جست‌وجوی استاد و پژوهشگر

Justin Kennedyمشاهده پروفایل

پژوهشگر

• Cyber-Physical Systems
• Marine Engineering
• Control Systems
• +۳ مورد دیگر

Dr Justin Kennedy is a researcher at Queensland University of Technology (QUT), affiliated with the School of Electrical Engineering and Robotics and the QUT Centre for Robotics. His research focuses on cyber-physical systems, quickest change detection problems, and bushfire simulations. He collaborates with Prof Daniel Quevedo on adaptive wave filtering, Dr Jasmin Martin on detection algorithms, and Dr Troy Bruggemann on bushfire modeling. Kennedy holds a PhD from QUT (2020), with his thesis addressing nonlinear disturbance observers for marine systems in collaboration with Australia's Defence Science and Technology Group. He graduated in 2016 with dual degrees in Electrical Engineering (First Class Honours) and Mathematics (Distinction). His research interests include Bayesian methods for system modeling, uncertainty quantification in dynamical systems, and control strategies for marine engineering challenges. Notable achievements include a Conference Young Author Award for work on wave-induced ship motion prediction using Bayesian techniques. He contributes to interdisciplinary projects such as NICCI (Network-Informed Control) exploring multi-technology networks. Current research explores privacy-preserving state estimation in cyberphysical systems and robust change detection mechanisms. Dr Kennedy’s publications span topics from parametric roll resonance detection to privacy-aware remote control systems, reflecting his expertise in blending engineering principles with advanced mathematical techniques. His work bridges theoretical advancements with real-world applications in maritime safety and autonomous systems.

Erin Petersonمشاهده پروفایل

دانشیار مدعو

• Environmental Monitoring
• Spatial and Temporal Statistics
• Geographic Information Science (GIS)
• +۳ مورد دیگر

Dr. Erin Peterson is an Adjunct Associate Professor at Queensland University of Technology (QUT), affiliated with the Faculty of Science, Tier 1 Research Centre - Science (U91), and the Centre for Data Science. She is also an Associate Investigator at the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) and the owner of EP Consulting in Brisbane. With over 20 years of experience, her work bridges natural resource management, GIS, and environmental statistics, focusing on innovative environmental monitoring using new technologies like in situ sensors and citizen science. Education: B.S. in Conservation Forestry (Michigan State University), M.S. and Ph.D. in Earth Resources (Colorado State University). Prior roles include Senior Research Scientist at CSIRO and leader of the Environmental & Agricultural Informatics team. Research interests include spatio-temporal modeling, adaptive monitoring frameworks, and integrating diverse datasets for ecological assessments. Key projects involve virtual reality for conservation modeling, jaguar population studies, and Great Barrier Reef monitoring. She teaches spatial statistical modeling courses and develops tools to make complex methodologies accessible to non-statisticians. Grants include leadership in Agri-Intelligence for Cotton Production and sustainability reporting projects. Collaborations span government, industry, and international institutions.

Byrenn Birchمشاهده پروفایل

مدرس ارشد

• Hypersonic Propulsion
• Aerothermodynamics
• Thermofluids Engineering
• +۳ مورد دیگر

Dr Byrenn Birch is a Senior Lecturer in Thermofluids Engineering at the School of Engineering, University of Southern Queensland. He holds a BEng and PhD from USQ. His research focuses on hypersonic propulsion, aerothermodynamics, and hypersonic wind tunnel technology. Birch is affiliated with the Institute for Advanced Engineering and Space Sciences and has contributed to major projects like the Australian Airborne Observation of Hayabusa2 reentry. His research interests emphasize experimental and computational analysis of hypersonic flows, spacecraft reentry dynamics, and advanced thermal protection systems. Key projects include optical tracking of hypersonic free-flight, Bayesian state estimation techniques, and shock layer radiation analysis of returning spacecraft. Birch teaches ENG1100 Introduction to Engineering Design , MEC2402 Stress Analysis , and MEC4104 Renewable Energy Technology . With over 3,799 research outputs viewed and 1,198 downloads, his work bridges fundamental fluid dynamics with aerospace applications. His recent publications (2020–2025) explore hypersonic facility design, reentry spectroscopy, and transient heat flux measurement innovations. Notable contributions include developing heated barrel modes for Ludwieg tubes and improving surface temperature identification methods for sample return capsules. Birch collaborates extensively with international space agencies and maintains an ORCID profile (https://orcid.org/0000-0003-0625-2455).

