جستجوی استاد راهنمای دانشگاهی

• Substance Use Epidemiology
• Mental Health
• Public Health
• +۵ مورد دیگر

Dr. Janni Leung is an NHMRC Development Fellow and Associate Professor at the National Centre for Youth Substance Use Research (NCYSUR), University of Queensland. With qualifications in public health, sociology, and psychology, their research focuses on the epidemiology of substance use and mental health disorders. They specialize in systematic reviews, meta-analyses, and statistical modeling to assess population-level impacts of addiction, informing policy in Australia and globally. Their work has generated over 100 publications with significant citation impact. Research Interests: Dr. Leung's program investigates comorbid substance use and mental health disorders, leveraging advanced epidemiological methods. Key areas include: Global patterns of cannabis, tobacco, and alcohol use Digital influences on substance use (social media, vaping) Treatment-seeking behaviors and barriers Development of diagnostic tools (e.g., Gaming Disorder Identification Test) Preventable disease burden from substance misuse Publication Trends: Their recent work demonstrates strong emphasis on meta-analyses of global substance use patterns, digital influences on youth behavior, and methodological innovations in causal inference. Longitudinal designs and cross-national comparisons dominate their 2023-2025 output, with consistent focus on policy-relevant outcomes. Awards & Recognition: NHMRC Development Fellowship (ongoing) Top 1% citation impact for 30% of publications Professional Engagement: Actively supervises students in addiction research, offers methodological consultation for systematic reviews and statistical modeling, and presents regularly at conferences including APSAD. Based at UQ St Lucia campus with cross-campus collaborations.

Daniel Stjepanovicمشاهده پروفایل

پژوهشگر ارشد

• Cannabis Policy
• Meta-Science of Addiction Research
• Cognitive Neuroscience
• +۲ مورد دیگر

Dr. Daniel Stjepanovic is a Senior Research Fellow at the National Centre for Youth Substance Use Research (NCYSUR) within the Faculty of Health, Medicine and Behavioural Sciences at The University of Queensland. His work focuses on substance use patterns shaped by public policy, including cannabis vaping, psychedelic microdosing, and social cognition in addiction. He combines expertise in cognitive neuroscience with experimental psychology to advance understanding of substance-related behaviors. Expertise: Cannabis policy analysis, meta-science, digital media impact on youth Collaborations: Duke Institute for Brain Sciences, Durham Veterans Affairs Medical Center Research Trends (2024–2025): Examines cannabis policy impacts via epidemiological methods Investigates psychedelics for self-treatment and their public health implications Develops causal inference frameworks in addiction research using DAGs and target trial emulation Conducts systematic reviews on tobacco cessation, drug checking services, and gaming disorders Supervision & Funding: Available for PhD supervision. Current NHMRC-funded projects include AI-powered anti-vaping campaigns and nicotine monitoring systems integrating global data.

Christin Schulzeمشاهده پروفایل

مدرس

• Computational Cognitive Science
• Decision Making Under Uncertainty
• Cognitive Development
• +۳ مورد دیگر

Christin Schulze is a Lecturer in Computational Cognitive Science at the School of Psychology, University of New South Wales (UNSW Sydney) . Her research investigates cognitive processes underlying adaptive behavior in uncertain environments, focusing on three core areas: (1) development of decision-making strategies in childhood, (2) influence of firsthand probabilistic experience on judgments, and (3) differences between individual and group strategies for handling uncertainty. Key real-world applications include avalanche risk navigation and educational decision-making Collaborations span institutions like University of Basel, University of Potsdam, and University of Melbourne Recent work explores cognitive cost timelines, adaptivity in probability learning, and social sampling mechanisms. Publications emphasize computational modeling, experimental validation, and cross-species comparisons (humans/chimps). She teaches in UNSW’s Psychology School and contributes to international handbooks and journals.

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.

