جستجوی استاد راهنمای دانشگاهی

Bin Pengمشاهده پروفایل

استاد

• Econometrics
• Panel Data Analysis
• Time Series Analysis
• +۳ مورد دیگر

Bin Peng is a Professor in the Department of Econometrics and Business Statistics at Monash University. His research focuses on developing novel econometric models and methods, particularly in panel data analysis, time series econometrics, and climate data modeling. He holds a PhD in Econometrics from Monash University (2013) under Professors Giovanni Forchini and Don Poskitt, preceded by a BSc in Mathematics from Nanjing University (2007). His work addresses structural changes in factor models, time-varying parameters in vector error-correction frameworks, and productivity convergence in manufacturing sectors. Key contributions include nonparametric panel models for climate data and methodologies for handling interactive effects in panel data with general factors. Peng has received multiple Dean’s Awards, including the 2021 Early Career Research Excellence Award, 2023 Commendation for Excellence, and 2024 Researcher of the Year. He leads a 2021–2025 project on modeling time trends in panel data, funded by Monash University. His recent articles (2021–2025) emphasize methodological advancements in econometric theory, applied to climate science, economic growth, and macroeconomic policy.

Dan Steinbergمشاهده پروفایل

دانشگاهی

• Probabilistic Machine Learning
• Variational Inference
• Bayesian Deep Learning
• +۷ مورد دیگر

Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).

Yunzhong Houمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Deep Learning
• Multiview Detection
• +۴ مورد دیگر

Dr. Yunzhong Hou is a Research Fellow at the School of Computing, The Australian National University (ANU), where he collaborates with Prof. Tom Gedeon and Dr. Liang Zheng. He holds a PhD in Computer Science from ANU (2019–2023) and a Bachelor's in Electronic Engineering from Tsinghua University (2014–2018). His research focuses on computer vision and deep learning, particularly in multiview detection, sensor optimization, and efficient AI systems. Education: PhD in Computer Science, ANU (2019–2023) Bachelor of Electronic Engineering, Tsinghua University (2014–2018) Research Interests: Multi-view detection and tracking Active vision and sensor optimization Efficient AI systems His work spans topics such as camera configuration optimization for pedestrian detection, deep learning for color quantization, and multi-camera systems. He has contributed to projects like the socioeconomic impact analysis of water reforms and privacy-preserving perception for robotics. Hou serves as a reviewer for top conferences (CVPR, ICCV) and journals (TPAMI, TIP). His research emphasizes scalable solutions for real-world applications, including drone vision control and edge computing optimizations. Current projects include machine learning for socio-economic analysis and privacy-aware robotic perception.

Trung Leمشاهده پروفایل

مدرس

• Deep Generative Models
• Optimization for Machine Learning
• Kernel Methods
• +۶ مورد دیگر

Trung Le is a Lecturer in the Department of Data Science & AI at Monash University. His research focuses on deep generative models, kernel methods, optimization in machine learning, and Bayesian inference, with applications to supervised learning, adversarial learning, and cyber security. He holds a PhD in Computer Science from the University of Canberra (2013). Key projects include the Australian Research Council-funded initiatives such as 'Can Machines Unlearn? Toward Next-Generation Safe Artificial Intelligence' (2025–2029) and 'Trustworthy Generative AI: Towards Safe and Aligned Foundation Models' (2024–2026). His work contributes to UN SDG 4 (Quality Education) through advancements in AI ethics and safety. Education: PhD in Computer Science, University of Canberra (2013) Research Areas: Continual Learning, Diffusion Models, Domain Adaptation, Adversarial Machine Learning Awards: KDD 2023 Best Student Paper Award, Imagine Cup 2010 Australia First Prize Trung has published extensively in top-tier venues like NIPS, ICLR, and JMLR, with over 100 outputs since 2009. His recent work emphasizes robustness in AI systems, including projects on adversarial defense and ethical concept erasure in generative models.