• Complex Systems Theory
• Dynamical Systems Theory
• Complex Networks
• +۷ مورد دیگر

Michael Small is a Professor and the CSIRO-UWA Chair of Complex Systems at the University of Western Australia (UWA), holding dual appointments with UWA’s Faculty of Engineering and Mathematical Sciences and CSIRO’s Mineral Resources division. As Director of the UWA Data Institute, he leads research in complex systems theory and dynamical systems, with applications to health, mining, and energy infrastructure. His academic roles include Deputy Editor-in-Chief of Chaos and Editor of Physica A . He holds a PhD in Applied Mathematics (UWA, 1998) and a BSc(Hons) in Pure Mathematics (UWA, 1994). His research focuses on mathematical modeling of complex systems, including disease transmission networks, mental health dynamics, and industrial process optimization. Notable projects include the Transforming Indigenous Mental Health & Wellbeing initiative and collaborations with mining giants like BHP and Rio Tinto. His awards include the V. Afraimovich Award (2022) and UWA Senior Research Award (2016). Recent work spans reservoir computing, network science, and epidemic modeling, with 354 publications and leadership in 18 grants. His interdisciplinary approach bridges academia and industry, addressing challenges in data-driven decision-making and sustainability.

John Alroyمشاهده پروفایل

دانشیار

• Biodiversity Loss
• Extinction Biology
• Paleontology
• +۳ مورد دیگر

John Alroy is an Associate Professor in the School of Natural Sciences at Macquarie University, specializing in extinction biology, paleontology, and macroecology. His research focuses on understanding patterns of biodiversity loss, particularly in mammals and marine organisms, and their implications for conservation. He has led projects such as 'Global patterns of mammalian biodiversity loss over the last 50,000 years' and contributed to datasets like the Paleobiology Database. Key research interests include the interplay between ecological communities and extinction events, spatial sampling biases in fossil records, and the application of statistical methods to biodiversity studies. His work bridges paleontology, ecology, and conservation, with a focus on informing modern conservation strategies through historical ecological data. Grants & Awards: Recipient of the Future Fellowship FT0992161 (2010-2012). Projects: MQ Safety Net: Explaining Global-scale Gradients in Forest Dynamics and Biodiversity (2019-2019). Key Contributions: Co-developer of the Paleobiology Database and author of influential works on extinction modeling and biodiversity metrics.

• Cryptography
• Machine Learning
• Database Systems
• +۳ مورد دیگر

Michael Brand is an Adjunct Professor at RMIT University's School of Computing Technologies. His research focuses on cryptography, privacy-preserving technologies, machine learning, and algorithm design. Key areas include zero-knowledge proofs, secure financial systems like FinTracer, and computational theory. His work bridges theoretical foundations with practical applications in data security and real-time processing systems. Research interests span advanced cryptographic protocols, privacy-enhancing machine learning techniques, and distributed computing paradigms. Notable contributions include innovations in blockchain tracing mechanisms and foundational work in statistical estimation theory. He actively publishes in top-tier venues, addressing challenges in secure computation, network analysis, and database systems. Collaborations emphasize interdisciplinary solutions to modern computational and security challenges.