Taha Hossein Rashidiمشاهده پروفایل

استاد

• Travel behaviour analysis
• Transportation planning
• Activity-based travel demand modelling
• +۸ مورد دیگر

Professor Taha Hossein Rashidi is a leading expert in Transport Engineering at the School of Civil and Environmental Engineering, University of New South Wales (UNSW), and a member of the Research Centre for Integrated Transport Innovation (rCITI). His work bridges disciplines like economics, statistics, urban design, and sustainability to advance smart-city solutions. Education : PhD, University of Illinois, Chicago (2011); MS Civil Engineering, Sharif University of Technology (2005); BS Civil Engineering, Sharif University of Technology (2003). His research focuses on travel behaviour analysis, activity-based travel demand modelling, integrated land use and transportation models, and autonomous driving technologies. He leads the rCITI Travel Behaviour Modelling Team, which includes 3 Post-docs, 7 PhD, and 3 MSc students. Recent work explores shared autonomous vehicles, social media data integration for transport models, and dynamic ride-sharing systems. His publications span topics like pedestrian demand modeling, residential relocation dynamics, and pandemic-related travel restrictions. Scientific Awards Fred Burggraf Award (TRB, 2008) Dwight Eisenhower Fellow (2008) ASCE Freeman Fellowship (2009) NSERC PDF Award (2012) Industrial RAND Fellowship (2012) Vice Chancellor’s Award for Teaching Excellence (2015, Team) Award for Engineering Education Engagement (2015, Team) Outstanding Paper (TRB Analytics Contest, 2017) He has secured over $1.2 million in research funding since 2007, including ARC DECRA and Linkage Grants. His teaching includes courses on geometric design, urban transport modeling, and transport econometrics.

• Artificial Intelligence
• Machine Learning
• Probabilistic Inferencing
• +۵ مورد دیگر

Barry Drake serves as an Adjunct Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he is also affiliated with the Faculty of Engineering and Information Technology and the Data Science Institute. Additionally, he holds the position of Co-Chief Scientist at the Digital Health Cooperative Research Centre and participates in government advisory committees. His career bridges academic research and industry application, with significant experience in translating research into practical technology solutions for business and government sectors. Drake earned his PhD in Computer Science from UNSW Australia (1998-2003) and a BSc with First Class Honors in Computer Science from UTS (1991-1996). His educational background is complemented by a Certificate of Proficiency for Radio Fitter/Mechanic Electronic Systems from the Royal Australian Navy. His research focuses on algorithms and software for practical smart systems, with particular expertise in applications of AI and machine learning, probabilistic inferencing and knowledge compilation, learning probabilistic models, and fast-efficient near-neighbor searching in ultra-high dimensional spaces. His work demonstrates a clear trajectory toward health informatics applications , especially in health systems and delivery of health services, where he has made significant contributions through projects like the Lumos statewide linkage programme. The analysis of his publication record reveals a consistent pattern of research bridging theoretical computer science with practical healthcare applications. His recent work shows increasing focus on health data integration, patient journey modeling, and privacy-preserving synthetic data generation for healthcare applications, while maintaining his foundational expertise in probabilistic models and efficient search algorithms. Inventor on 20 filed patents ORCID identifier: 0000-0003-0572-9936 Drake actively supervises Masters and PhD students according to his profile, and his funded research portfolio includes multiple grants from the Digital Health CRC and NSW Health. His industry experience, particularly his 13 years at Canon Information Systems Research Australia where he served as Senior Principal Engineer and lead researcher for machine learning, informs his approach to technology research methods from a commercial perspective. His current projects focus on the impact of integrated care in New South Wales, synthetic data generation, and patient journey modeling. As Co-Chief Scientist at the Digital Health CRC, Drake contributes to a major national initiative focused on digital health innovation. His work with the Lumos programme has created Australia's first statewide linked data asset across primary care and other health settings, providing unique insights about cross-setting healthcare utilization. This initiative represents a significant contribution to health data infrastructure in Australia.

• 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.

