جست‌وجوی استاد و پژوهشگر

• 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.

• Computer Vision
• Machine Learning
• Control Engineering
• +۶ مورد دیگر

Dr. Dimity Miller is a Lecturer at the Faculty of Engineering , Queensland University of Technology , specializing in Computer Vision , Machine Learning , and Robotics . With a Doctoral Degree and Bachelor of Engineering (Mechatronics, Robotics) from QUT, her research focuses on uncertainty quantification, open-set recognition, and robust object detection systems. Her work spans interdisciplinary domains including Internet of Things , Adversarial AI Defense , and Autonomous Systems . Recent publications analyze topics such as probabilistic object detection , backdoor attack mitigation , and electric vehicle charge prediction . She explores methods like dropout sampling , neural radiance fields , and vision-language models . Key themes in her research include: Model Robustness in Open-Set Conditions Uncertainty Propagation in IoT Human-AI Collaboration in Image Analysis As an active researcher in QUT Centre for Robotics , Dr. Miller contributes to advancing AI reliability in safety-critical applications through both theoretical and applied studies.

• Autonomous Multi-Agent Systems
• Probabilistic Reasoning
• Information Fusion
• +۳ مورد دیگر

Hsan Sandar Win is a part-time Ph.D. candidate and Researcher at the University of Adelaide's School of Electrical and Mechanical Engineering, within the Faculty of Sciences, Engineering and Technology. Her research focuses on uncertainty-aware frameworks for enhancing situational awareness in autonomous systems operating under tactical edge conditions. Specifically, she explores probabilistic reasoning, adaptive decision-making, and knowledge fusion to improve situational understanding and manage environmental uncertainty. Her current work as a Research Assistant involves semantic knowledge representation through ontology and probabilistic inference with incomplete data to support robotic environmental awareness. She aims to develop scalable methodologies for resilient autonomy and collaborative mission execution in complex operational domains.

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.

• Privacy-Preserving Machine Learning
• Reinforcement Learning
• Data Privacy and Security
• +۵ مورد دیگر

Associate Professor Kee Siong Ng is affiliated with the School of Computing at the Australian National University (ANU). His research focuses on privacy-preserving technologies, reinforcement learning, distributed systems, and blockchain applications. He has contributed extensively to areas such as privacy-preserving machine learning, federated learning, entity resolution, and scalable database systems. His work emphasizes balancing computational efficiency with privacy guarantees in data-driven environments. Key research contributions include methodologies for secure data processing in federated learning frameworks, privacy-preserving reinforcement learning for population-level systems, and blockchain-based digital identity solutions. He has led projects like Integrated Graph Analytics and contributed to initiatives involving the Australian Medicare dataset. His research often intersects theoretical foundations with practical implementations, addressing challenges in scalability and real-world applicability. Ng has published over 24 peer-reviewed articles, with notable works appearing in venues like IEEE Transactions on Parallel and Distributed Systems and Transactions on Machine Learning Research . His articles frequently explore cutting-edge topics such as differential privacy, approximation algorithms, and multi-agent systems. Collaborations include industry partnerships and interdisciplinary efforts involving health informatics and financial intelligence. His projects include Integrated Graph Analytics (2018–2021): Focused on scalable graph-based data analysis. Translational Fellowship (2018–2022): Bridging theoretical research with practical applications. Research on Data Sets for Health and Pharmaceutical Schemes (2020): Analyzing Medicare and pharmaceutical data with privacy safeguards. Ng's work prioritizes ethical AI and privacy-by-design principles, with a focus on real-world deployment challenges in distributed and federated systems.

Stuart Clarkمشاهده پروفایل

استاد

• Sedimentary basin dynamics
• Machine learning applications in geology
• Numerical simulations of Earth processes
• +۵ مورد دیگر

Professor Stuart Clark is a distinguished academic at the University of New South Wales (UNSW), currently serving as Professor in the Civil and Environmental Engineering department within the Faculty of Engineering. He was appointed Director of Governance for the Faculty of Engineering in 2024 and promoted to Professor in 2025. Previously, he was an Associate Professor (2021-2025) and Senior Lecturer (2017-2021) in the Minerals and Energy Resources School at UNSW. His educational background includes a PhD in Geophysics from the University of Sydney (2007), Master of Arts from the University of Melbourne (2004), and BSc.(Hons)/B. Arts from the University of Sydney (2002). He also earned a Graduate Certificate in University Learning and Teaching from UNSW in 2022. Professor Clark's research focuses on understanding the influence of deep Earth processes on sedimentary basin development and applying machine learning to geological modeling. His work spans quantitative sedimentary basin dynamics, numerical simulations of Earth processes like subduction and sediment transport, and innovative applications at the intersection of geology and machine learning. His research has significant implications for sustainable resource use and exploration. His recent publications reveal a strong emphasis on basin analysis, particularly in the Northern Carnarvon Basin, with applications spanning petroleum geology, resource distribution, and geological modeling using advanced statistical methods like Bayesian inference. His work increasingly bridges traditional geology with computational approaches, including machine learning applications for fracture detection, fault identification, and image analysis of geological structures. Arc Postgraduate Council's Supervisor Award (2021) UNSW Vice Chancellor's Teaching Excellence Award - Rising Star (2019) UNSW Engineering Hero Award (2020) Professor Clark has successfully secured multiple significant research grants, including the Geodynamical Assessments of Subsidence in the North West Shelf in Australia (2024-2025), the ARC Industry Transformation Research Hub for Resilient and Intelligent Infrastructure Systems (2021-2027), and several Australian Research Council Linkage projects. He currently supervises numerous PhD and research master's students working on diverse projects related to basin evolution, dynamic topography, fracture mechanics, and resource exploration. His teaching portfolio includes undergraduate and postgraduate courses in geology, sedimentary and energy resources, and seismic imaging.

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.

• 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.