Phil Bellمشاهده پروفایل

دانشیار

• Dinosaur Paleontology
• Vertebrate Ichnology
• Cretaceous Ecosystems
• +۱ مورد دیگر

Dr. Phil Bell is an Associate Professor in Earth Sciences (Palaeontology) at the School of Environmental and Rural Science, University of New England, where he leads the Dino Lab within the Palaeoscience Research Centre. His fieldwork focuses on Cretaceous ecosystems in Canada, Mongolia, and Australia. Qualifications: PhD - University of Alberta BSc (First Class Honours) - Macquarie University Research examines dinosaur evolution, behavior through skin impressions, and Cretaceous paleoecology. Specific interests include dinosaur thermoregulation evidenced by skin morphology, trackway analysis, and taphonomic studies of bonebeds. Australian research concentrates on Lightning Ridge fossil sites. Teaching coordinates geoscience units including: Introductory Palaeontology; Dinosaurs!; Vertebrate Palaeontology; and Applied Geoscience. Recent publications demonstrate expertise in vertebrate ichnology, exceptional soft-tissue preservation, and high-latitude dinosaur faunas. Studies frequently integrate histological analysis and 3D preservation techniques to reconstruct paleobiology.

• Sample Survey Design and Analysis
• Robust Statistical Methods
• Graphical Methods in Statistics
• +۴ مورد دیگر

Professor Raymond Chambers is a distinguished statistician currently serving as an Honorary Fellow at the School of Mathematics and Applied Statistics, Faculty of Engineering and Information Sciences, University of Wollongong (2022–present). His career spans leadership roles at the University of Southampton (2003–2006 as Director, Southampton Statistical Sciences Research Institute; 1995–2000 as Professor and Head of Department of Social Statistics; 1999–2000 as Leverhulme Professor) and the Australian National University (1989–1995 as Senior Lecturer in Statistics). He completed his PhD in Biostatistics at Johns Hopkins University (1979–1983) and has supervised 9 PhD students in areas like Small Area Estimation , Robust Inference , and Survey Methodology . His research focuses on Sample Survey Design and Analysis , Robust Statistical Methods , Statistical Modelling and Inference , and Analysis of Computer-Linked Data . Recent publications emphasize Small Area Estimation , Probabilistic Data Linkage , and Non-Linear Spatial Models , reflecting his expertise in integrating survey data with advanced statistical techniques. Professor Chambers has received multiple accolades, including Elected Member of the International Statistical Institute Elected Fellow of the American Statistical Association Elected Fellow of the Academy of Social Sciences in Australia Leadership roles as President of the International Association of Survey Statisticians (2011–2013) He has secured significant funding from Australian and international agencies, including Discovery Projects and Linkage International grants, supporting research in Small Group Analysis , Missing Data Handling , and Longitudinal Survey Methodology . His professional contributions include editorial roles at the Journal of the Royal Statistical Society and The Annals of Statistics .

Sumonkanti Dasمشاهده پروفایل

پژوهشگر ارشد

• Complex Survey Data Analysis
• Multilevel Time Series Modelling
• Small Area Estimation
• +۳ مورد دیگر

Sumonkanti Das is a Research Fellow at The Australian National University (ANU), affiliated with the Research School of Finance, Actuarial Studies & Statistics and the National Centre for Epidemiology and Population Health. He holds an Honorary Fellowship at the University of Wollongong. His research focuses on complex survey data analysis, small area estimation, and spatial modeling in public health and demography. Das completed his PhD in Statistics at the University of Wollongong, with a focus on robust inference in poverty mapping. Education: PhD in Statistics (2016), University of Wollongong M.Sc. in Statistics Research Interests: Multilevel time series modeling, spatial/spatio-temporal analysis, and application of statistical methods to demographic and health indicators in developing countries. His work bridges statistical theory with practical challenges in public health surveillance and policy. Projects & Grants: He contributed to an NHMRC Idea Grant on smoking prevalence estimation in Australia and an ARC Linkage Project on pest and disease surveillance. His current work emphasizes integrating remote sensing data with demographic surveys for improved health trend analysis. Awards: Honorary Fellow, University of Wollongong (2016–2025) Teaching & Outreach: Coordinates ANU Statistical Support Network courses, bridging academic research with practical statistical training.

