions include over 30 peer-reviewed publications and 8 funded research projects. Awards include PFHEA recognition for educational excellence. Supervises research students and actively participates in grant-funded initiatives such as the 'Valuation of cryptographic assets' project (2019-2021). Engages in interdisciplinary teams addressing financial, environmental, and health sector challenges through statistical innovation.

Janice Scealyمشاهده پروفایل

دانشیار

• Compositional Data Analysis
• Directional Statistics
• Robust Statistics
• +۵ مورد دیگر

Janice Scealy is an Associate Professor in the Research School of Finance, Actuarial Studies & Statistics at The Australian National University (ANU). She holds a PhD in Statistics from ANU (2011) and a Bachelor of Mathematics (Honours) from the University of Wollongong (2003). Her research focuses on compositional data analysis, directional statistics, robust statistics, and applications in geosciences like palaeomagnetism and seismology. She has held roles including ARC DECRA Fellow (2018-2022) and has supervised multiple research projects. Notable contributions include advancements in hypersphere-based statistical models and robust estimation techniques. Education: PhD (ANU 2011), BMath (Wollongong 2003) Her research interests include statistical methods for manifold-valued data, geostatistics, and model selection in linear mixed models. Recent work applies elliptical distributions to seismic moment tensor classification and integrates Fisher's palaeomagnetic theories with modern statistics. She leads projects on non-Euclidean data analysis and biologics immunogenicity assessment. Key collaborations involve geophysical applications and statistical methodologies for spatial data. Awards include ANU research grants (2018–2025) and contributions to international statistical reviews.

• Applied Statistics
• Data Science
• Methodological Research
• +۷ مورد دیگر

Max Moldovan, PhD, is a Researcher at the School of Agriculture, Food and Wine within the Faculty of Sciences, Engineering and Technology at the University of Adelaide. He joined the Biometry Hub in July 2021 after a seven-year tenure at the South Australian Health and Medical Research Institute (SAHMRI), where he served as Lead Bioinformatician and Senior Data Scientist. He is eligible to supervise both Masters and PhD students. Max obtained his PhD in Statistics from the University of Melbourne in 2008, followed by postdoctoral training at the Walter and Eliza Hall Institute of Medical Research (WEHI). His career has focused on high-impact research in data science and applied statistics within life sciences. PhD in Statistics, University of Melbourne (2008) Postdoctoral Training, Walter and Eliza Hall Institute of Medical Research (WEHI) Max Moldovan's research centers on methodological and applied statistics in life sciences, with a focus on exact, unbiased, and efficient inference—particularly causal inference—from observational and routinely collected datasets. He is actively engaged in developing and applying statistical learning methods and generalized additive modelling approaches. His work has had tangible national and international impact, particularly in health and agricultural data analytics. Although no specific publications are listed in the provided text, Max's research trajectory indicates a strong focus on robust statistical methodologies for real-world data, especially in biometric and biomedical contexts. His work likely spans epidemiology, public health, and agricultural science applications. Research Translation Award, SAHMRI (2019) High-impact Publication Award, SAHMRI (2020) Max has supervised or co-supervised higher degree research students at the University of Adelaide and was actively involved in major research projects during his time at SAHMRI. While specific grants are not listed, his award-winning work suggests successful grant funding and translational research outcomes. He contributes to the Biometry Hub, a specialized unit supporting statistical design and analysis across research domains at the university. Max is a member of the Biometry Hub at the University of Adelaide, a collaborative team providing advanced statistical support for research projects across agriculture, health, and environmental sciences. His role involves methodological development, data analysis leadership, and training researchers in robust statistical practices.

Ong Huey Fangمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Data Mining
• Bioinformatics
• +۷ مورد دیگر

Dr. Ong Huey Fang is a Senior Lecturer in the School of Information Technology at Monash University Malaysia since 2019. She holds a PhD in Intelligent Computing from Universiti Putra Malaysia, with earlier degrees from Universiti Teknologi Malaysia. Her research focuses on artificial intelligence applications in bioinformatics (e.g., cancer biomarker discovery), machine learning (transfer learning for Malaysian English), and computer vision (micro-expression analysis). She has led projects like the 2022 Deep Associative Classification initiative. Professional memberships include IEEE, ACM, and AWS certification. Current work integrates multi-omics data analysis and causal graph modeling to address biases in AI systems. Education: BSc (UTM), MSc & PhD (UPM) Professional Certifications: AWS Cloud Practitioner Research spans cancer biomarkers via omics data, NLP for low-resource languages, and VR-based educational tools. Recent projects include causal analysis of video micro-expression bias and blockchain-enhanced healthcare systems. She actively collaborates internationally on topics like biomedical event extraction and supply chain digitalization.