Branko Risticمشاهده پروفایل

پژوهشگر ارشد

• Information Sciences
• Information Fusion
• Statistical Signal Processing
• +۷ مورد دیگر

Dr. Branko Ristic is a Principal Research Fellow at the School of Engineering, RMIT University. His research focuses on information fusion, statistical signal processing, target tracking, and robotics, with applications in defense and security. He leads projects on autonomous systems, sensor networks, and uncertainty modeling. Ristic advises on topics like decentralized multi-platform search, machine learning for tracking, and possibility theory-based methods. His work appears in top journals like IEEE Transactions and Entropy. He offers short courses in target tracking and sensor fusion. Research Interests: Information fusion, statistical estimation, Monte Carlo methods, and field robotics. His recent work emphasizes autonomous search algorithms and threat assessment systems, leveraging possibilistic frameworks and Bayesian networks. Articles span radar technology, IoT localization, and multi-agent systems. Supervision Projects: Includes decentralized search strategies, real-data maritime tracking, and threat valuation networks. Affiliations: RMIT University, School of Engineering (City Campus).

• System Identification
• Stochastic Signal Processing
• Dynamic System Modelling
• +۴ مورد دیگر

Professor Brett Ninness is a faculty member at the University of Newcastle, Australia, holding the position of Professor in the School of Electrical Engineering and Computing. He has served in various administrative roles, including Assistant Dean (Research Training), Deputy Head of Faculty, and Acting Pro Vice Chancellor. His research focuses on dynamic system modeling, system identification, stochastic signal processing, and wireless communications. He has authored over 100 papers and held editorial roles in journals like Automatica and IEEE Transactions on Automatic Control. Education: Brett Ninness earned his BE, ME, and PhD in Electrical Engineering from the University of Newcastle. His teaching spans systems theory, signal processing, digital systems, and communications. Research Interests: He specializes in system identification, noise-robust signal processing, and applications in wireless communications. His work includes developing algorithms for state estimation, Kalman filtering, and nonlinear model identification. Recent projects involve collaborations with industry partners like Bell Labs and Agere Systems. Publications: His most recent works address smoothed state estimation, nonlinear system identification, and distributed Kalman filtering. Articles highlight advancements in stochastic processes, control systems, and Bayesian methods. Awards: Recipient of the 2001 Tall Poppy Award for outstanding scientific contribution. He is a member of editorial boards and international committees, including the IEEE Technical Committee on System Identification and IFAC. Grants & Supervision: Extensive grants in system identification and control theory. Supervised numerous PhD/Master’s students, though specific names are not listed here. Collaborations: Partnerships with institutions like Royal Institute of Technology (Sweden), Linköping University, and Vrije Universiteit Brussel. Active in global research networks through IEEE and IFAC committees.

• Control Engineering
• System Identification
• Bayesian Estimation
• +۵ مورد دیگر

Adrian Wills is an Associate Professor in the School of Engineering (Mechatronics) at the University of Newcastle, Australia. He leads the Mechatronics Engineering program and holds academic appointments since July 2015. His research focuses on Bayesian estimation, system identification, and control engineering, with applications in robotics and mechatronics. Wills has collaborated with institutions globally, including Linköping University, Uppsala University, and the University of British Columbia. He earned his B.E. (Elec.) and Ph.D. from the University of Newcastle in 1999 and 2003, respectively. Teaching expertise includes delivering advanced courses in estimation and optimization within the Mechatronics Engineering program. Administrative roles include program convenor for Mechatronics Engineering. Research highlights include contributions to state-space models, nonlinear system identification, and model predictive control. His work bridges theoretical advancements with practical applications in engineering systems and healthcare. Key collaborations involve Professors Lennart Ljung, Thomas Schön, and Bhushan Gopaluni, among others. His technical contributions span MATLAB toolboxes (e.g., UNIT), FPGA/ASIC implementations for control systems, and interdisciplinary projects in strain measurement using neutron diffraction.