Minh Buiمشاهده پروفایل

دانشیار

• Computational Biology
• Evolutionary Biology
• Bioinformatics
• +۳ مورد دیگر

Associate Professor Minh Bui leads the Computational Phylogenomics Lab at the School of Computing, Australian National University (ANU). Previously, he was a Research Fellow at the Max Perutz Laboratories in Vienna, Austria. His research focuses on developing computational methods and models for phylogenetic inference, particularly with the widely used IQ-TREE software. He holds a PhD from the University of Vienna (2009), MSc from the University of Freiburg (2005), and BSc from Vietnam National University (2001). His work bridges computing, biology, and statistics, addressing challenges in evolutionary research using ultra-large genomic datasets. Education: PhD in Biology, University of Vienna, 2009 MSc in Biology, University of Freiburg, 2005 BSc in Biology, Vietnam National University, 2001 His research interests include phylogenetic inference, evolutionary genomics, and bioinformatic tool development. He leads an international team developing IQ-TREE, which is applied in studies ranging from ancient life forms to SARS-CoV-2 evolution. Notable recognitions include being a Web of Science Highly Cited Researcher (2021–2023) and an Australian Field Leader in Evolutionary Biology. Recent projects include improving phylogenetic methods for pandemic-scale data and resolving evolutionary relationships in eukaryotes and plants. His grants support software development, pandemic genomic epidemiology, and phylogenetic model innovation. Minh Bui is actively involved in mentoring students and collaborates globally on computational and evolutionary studies.

John Patersonمشاهده پروفایل

استاد

• Palaeontology
• Cambrian and Ordovician Biotas
• Evolutionary Biology
• +۴ مورد دیگر

Professor John Paterson is a leading palaeontologist at the University of New England , serving as Professor of Earth Sciences within the School of Environmental and Rural Science since 2016. His academic journey began with a B.Sc. (Hons) and Ph.D. from Macquarie University, focusing on Cambrian-Ordovician trilobites. He has held leadership roles, including Head of Earth Sciences (2008–2020) and ARC Future Fellow (2014–2018) , and remains an active member of the International Subcommission on Cambrian Stratigraphy . Research Interests center on documenting the palaeo-biodiversity of Australian Cambrian and Ordovician biotas (444–541 million years ago), using fossils to address evolutionary, biogeographic, and palaeoecological questions. His work spans taxonomy, phylogeny, biostratigraphy, and the refinement of the geologic timescale through global strata correlation. Recent publications highlight his focus on the Emu Bay Shale Lagerstätte , including studies on trilobite predator-prey dynamics , radiodont vision , and arthropod biomechanics . These works integrate finite-element analysis , laser micropyrolysis , and multi-proxy stratigraphy to explore evolutionary drivers during the Cambrian Explosion. Scientific Awards include the Anton Hales Medal (2016), UNE Vice Chancellor’s Award for Excellence in Research (2009), and the Charles Marshall Award (2001). He has secured major grants such as ARC Discovery Projects and National Geographic Society grants , totaling over AUD $350,000. Academic Leadership includes editorial roles at Alcheringa and Zootaxa , organizing international conferences like Palaeo Down Under 2 , and coordinating multidisciplinary teams for projects on the Flinders Ranges and Georgina Basin . His work bridges fieldwork, lab analysis, and global collaborations to unravel the complexities of early animal evolution.