Eric Howardمشاهده پروفایل

پژوهشگر ارشد

• Quantum Physics
• Machine Learning
• Gravitational Waves
• +۴ مورد دیگر

Dr. Eric Howard is a Research Fellow at Macquarie University , affiliated with the School of Engineering , School of Mathematical and Physical Sciences , and School of Computing . His research spans interdisciplinary domains at the intersection of quantum physics, machine learning, and AI-driven systems. Key research themes include: Quantum cryptography for Industry 4.0 security Machine learning in IoT temperature sensing Adversarial AI in cybersecurity 6G wireless communication optimization Quantum information processing Deep learning for data imputation Recent publications demonstrate a focus on emerging technologies, with articles on quantum Bayesian inference , 6G signal processing , and smart city IoT systems . His collaborative work extends to blockchain-enabled supply chain visibility and generative AI applications in programming. Research collaborations span institutions in India (AIP Publishing) and Australia, with technical contributions to quantum dynamics, neural network applications, and nanosensor development.

Hasan Fallahgoulمشاهده پروفایل

مدرس ارشد

• Econometrics
• Finance
• Statistics
• +۸ مورد دیگر

Hasan Fallahgoul is currently a Senior Lecturer at the School of Mathematical Sciences, Monash University, and a member of the Monash Centre for Quantitative Finance. Prior to this, he held post-doctoral positions at the Swiss Finance Institute (EPFL) and the European Center for Advanced Research in Economics and Statistics (ECARES) at the Free University of Brussels, Belgium. Senior Lecturer, Monash University (2025–present) Post-Doctoral Researcher, Swiss Finance Institute (2024–2025) Post-Doctoral Researcher, ECARES (2023–2024) His research interests span Econometrics, Finance, Statistics, and Machine Learning, with a focus on Tail Risk, Neural Networks, and Fractional Calculus. Recent work integrates interpretable AI into asset pricing, develops significance tests for neural networks, and explores high-dimensional learning in finance. His recent article trends highlight applications of Machine Learning in financial econometrics, including state space models, Lévy processes, and tempered stable distributions. He has contributed to understanding complexity in financial models, significance testing frameworks, and the role of social signals in market dynamics. Hasan is supported by grants from the Australian Research Council (DP250100063) and the National Natural Science Foundation of China (72033002). He actively develops open-source tools like the SSMEfficientInference Python package for state space model analysis. He is affiliated with the Monash Centre for Quantitative Finance , where he collaborates on interdisciplinary projects involving econometrics, machine learning, and financial engineering.

• Forensic Genetics
• Human Identification
• DNA Profiling
• +۴ مورد دیگر

Jodie Ward is an Adjunct Associate Professor (Forensic Genetics) in the Centre for Forensic Science at the University of Technology Sydney (UTS), School of Mathematical and Physical Sciences. She is a leading expert in forensic human identification with over a decade of experience advancing capabilities to resolve unidentified and missing persons cases. Dr. Ward's research interests focus on forensic genetics , human identification , and disaster victim identification . Her pioneering work includes operationalizing the National DNA Program for Unidentified and Missing Persons (2020-2024) and the NSW Specialist DNA Laboratory (2011-2019). She has developed interdisciplinary forensic frameworks that integrate cutting-edge DNA technologies with traditional identification methods to resolve cold cases. Her recent publications demonstrate a strong focus on forensic genomics, particularly the application of single nucleotide polymorphism (SNP) profiling, forensic investigative genetic genealogy, and kinship inference techniques for human remains identification. Her research shows consistent innovation in applying advanced genetic tools to challenging forensic samples, including degraded skeletal remains. Public Service Medal (PSM) 2024 Royal Australian Navy Group Commendation 2022 Churchill Fellowship (CF) 2015 NSW Women of Excellence 2023 100 Women of Influence 2018 Dr. Ward currently provides forensic HID expertise to impactful global projects, including serving as the forensic geneticist for the Office of the Director of Authorised Intervention, Tuam (ODAIT), Ireland, where she guides DNA-led identification of commingled juvenile remains. She founded Forensic Human Identification Co. in 2024, a social enterprise focused on providing equal access to forensic identification services globally. She supervises postgraduate research projects focused on advancing forensic genomics tools for identifying skeletonized remains and repatriating Indigenous remains. Her laboratory work at UTS's Australian Facility for Taphonomic Experimental Research (AFTER), where she served as Director from 2021-2023, has translated research findings into practical forensic techniques like forensic investigative genetic genealogy and craniofacial reconstruction, validated using donated human bodies before application to casework.

