جست‌وجوی استاد و پژوهشگر

Arthur ter Hofstedeمشاهده پروفایل

استاد

• Business Process Automation
• Process Mining
• Data Quality
• +۳ مورد دیگر

Arthur ter Hofstede is a Professor in the Faculty of Science and Head of the School of Information Systems at Queensland University of Technology (QUT). He previously held a Professorship at Eindhoven University of Technology (2010–2018) and has been a Visiting Professor at Tsinghua University (China), Sapienza University of Rome (Italy), and Sun Yat-sen University (since 2015). His research focuses on Business Process Automation (BPM), Process Mining, and Data Quality, with particular emphasis on the YAWL workflow system. He has supervised numerous PhD students in areas like process-data governance and gamified data cleaning. His work bridges academia and industry, addressing challenges in healthcare, insurance, and logistics. Key contributions include frameworks like ProcessProfiler3D for performance visualization and OrgMining 2.0 for organizational model mining. Awards include prestigious visiting scholar appointments and leadership roles in international collaborations. Education: Ph.D. and M.Sc. from Katholieke Universiteit Nijmegen (Netherlands). Research projects include Australian Competitive Grants on topics like cost-aware BPM and liquid process model collections. Notable publications explore event log imperfection patterns, privacy-preserving process mining, and robotic process automation integration. Grants: DP150103356 (Liquid Process Model Collections), DP120101624 (Cost-aware BPM) Labs/Teams: Centre for Data Science, YAWL Development Team International Collaborations: Sapienza University, Tsinghua University, Sun Yat-sen University

Alan Hájekمشاهده پروفایل

استاد

• Philosophy
• Probability Theory
• Formal Epistemology
• +۴ مورد دیگر

Professor Alan Hájek is a faculty member at the Australian National University, affiliated with the School of Philosophy, Centre for Moral, Social and Political Theory, and Centre for Philosophy of the Sciences. His work bridges formal epistemology, probability theory, and decision theory. Expertise: Counterfactual reasoning, Bayesian epistemology, and infinite utility paradoxes. Roles: Professor in multiple interdisciplinary philosophy centers. His research interests focus on the foundations of probability, decision-making under uncertainty, and the intersection of formal methods with philosophical questions. Recent publications analyze counterfactual semantics, Pascal’s Wager, and triviality results in probabilistic frameworks. The 15 most recent articles span 2021–2025, emphasizing counterfactual logic (e.g., Most counterfactuals are false ), decision theory (e.g., Satan, Saint Peter and Saint Petersburg ), and epistemic paradoxes (e.g., The cable guy paradox ).

• Process Mining
• Anomaly Detection
• Blockchain
• +۳ مورد دیگر

Ko, Jonghyeon is a researcher affiliated with the Ulsan National Institute of Science and Technology (UNIST) , specifically the Department of Materials Science and Engineering within the College of Natural Science and Engineering. His work spans multiple disciplines including process mining, anomaly detection, blockchain technology, AI computing, and environmental engineering. His research interests include: Anomaly detection in business process event logs Blockchain-based systems for nuclear/radioactive waste management AI computing using neuromorphic devices Statistical leverage and information-theoretic approaches to process mining Optimization of autonomous vehicle safety systems Recent publications demonstrate expertise in developing formal languages for data quality simulation, probabilistic trace alignment methods, and practical tools for anomaly detection like AIR-BAGEL. While no explicit scientific awards are mentioned in the text, his work has been published in venues such as Information Systems , npj Unconventional Computing , and Expert Systems with Applications .

Malcolm Sambridgeمشاهده پروفایل

استاد

• Geophysics
• Seismology
• Inverse Problems
• +۴ مورد دیگر

Malcolm Sambridge is a Professor of Seismology and Mathematical Geophysics at the Research School of Earth Sciences (RSES), Australian National University (ANU). He holds roles including former Head of Seismology and Mathematical Geophysics (2006–2016) and has been a Fellow and Research Fellow at ANU since 1992. His research focuses on inverse problems, computational geophysics, seismic wave propagation, and statistical inference applied to Earth Sciences. He has led projects such as the Australian Passive Seismic Server and the Australian Seismometers in Schools Network (AuSIS). Education includes a B.Sc. in Physics (1983, Loughborough University), a Certificate in Advanced Mathematics (1984, University of Cambridge), and a Ph.D. in Geophysics (1988, ANU). His career includes visiting roles at Caltech and the Carnegie Institution of Washington. Research interests emphasize developing algorithms for geophysical inference, including the Neighbourhood Algorithm for nonlinear inversion. Key contributions include studies on Earth's inner core structure, seismic tomography, and Bayesian methods. He advises students across physics, mathematics, and Earth Sciences, focusing on computational and theoretical geophysics. Publications span over 150 articles, with recent work on optimal transport for inversion, trans-dimensional Bayesian tomography, and seismic imaging techniques. His software tools, like pyprop8 and TerraWulf, support geophysical modeling and high-performance computing.