Asal Bidarmaghzمشاهده پروفایل

مدرس ارشد

• Energy Geotechnics
• Underground Climate Change
• Geotechnical Engineering
• +۵ مورد دیگر

Dr. Asal Bidarmaghz is a Senior Lecturer in the School of Civil and Environmental Engineering at the University of New South Wales (UNSW Sydney). Her expertise lies in Energy Geotechnics and Underground Climate Change, focusing on energy geostructures design, urban subsurface thermal management, and synergies between underground and above-ground infrastructure. She holds a position at the Kensington Campus in the Civil Engineering Building (Room CE502). Her research integrates hydro-thermo-mechanical analysis of soils and geotechnical systems, with a particular emphasis on renewable energy applications such as energy tunnels and geothermal systems. Dr. Bidarmaghz has contributed to advancements in thermal testing devices, subsurface thermal modeling, and sustainable infrastructure design. Her work addresses challenges in urban geothermal potential, energy efficiency in underground facilities, and the interplay between natural convection and geothermal systems. Publications highlight her exploration of coupled thermal-hydraulic processes, energy tunnel optimization, and Bayesian parameter estimation in subsurface modeling. While no awards or grants are explicitly listed, her extensive scholarly output reflects a strong focus on innovative solutions for climate-resilient infrastructure. Collaborations likely involve interdisciplinary teams addressing urban subsurface dynamics and geothermal energy integration.

Rohitash Chandraمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Machine Learning
• Climate Science
• +۱۳ مورد دیگر

Dr. Rohitash Chandra is a Senior Lecturer in Data Science at the University of New South Wales (UNSW) School of Mathematics and Statistics. He leads research in AI methodologies (e.g., Bayesian deep learning, neuroevolution) with applications to climate extremes, geoscience models, mineral exploration, biomedicine, and ancient religious text analysis. He is the Data Theme Lead of the ARC ITTC Training Centre for Data Analytics in Minerals and Resources (2020–2025) and a Chief Investigator in NHMRC projects on AI-driven drug repurposing for COVID-19. Education: PhD in Artificial Intelligence (Victoria University of Wellington, 2012), MSc (University of Fiji, 2008), BSc (University of the South Pacific, 2006). Research Interests: His work spans AI-driven climate modeling (cyclone forecasting, paleoclimate analysis), geoscience applications (remote sensing for mineral exploration), medical diagnosis (skin cancer detection, drug repurposing), and AI in humanities (sentiment analysis of religious texts like the Bhagavad Gita). He pioneers methods like Bayeslands for geoscientific parameter estimation and Bayesreef for reef evolution modeling. Articles Trends: Recent work focuses on LLM applications in cultural analysis, economic forecasting frameworks, and Bayesian neural networks for uncertainty quantification. His publications bridge AI with interdisciplinary challenges in earth sciences, healthcare, and social sciences. Grants & Awards: Recipient of the UNSW Science Silverstar Award (2022), Sydney Fellowship (2017–2019), and over $10M in ARC/industry funding. He has supervised 40+ students across PhD, masters, and honours programs. Labs & Teams: Founder of the Transitional Artificial Intelligence Research Group (t-AI). Collaborates with EarthByte Group, UN agencies, and institutions like Imperial College London on projects ranging from Mars landscape modeling to Hindu philosophy LLMs like Vedanta-GPT.

• Survey Methodology
• Multilevel Modelling
• Poststratification
• +۳ مورد دیگر

Dr Lauren Kennedy is a Lecturer at the University of Adelaide's School of Computer and Mathematical Sciences, Department of Mathematical Sciences. Her research focuses on survey methodology, multilevel modeling, poststratification, causal inference, and Bayesian statistical techniques. She specializes in addressing challenges arising from non-representative data and improving inference in social sciences through advanced statistical methods. Her work emphasizes practical applications in public opinion analysis, epidemiological modeling, and policy evaluation. Dr Kennedy is actively involved in supervising postgraduate students in Masters and PhD programs, particularly as a co-supervisor. She maintains an office in 6.56 Ingkarni Wardli Building on North Terrace campus and can be contacted at lauren.a.kennedy@adelaide.edu.au . Key research contributions include innovations in Bayesian workflow, cross-validation methodologies, and the integration of machine learning with traditional survey techniques. Her recent publications highlight advancements in causal inference frameworks and hierarchical modeling approaches for large-scale datasets.