Nan Zouمشاهده پروفایل

مدرس

• Time series analysis
• Extreme value theory
• Resampling-type methods
• +۳ مورد دیگر

Nan Zou is a Lecturer in Statistics at Macquarie University's School of Mathematical and Physical Sciences. She holds a Ph.D. in Mathematics with a Specialization in Statistics from the University of California San Diego (2017) and a B.B.A. in Management Science from Renmin University of China (2011). Her research focuses on statistical methodology and theory in time series analysis, extreme value theory, resampling techniques, and dynamical systems. She has collaborated internationally and received the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Her recent work addresses bias correction in extreme value analysis, bootstrap methods for unit root testing, and multivariate time series extremes. Key contributions include reweighted madogram estimators and seasonal unit root tests under periodic variation. Current research emphasizes statistical inference for massive datasets and dynamical systems. Nan Zou actively supervises PhD and MRes students at Macquarie University, encouraging applications from eligible candidates. She has published in journals like the Annals of Statistics and Journal of Nonparametric Statistics , with a focus on methodological advancements in statistical theory.

• Survival Analysis
• Generalised Linear Mixed Models
• Asymptotic Theory
• +۳ مورد دیگر

Aishwarya Bhaskaran is a Lecturer in Theoretical Statistics at the School of Mathematical and Physical Sciences, Macquarie University. She holds a PhD in Statistics from the University of Technology Sydney (2023), followed by a postdoctoral position at Macquarie University. Her research focuses on theoretical and methodological advancements in survival analysis, generalized linear mixed models (GLMMs), asymptotic theory, and variational approximation methods. She is particularly interested in Bayesian inference and enhancing scalability for large datasets. Her teaching includes courses on Generalized Linear Models (STAT7111/8111) and Statistical Inference for Data Science (STAT3191/6191). Research interests include bridging statistical theory with real-world applications. Notable contributions span asymptotic improvements for GLMMs, dispersion parameter extensions, and variational methods for complex models. Her work emphasizes computational efficiency and practical applicability. Recent collaborations highlight international networks in statistical methodology and applications. While no specific awards are named, her research has garnered citations across peer-reviewed journals. She actively participates in academic networks and contributes to advancing statistical theory and its computational implementation.

• Data Management
• Database Systems
• Graph Data Management
• +۴ مورد دیگر

Professor Chengfei Liu is a faculty member at Swinburne University of Technology, leading the Web and Data Engineering research group and serving as focus area leader for Knowledge and Data Intensive Systems in the SUCCESS Centre. His research focuses on web data management, advanced database systems, graph data, and workflow models. He has held academic positions at the University of South Australia, University of Technology Sydney, and the University of Queensland's DSTC. His work spans data management, distributed systems, and information systems, with notable contributions to cohesive subgraph discovery and efficient algorithms. Research interests include keyword queries, uncertain data, graph databases, and artifact-centric workflows. His awards include the Vice-Chancellor's Research Excellence Award (2007) and multiple best paper/demo awards at top conferences. Professor Liu supervises numerous PhD students and holds grants from the Australian Research Council (ARC), including projects on dynamic networks, heterogeneous graphs, and information resilience. He has authored over 297 publications, with recent work addressing topics like Bitcoin transaction prediction, federated subspace clustering, and multimodal eating disorder detection. His professional roles include program committee memberships at SIGMOD, ICDM, and CIKM, reflecting his influence in data science and database research.