Xiongcai Caiمشاهده پروفایل

دانشیار مدعو

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Dr. Xiongcai Cai is an Adjunct Associate Professor at the School of Computer Science and Engineering, University of New South Wales (UNSW). With expertise in Artificial Intelligence , Machine Learning , and Computer Vision , he contributes to advancing Recommender Systems , Natural Language Processing , and Health Informatics . His work bridges theoretical and applied research in technology for human-centric applications. Current roles: Adjunct Associate Professor, UNSW School of Computer Science and Engineering Key research areas: Machine Learning, Recommender Systems, Computer Vision, Generative AI Dr. Cai's research portfolio demonstrates a consistent focus on recommender systems and machine learning over the past decade. His technical contributions span graph convolutional networks , temporal bilinear models , and embedding techniques for collaborative filtering. Recent work in 2025 addresses knowledge distillation for GCNs-based recommenders, while earlier studies tackled cold-start transitions and matrix factorisation boosting. His publication history (2 book chapters, 7 journal articles, and 36 conference papers) reveals a strong emphasis on real-time applications in domains like gait recognition (2020), health data analytics (2016), and social network recommendation (2010-2015). The research applies mathematical rigor to practical challenges in online dating platforms , medical decision support , and object tracking systems . Contact details: Email: x.cai@unsw.edu.au Phone: +61 2 9385 8858

Nicholas Wormaldمشاهده پروفایل

استاد

• Combinatorics
• Random Graphs
• Graph Theory
• +۴ مورد دیگر

Nicholas Wormald is a Professor in the Department of Mathematical Sciences at Monash University, Australia. He joined the university in 2013 as an Australian Laureate Fellow, following a decade as a Canada Research Chair in Combinatorics and Optimization at the University of Waterloo, Canada. His primary research focuses on combinatorics, probabilistic methods, and random structures, with notable contributions to random graph theory, enumeration, and applications in optimization. Wormald holds a PhD from the University of Newcastle. His work spans topics including Hamilton cycles in random regular graphs, k-core emergence, probabilistic combinatorics, and algorithms for graph generation. He has contributed to foundational results such as the sudden emergence of giant components in random graphs and the contiguity of random graph models. He serves on editorial boards of journals like the Electronic Journal of Combinatorics and Random Structures and Algorithms . His research interests also include underground mine optimization and the study of Steiner trees. Notable projects include enumeration and random generation of contingency tables, properties of large discrete structures, and the analysis of random structures' applications. Wormald has authored or co-authored over 250 publications, with recent work focusing on asymptotic enumeration, hypergraphs, and the probabilistic analysis of combinatorial structures.

• Computational Mechanics
• Structural Crashworthiness
• Additive Manufacturing
• +۵ مورد دیگر

Professor Qing Li is a faculty member at the School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney. He obtained his PhD from Sydney in 2000, underwent postdoc training at Cornell University (2000-2001), and held academic roles at James Cook University (2004-2006). He joined Sydney in 2006 via a Sesqui senior lectureship, becoming Associate Professor in 2010 and Professor in 2014. He served as Director of Postgraduate Studies (2007-2012) and Director of Biomedical Engineering (2017-2019). Education: PhD (University of Sydney, 2000), ME (UTS), ME (Hunan) His research focuses on computational design, multidisciplinary optimization of nonlinear and time-dependent multifunctional materials, and biomedical applications. Key areas include additive manufacturing , biomechanics , and machine learning in structural reliability. Recent publications emphasize fracture modeling in biomaterials, reliability analysis, and tissue scaffold design. Recent publications highlight Bayesian learning for robotic systems, probabilistic transformation in reliability analysis, and phase field fracture models for additively manufactured composites. Collaborations with industry partners like Cochlear and Stryker span ARC , NHMRC , and MRFF projects. Awards: Clarivate Highly Cited Researcher (2020), Top 50 Australia Research Leader (2020), APACM Computational Mechanics Award (2016) Fellowships: ARC Future Fellow (2013-2017), ARC Australian Postdoctoral Fellow (2001) He supervises PhD students in projects like epidermal electrodes , Silver Diamine Fluoride remineralization , and virtual surgical planning . His leadership extends to the Centre for Advanced Materials Technology and editorial roles in computational methods journals.