• Environmental Statistics
• Spatio-Temporal Modeling
• Bayesian Hierarchical Models
• +۳ مورد دیگر

Michael Bertolacci is a Senior Research Fellow at the School of Mathematics and Applied Statistics, University of Wollongong. He holds a PhD from the University of Western Australia (2016–2020). His research focuses on large-scale spatio-temporal problems in environmental statistics, including hierarchical Bayesian models for rainfall analysis, nonstationary time series methods, and flux inversion for trace gases. Current work emphasizes offshore engineering applications. He has secured funding from the University of Wollongong and NASA, collaborating on projects like WOMBAT v2.0, a Bayesian flux-inversion framework. His GitHub repositories reflect computational contributions to statistical methods and geospatial modeling.

Patrick Laubمشاهده پروفایل

مدرس ارشد

• Actuarial Science
• Data Science
• Artificial Intelligence
• +۳ مورد دیگر

Patrick Laub is a Senior Lecturer at the UNSW School of Risk and Actuarial Studies, where he teaches courses in artificial intelligence and machine learning with a focus on risk and insurance applications. His academic work bridges the gap between advanced computational methods and practical actuarial problems. Patrick holds a joint PhD in computational applied probability completed between the University of Queensland and Aarhus University. He also possesses degrees in software engineering and mathematics, providing him with a strong interdisciplinary foundation for his research. His research focuses on computationally challenging problems in actuarial data science, with particular emphasis on natural catastrophe modeling and artificial intelligence applications. Key areas of investigation include Hawkes processes for modeling contagion in insurance claims, Approximate Bayesian Computation for fitting complex insurance loss models, and Empirical Dynamic Modeling for analyzing complex temporal dependencies. His work addresses critical challenges in risk assessment, particularly for extreme events where traditional statistical methods may be inadequate. Analysis of Patrick's recent publications reveals a strong trend toward integrating advanced statistical methodologies with practical actuarial applications. His work spans from theoretical developments in point processes and Bayesian inference to practical implementations in computational environments. A notable theme is the application of machine learning techniques to traditional actuarial problems, particularly in the areas of catastrophe modeling and risk prediction. Patrick has developed and taught innovative courses since 2022, including 'Artificial Intelligence & Deep Learning and their Applications to Risk and Insurance' (ACTL3143 and ACTL5111) and 'Statistical Machine Learning for Risk and Actuarial Applications' (ACTL5110). These courses reflect his commitment to preparing students for the evolving landscape of data-driven risk management in the insurance industry. His research is supported by UNSW's strong infrastructure for computational research, though specific lab affiliations are not explicitly mentioned in the available information. Patrick maintains an active research program with numerous publications across statistics, actuarial science, and computational methods.

Erik Meijeringمشاهده پروفایل

استاد

• Biomedical Imaging
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Erik Meijering is a Professor of Biomedical Image Computing at the University of New South Wales (UNSW), School of Computer Science and Engineering. His research focuses on developing deep learning and machine learning methods for biomedical imaging analysis, including cell tracking, neuron reconstruction, and image segmentation. He holds an IEEE Fellowship and has served in key roles such as Chair of the IEEE Signal Processing Society's Bio Imaging and Signal Processing Technical Committee. Educations: MSc in Electrical Engineering (Delft University of Technology, 1996), PhD in Medical Image Analysis (Utrecht University, 2000). He previously worked at Erasmus University Medical Center (2002–2019) and Swiss Federal Institute of Technology (2000–2002). Research interests include biomedical image computing, computer vision, and the application of AI in microscopy and clinical imaging. Notable projects include the Cell Tracking Challenge and BigNeuron initiative, which benchmark computational methods in bioimage analysis. His work spans over 300 publications, including high-impact journals like Nature Methods and IEEE Transactions on Medical Imaging. He has supervised numerous PhD, Master's, and Bachelor's students, contributing to advancements in medical image analysis and AI-driven diagnostics.