Choo Chung Siungمشاهده پروفایل

مدرس ارشد

• Tunnelling
• Trenchless Technologies
• Numerical Modelling
• +۵ مورد دیگر

Dr. Choo Chung Siung is a Senior Lecturer at Swinburne University of Technology Sarawak, Faculty of Engineering, Computing and Science, where he serves as Director of the Centre for Innovative Society. With 12 years of combined academic and industry experience, he teaches soil mechanics and geotechnical engineering to future civil engineers. Dr. Choo obtained his Bachelor of Engineering in Civil Engineering (1st Class Honours) in 2010 and completed his PhD at Swinburne University of Technology. His research focuses on tunnelling and trenchless technologies, numerical modeling of soil-structure interaction, machine learning applications in geotechnical engineering, upcycling industrial wastes for construction, and geoeducation innovations. Analyzing his recent publications reveals a strong emphasis on pipe jacking forces in weathered geological formations, with increasing integration of machine learning techniques in geotechnical analysis. His work bridges traditional geotechnical engineering with modern computational approaches, particularly in the context of Southeast Asian geological conditions. Vice-Chancellor's 2019 Industry Engagement Award (Highly Commended), Swinburne University of Technology International Convention Scholarship Award (ICSA) 2019/2020, Sarawak Convention Bureau Best Civil Engineering Graduate 2010, Cahya Mata Sarawak IEM Gold Medal Award 2009, Institution of Engineers Malaysia Dr. Choo actively supervises research students and has secured multiple research grants from both industrial sources and the Ministry of Higher Education's Fundamental Research Grant Scheme. His professional engagement extends to numerous industry organizations, where he serves as a committee member and technical expert. As Director of the Centre for Innovative Society, he leads interdisciplinary research initiatives that address regional engineering challenges.

• Time Series Econometrics
• Statistics
• Finance
• +۷ مورد دیگر

Associate Professor Simon Kwok is an academic in the School of Economics at The University of Sydney. He holds a PhD in Economics from Cornell University (2012), along with a BSc (Hons) and MPhil from HKU. His research focuses on time series econometrics, statistics, and finance, with particular expertise in specification tests of time series and point process models, tests of Granger causality, and their empirical applications. His research interests span multiple areas of financial economics: Asset and Derivative Pricing High Frequency Finance Program Evaluation Market Microstructure Credit Contagion Financial Contagion Professor Kwok's recent publications demonstrate a strong focus on cryptocurrency markets, financial bubbles, and high-frequency data analysis. His work often involves collaborations with researchers like Robert Jarrow and Michael Leong. His research has practical implications for risk management, portfolio construction, and understanding market dynamics, as evidenced by his 2023 study finding that high-risk cryptocurrencies generally underperform their low-risk counterparts. His notable scientific contributions include: Developing tests for autocorrelated jump occurrences in financial markets Studying asset price bubble dynamics and explosion time characterization Investigating Bitcoin spillovers and cryptocurrency risk exposures Creating the PCDID Approach for Difference-in-Differences analysis Professor Kwok supervises graduate students in econometrics, statistics, and finance. He is currently advising John BILSEL on "Long term Stock-Bond Correlation Dynamics." His work has received funding for research in financial econometrics and time series analysis, with applications to market microstructure and financial contagion. He is also a member of The University of Sydney Nano Institute, indicating interdisciplinary research interests that extend beyond traditional economics into complex systems analysis.

• Applied Statistics
• Biostatistics
• Data Communications
• +۵ مورد دیگر

Tareq Alodat is a Lecturer in Statistics and Data Science at the Mathematics & Statistics school of La Trobe University in Melbourne, Australia. He simultaneously holds a Postdoctoral Research Fellow position at UNSW Australia's School of Mathematics and Statistics in Sydney. His academic background includes: PhD from La Trobe University, Melbourne, Australia (2016-2019) Dr. Alodat's research expertise spans theoretical and applied statistics with significant contributions to long-range dependent processes, stochastic modeling, and nonparametric methods. His work bridges probability theory with practical applications in ecology, medical research, and spatial statistics. He has developed novel statistical approaches for complex data structures and contributed to distribution theory, particularly with generalized skew-normal distributions. Analysis of his publication trajectory reveals increasing specialization in fractional stochastic differential equations applied to spherical geometry, alongside continued work in ecological statistics. His research demonstrates consistent theoretical development paired with practical applications across diverse domains, with recent publications showing sophisticated mathematical techniques applied to real-world problems. Dr. Alodat serves as a reviewer for several prominent journals including Communications in Statistics-Simulation and Computation, Symmetry-Basel, and Entropy, reflecting his standing in the statistical community.

• 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.