Alistair Barrosمشاهده پروفایل

استاد

• Service Computing
• Enterprise Systems
• Business Process Management
• +۵ مورد دیگر

Professor Alistair Barros is Head of School and Academic Program Leader of Service Science at QUT’s School of Information Systems (Faculty of Science). He holds a PhD from the University of Queensland, with extensive ICT experience in academia and industry (e.g., Global Research Leader at SAP AG). His research focuses on enterprise systems design, optimization, and interoperability in cyber-physical environments leveraging cloud, IoT, and blockchain technologies. Key areas include service computing methods (microservices, business process management), model-based systems re-engineering, and distributed service coordination. Education: PhD in Information Systems from the University of Queensland. Research Interests: Enterprise systems modernization, service-oriented architectures, Industry 4.0 applications, and digital transformation in sectors like construction, manufacturing, and supply chains. He has published 180+ peer-reviewed articles (including 6 edited books) and holds 17 US patents. Notable contributions include the BPMN 2.0 standard co-authorship and the 'Workflow Patterns' paper (most cited in BPM). Awards: Test of Time Award (BPM 2015), Wharton Education Award (TeachConnect platform), and impactful industry collaborations (e.g., SAP NetWeaver, Federal Government WPIT project). Grants & Projects: Led ARC, EU FP6, and industry-funded projects. Current focus includes re-engineering legacy systems for microservices, EV charging network design, and adversarial ML in cybersecurity. Labs/Teams: Active in QUT’s Centre for Data Science, focusing on service science and cyber-physical systems integration.

Michael Burkeمشاهده پروفایل

مدرس ارشد

• Robotics
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Dr. Michael Burke is a Senior Lecturer and Deputy Graduate School Coordinator at the Department of Electrical and Computer Systems Engineering, Monash University. He specializes in robotics, focusing on probabilistic machine learning and computer vision for autonomous systems. His roles include research supervision, curriculum development, and fostering graduate culture. Education: PhD in Statistical Signal Processing, University of Cambridge (2012–2016) MSc in Electronic Engineering, Stellenbosch University (2009–2011) BEng in Electronic Engineering, University of Pretoria (2005–2008) Research Interests: Robot learning and control Probabilistic machine learning for robotics Interpretable robotics systems and hybrid control architectures Robot perception and visuomotor control His work bridges robotics with machine learning to develop safe, adaptive autonomous systems for applications like surgical robotics and field robotics. Awards and Recognition: Best Paper Award at AMDO 2014 Runner-Up for Best Paper at CoRL 2019 Advising & Grants: Supervising over 20 PhD and Master’s students in robotics and related fields Principal Investigator for ARC-funded projects on robotic pharmaceutical formulation and human-robot interaction (2024–2027) Labs and Teams: Past leadership of the Mobile Intelligent Autonomous Systems group at CSIR, South Africa (2009–2018) Current collaborations with researchers at the University of Edinburgh and University of Melbourne

Dan Steinbergمشاهده پروفایل

عضو هیئت علمی

• 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).

• Bayesian Statistics
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Dr. Yanan Fan is a Senior Principal Research Scientist at CSIRO's Data61 and an Adjunct Professor of Statistics at the University of New South Wales (UNSW). His research focuses on Bayesian models, computational methods for real-world problems, and interdisciplinary applications in fields like medical imaging, cosmology, and climate science. He holds a PhD in Statistics from the University of Bristol and has over 20 years of academic experience at UNSW's School of Mathematics and Statistics. Education: PhD in Statistics, University of Bristol, UK Undergraduate Degree in Mathematics, University of Melbourne Research Interests: Fan develops Bayesian semiparametric models, approximate Bayesian computation (ABC), and scalable computational methods for medical imaging (e.g., PET), cosmology, and climate modeling. He also investigates gender bias in educational evaluations and leads initiatives like the Data4Good stream of UDASH. His work emphasizes practical problem-solving through advanced statistical techniques. Leadership & Contributions: As Team Leader of Bayesian Computational Methods and Applications at Data61, he drives innovation in statistical methodologies. He has served on the Scientific Committee of MATRIX research institute and as an Associate Editor for major statistical journals. His projects include probabilistic climate projections and bias analysis in student evaluations. Labs & Groups: Active member of the StatML Group and leader of the Bayesian Computational Methods team, focusing on integrating machine learning and statistical computing.