Feng Liuمشاهده پروفایل

استادیار

• Machine Learning
• Hypothesis Testing
• Trustworthy Machine Learning
• +۳ مورد دیگر

Feng Liu is an Assistant Professor at the Decision Systems and e-Service Intelligence (DeSI) Lab within the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). He also serves as a Visiting Scientist at RIKEN-AIP, Japan. His academic journey includes a PhD in Computer Science from UTS (2020), an MSc in Probability and Statistics from Lanzhou University (2015), and a BSc in Mathematics from the same institution (2013). His educational background includes: Ph.D. (2020), Computer Science, University of Technology Sydney, Australia M.Sc. (2015), Probability and Statistics, Lanzhou University, China B.Sc. (2013), Mathematics, Lanzhou University, China Feng Liu's research centers on developing trustworthy intelligent systems through hypothesis testing and reliable knowledge transfer across domains. His work spans two-sample testing for distribution comparison, transfer learning for knowledge adaptation across domains, and defending against adversarial attacks to improve model robustness. His approach combines theoretical foundations with practical applications, particularly in domain adaptation with interval-valued data and secure multi-source learning. His recent publications demonstrate a strong focus on trustworthy machine learning, with significant contributions to interval-valued data processing, novel class discovery under unreliable sampling conditions, and privacy-preserving domain adaptation. His work bridges theoretical machine learning with practical applications in computer vision, bioinformatics, and recommender systems, showing a consistent pattern of addressing fundamental challenges in trustworthy AI. Among his notable recognitions are: Outstanding Reviewer Award of ICLR (2021) AAII Best Student Paper Award (2020) Best Student Paper Award from IEEE International Conference on Fuzzy Systems (2019) UTS-FEIT HDR Research Excellence Award (2019) Publons Peer Review Awards - Top 1% reviewers in Computer Science (2019, 2018) Dr. Liu has actively contributed to the academic community through supervision and service. He has helped supervise four students who collectively produced eight academic papers, three of which were published in CORE Tier A* venues. His service includes program committee roles for major conferences including NeurIPS, ICML, ICLR, and AAAI, as well as reviewing for prestigious journals like IEEE-TPAMI and IEEE-TNNLS. His research has been supported by various grants, including the Australian Laureate postdoctoral fellowship. As part of the AAII at UTS, Dr. Liu contributes to a vibrant research environment focused on advancing artificial intelligence through interdisciplinary collaboration. His work in the Decision Systems and e-Service Intelligence Lab addresses real-world challenges in trustworthy machine learning, with applications spanning healthcare, robotics, and secure information systems.

• Statistical Modeling
• Machine Learning
• Regression Analysis
• +۳ مورد دیگر

Wang You-Gan is an accomplished academic researcher with over three decades of publication history spanning from 1991 to 2024. His work demonstrates a strong foundation in statistical methodology with applications across multiple domains including environmental science, fisheries management, machine learning, and computational statistics. His research has been published in high-impact journals across statistics, computer science, environmental science, and biology. Wang You-Gan's primary research interests include statistical modeling, regression analysis (particularly support vector regression), longitudinal data analysis, machine learning applications, and environmental statistics. His work shows a clear evolution from early fisheries and environmental applications to broader computational statistical methods with applications in energy systems, genomics, and cloud computing. His research demonstrates strong methodological development coupled with practical applications. His recent publications (2022-2024) show a strong focus on advanced regression techniques, particularly support vector regression with innovations in handling heterogeneous variances, autoregressive processes, and automatic hyperparameter selection. He has also expanded into machine learning applications for energy demand forecasting, air quality prediction, and data center optimization. His work often bridges theoretical statistical development with practical implementation across diverse domains. Wang You-Gan has received significant citation impact for his methodological contributions, with several papers accumulating over 100 citations. His collaborations span multiple institutions and disciplines, indicating his work's broad relevance across fields. His research has important applications in environmental monitoring, energy systems management, computational biology, and cloud computing infrastructure. The consistent publication record over more than 30 years demonstrates his sustained contribution to statistical methodology and its applications.

• Bayesian Statistics
• Approximate Bayesian Computation
• Statistical Modeling
• +۶ مورد دیگر

Christopher Drovandi is a Professor at Queensland University of Technology and a Chief Investigator in the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). His research spans statistical methodology development and applications across diverse fields including ecology, biomedicine, and environmental science. Dr. Drovandi's primary research interests focus on advancing Bayesian statistical methods, particularly in the areas of: Approximate Bayesian Computation (ABC) and likelihood-free inference Synthetic likelihood methods Sequential Monte Carlo algorithms Bayesian experimental design Statistical computing for complex models His recent work demonstrates a strong emphasis on addressing model misspecification in Bayesian inference, developing robust computational methods for intractable likelihoods, and applying advanced statistical techniques to solve real-world problems in ecology, biomedicine, and environmental science. Drovandi has made significant contributions to both the theoretical foundations and practical applications of modern Bayesian statistics. Dr. Drovandi has received research funding through multiple Australian Research Council grants and collaborates extensively with researchers across disciplines. His work has been published in top-tier statistical and interdisciplinary journals including the Journal of the American Statistical Association, Bayesian Analysis, and PLOS Computational Biology.