• Efficiency and Productivity Analysis
• Sustainability
• Environmental and Social Performance
• +۳ مورد دیگر

Dr. Amir Moradi is a Senior Lecturer in Accounting at the School of Business, Law and Entrepreneurship, Swinburne University of Technology. He holds a PhD in Engineering from the same institution and specializes in efficiency and productivity analysis, sustainability, and environmental/social performance. His research spans sectors like universities, banking, and mining, with publications in high-impact Q1/Q2 journals such as Annals of Operations Research and Economic Modelling . Education: PhD in Engineering (Swinburne University of Technology), focused on mathematical and modeling techniques for efficiency analysis. Research interests include: Efficiency analysis of public and private sectors Sustainability and environmental performance metrics Banking and financial sector efficiency Integrated reporting and corporate governance Key research trends in his articles focus on: Quantifying impacts of carbon pricing and climate policies Methodological advancements in data envelopment analysis (DEA) Post-GFC banking efficiency and risk management Superannuation withdrawal effects on retirement savings Grants include funding from AFAANZ and Swinburne for projects on ESG integration in portfolios, integrated reporting impacts, and Malaysian corporate efficiency. He has supervised numerous PhD/Master’s candidates on topics like banking performance in Vietnam, airline inefficiency drivers, and renewable energy policy.

• High-dimensional inference
• Forecasting
• Semi-parametric Bayesian inference
• +۴ مورد دیگر

Didier Nibbering is an Assistant Professor (Lecturer) in the Department of Econometrics and Business Statistics at Monash University, Faculty of Business and Economics, Melbourne, Australia. His research lies at the intersection of econometrics, statistics, and computational methods. Research Interests: His primary research areas include high-dimensional inference, forecasting, and semi-parametric Bayesian inference. He works on developing and applying advanced statistical models for complex economic and financial data, particularly in contexts involving large-dimensional datasets and latent variable structures. The recent publications indicate a strong focus on Bayesian computational techniques, state space models, variational inference, and forecasting applications in economics and environmental modeling. His work combines theoretical rigor with practical applications in policy-relevant domains such as carbon emissions forecasting. Scientific Contributions: Developed novel hybrid MCMC methods for high-dimensional latent variable models. Advanced variational approximations for state space models using importance sampling. Proposed asymmetric grouping methods for improved forecasting of carbon emissions. Contributed to a comprehensive modern review of Bayesian forecasting in economics and finance. Introduced high-dimensional extensions to multinomial logit models. Advising and Grants: While specific details about PhD students or grant funding are not provided in the text, his active publication record and collaborations suggest ongoing research supervision and external funding involvement typical for early-career faculty. He has collaborated with prominent researchers in econometrics such as Gael Martin, David Frazier, and Richard Paap. Labs and Teams: There is no explicit mention of lab affiliations or research teams, but his work appears to be part of the broader econometrics and statistics research group at Monash University, with international collaborations including Stanford University.

Longbing Caoمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Professor Longbing Cao is the Distinguished Chair in Artificial Intelligence and Director of the Frontier AI Research Centre at Macquarie University. He holds dual PhDs in Artificial Intelligence and Computing Science, an MSc in Communication, and a Bachelor's degree in Electrical Engineering. His research spans AI, data science, behavior informatics, and enterprise innovation, with over 400 publications and significant industry collaborations. Education: PhD in Pattern Recognition and Intelligent Systems, Graduate University of Chinese Academy of Sciences PhD in Computing Science, University of Technology Sydney MSc in Communication Bachelor's in Electrical Engineering Research Interests: Focuses on AI systems, federated learning, time series forecasting, and humanoid robotics. His work integrates theoretical advancements with real-world applications in finance, healthcare, and smart cities. Recent Articles: Explores cutting-edge topics like generative AI (ProgDiffusion), federated learning (FedSI), and pandemic modeling (waning effect of interventions). These contributions highlight advancements in both foundational AI and applied data science. Awards: Eureka Prize (2019), ACM Distinguished Scientist (2019) Grants & Projects: Leads major initiatives such as the Federated Omniverse Facilities for Smart Digital Futures (2024-2025) and Data Complexity and Uncertainty-Resilient Deep Variational Learning (2024-2027). Prior roles include Founding Director of UTS Advanced Analytics Institute (2011-2014). Labs & Teams: Directs the Frontier AI Research Centre and collaborates with global institutions like Microsoft, Commonwealth Bank, and Shanghai Stock Exchange.