• Robotics
• Autonomous Systems
• Data Fusion
• +۷ مورد دیگر

Hugh Durrant-Whyte is a Professor at the University of Sydney and Director of the Centre for Translational Data Science. He holds a BSc (Eng) from the University of London, an MSE, and a PhD from the University of Pennsylvania. Previously, he served as CEO of National ICT Australia (NICTA) and Director of the Australian Centre for Field Robotics (ACFR). His research focuses on robotics, autonomous systems, and data fusion, with over 350 publications and four successful startups. Education: BSc (Eng) in Engineering, University of London MSE in Robotics, University of Pennsylvania PhD in Robotics, University of Pennsylvania Affiliations: Director, Centre for Translational Data Science Former CEO, NICTA (2010-2014) Former Director, ACFR (1995-2010) His research interests span robotics, autonomous systems, and sensor networks. Notable contributions include foundational work on SLAM (Simultaneous Localization and Mapping) and decentralized data fusion. He has pioneered applications in mining automation, autonomous vehicles, and environmental modeling. His work emphasizes practical real-world systems, with projects like autonomous straddle carriers for container terminals and terrain mapping for mining operations. Awards & Honors: NSW Scientist of the Year (2010) Fellow of the Royal Society (FRS), Australian Academy of Science (FAA), and IEEE (FIEEE) Recipient of multiple IEEE Best Paper awards Grants & Labs: Leadership in securing multi-million-dollar grants for robotics and data science initiatives Centre for Translational Data Science: Focuses on translating data science into real-world impact He advises on numerous government and industry projects, bridging academic research with industrial applications. His research teams have developed influential algorithms for autonomous navigation and multi-agent systems.

• Composite Materials
• Structural Mechanics
• Fracture Mechanics
• +۵ مورد دیگر

Dr. Muni Rami Reddy Rasappagari is a casual academic staff member at the University of Southern Queensland (UniSQ) , Australia, within the School of Engineering . He holds a Diploma in Civil Engineering from SV Govt Polytechnic Tirupati, a Bachelor of Technology from Nagarjuna University, a Master of Science in Civil Engineering from Indian Institute of Technology (IIT), and a PhD from the University of Madras . His research spans composite materials , structural mechanics , fracture mechanics , and nanomaterials , with a particular focus on graphene-reinforced composites , functionally graded materials , and delamination modeling . His work integrates finite element analysis , fractal methods , and vibration analysis to address complex structural integrity problems. Dr. Rasappagari's recent publications (2018–2022) emphasize graphene nanoplatelet (GPL) reinforcement in composite plates, exploring free and forced vibration , flexural behavior , and boundary condition effects . Earlier work (2007–2009) centered on fractal finite element methods for crack sensitivity analysis and stress intensity factor computation in anisotropic and multi-crack systems . Contact: 📧 muniramireddy.rasappagari@unisq.edu.au

• Software Engineering
• Parallel and Distributed Systems Architecture
• Trusted Components and Services
• +۶ مورد دیگر

Professor Heinrich Schmidt is an Adjunct Professor in the School of Science at RMIT University, Australia. His research focuses on Software Engineering, Distributed Systems, and Cyber-Physical Systems. He specializes in areas such as formal verification, safety-critical systems, and cloud computing. His work emphasizes practical applications in industrial automation, IoT, and HPC environments. Key research interests include spatio-temporal analysis, fault tolerance, and adaptive systems design. He has supervised projects on IoT data contextualization, software fault characterization, and spatial modeling in PRISM. Over 98 publications highlight his contributions to formal methods, distributed systems, and industrial software solutions. Professor Schmidt collaborates on projects like Chiminey (cloud/HPC integration) and VxLab (industrial visualization). His teaching covers parallel systems, trusted components, and model-based monitoring. No specific awards are listed, but his extensive publication record underscores his academic impact.

Mohammad Kazemi Beydokhtiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Natural Language Processing (NLP)
• Geospatial Domain
• +۵ مورد دیگر

Mohammad Kazemi Beydokhti is a Research Fellow (Level A) at RMIT University's Research & Innovation Capability department. He is also a PhD candidate with expertise in Machine Learning, NLP, and Geospatial Domain applications. His research focuses on qualitative spatial reasoning, GeoQA systems, knowledge graphs, and LLMs, with notable contributions to projects like Dynamic Vicmap (awarded the 'Innovation Award' by GCA) and the RMIT AWS Cloud Supercomputing Hub. He has collaborated with institutions like Utrecht University and has taught courses such as Advanced Imaging Technology (GEOM2112), Geospatial Programming with Python (GEOM2157), and Applied Geospatial Techniques (GEOM2450) as a tutor at RMIT from July 2021 to August 2024. His scientific contributions span geospatial question-answering systems, probabilistic spatial reasoning, and spatial-temporal modeling of seismic activity. Key projects include the DBSCAN-based seismic province analysis in Iran and ANP-OWA method applications in air quality monitoring station placement. Research Awards: Innovation Award (GCA) for Dynamic Vicmap project Labs/Teams: Involved in RMIT's AWS Cloud Supercomputing Hub and geospatial collaboration networks