جستجوی استاد راهنمای دانشگاهی

Associate Professor Feng Chen is a faculty member at the School of Mathematics & Statistics, University of New South Wales, specializing in statistical methodology development and applications. His research bridges theoretical statistics and practical implementations across financial modeling, spatiotemporal processes, and public health analysis. PhD in Statistics from University of Hong Kong (2008) MSc in Applied Probability & Statistics from Lanzhou University (2004) BSc in Mathematics from Lanzhou University (2001) Research focuses include: Nonparametric and semiparametric statistical methods Point process modeling with emphasis on Hawkes processes Statistical computing and algorithm development Applications to financial data, earthquake analysis, and public health Recent publications demonstrate methodological advances in: Hawkes process estimation with complex data structures Renewal process applications in seismology GARCH modeling with missing data Spatiotemporal clustering analysis Scientific recognition includes: UNSW Science Staff Impact Award (2023) Professional roles: Director of Research Postgraduate Studies (2023--) Associate Editor for multiple journals Statistics Honours Coordinator (2013-2018) Active participant in statistical societies

• Nonparametric and semiparametric statistics
• Point processes and self-exciting processes
• Statistical computing
• +۳ مورد دیگر

Paul Wu is an Associate Professor in the School of Mathematical Sciences at Queensland University of Technology (QUT), Faculty of Science, where he also serves as an industry research fellow in the strategic partnership between the Centre for Data Science (CDS) and AIS/QAS. He leads the sports systems domain within CDS and applies statistical and machine learning models to complex systems across sports, marine science, and defence sectors. PhD, Queensland University of Technology Master of Engineering Science (Computer and Comm Engineering), Queensland University of Technology Bachelor of Engineering (Electrical and Computer Engineering), Queensland University of Technology His research focuses on Bayesian statistics, dynamic Bayesian networks, state space modelling, and simulation techniques. He works closely with domain experts to solve real-world problems in sports performance, ecological resilience, and human systems. His interdisciplinary work spans sports science , marine ecology , and defence applications . Paul’s recent publications demonstrate a strong trajectory in predictive analytics for elite sports and ecosystem modelling, particularly using Bayesian frameworks. His work on swimming performance prediction has informed national training strategies and contributed to competitive success. He has also advanced methods in clustering, model adaptation, and psychosocial risk assessment. His scientific contributions have been recognized through impactful collaborations with elite sports organizations including the Australian Institute of Sport, Swimming Australia, and the West Coast Eagles. Testimonials highlight his role in transforming data analytics in sports injury recovery and performance optimization. Applied Bayesian models in elite sports decision-making Developed predictive tools for marine ecosystem resilience Collaborated on over 30 industry and government projects Supervises research in complex sports data analytics Paul leads a dynamic research environment focused on translating statistical innovation into practical impact across diverse domains.

• Statistics
• Sports science and exercise
• Ecological applications
• +۷ مورد دیگر

Dr. Tom James Stindl is a Lecturer and statistician at the School of Mathematics and Statistics, UNSW Sydney. His work centers on point process models, particularly renewal Hawkes processes, with applications across finance, seismology, crime analysis, and bushfire modeling. He earned his Ph.D. in statistical inference for self-exciting point processes under Dr. Feng Chen at UNSW Sydney. Supervises PhD, MRes, and Honours students (e.g., Jason Lambe, Zhe Han) Research focuses on computational statistics, Hawkes processes, and Bayesian/non-parametric methods Key grant: "Inference for Hawkes processes with challenging data" (Australian Research Council, 2024-2026) His recent publications analyze statistical inference techniques for point process models, though specific titles aren't listed here. Teaching includes courses like Statistical Inference and Statistical Modelling and Computing . Contact: t.stindl@unsw.edu.au

• Statistics
• Applied Statistics
• Econometric and Statistical Methods
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Dr. Jeffrey Kwan is a Lecturer in Statistics at the School of Mathematics and Statistics, University of New South Wales (UNSW Sydney). His research focuses on probability theory and stochastic processes, particularly Hawkes processes and their asymptotic behavior. He completed his PhD in 2023, specializing in ergodicity and applications for non-stationary and non-exponential Hawkes models. PhD (UNSW Sydney, 2023): Ergodicity of Hawkes processes Jeffrey's research spans probability theory, stochastic processes, financial data modeling, and data-driven legal analysis. He has published extensively on Hawkes processes, including parametric inference, ergodicity, and applications in terrorism modeling, crime analysis, and public policy. His scientific awards include the Excellence in Postgraduate Research (Statistical Society of Australia, NSW Branch, 2022), University Medal (UNSW Sydney, 2017), and multiple Dean's List recognitions. He has taught undergraduate and postgraduate courses in statistics, probability, and data science, including roles in the Business School and Faculty of Science. 2025: Chair, School of Mathematics and Statistics EDI Committee 2024: Secretary, Statistical Society of Australia (NSW Branch) 2023: Statistical Consultant at Stats Central (Mark Wainwright Analytical Centre)

• Probability Theory
• Stochastic Processes
• Statistical Inference
• +۳ مورد دیگر

Sanje Mahasivam is a Research Fellow at RMIT University's School of Science in Melbourne, Australia. His research focuses on advanced materials and nanotechnology with applications in environmental monitoring, food safety, and clinical diagnostics. Institution: RMIT University School: School of Science Campus: City Campus Australia Email: sanje.mahasivam@rmit.edu.au Dr. Mahasivam's research interests span nanotechnology, materials chemistry, and biosensor development, with particular expertise in nanozymes, plasmonic nanoparticles, and point-of-care diagnostic systems. His work bridges the gap between fundamental materials science and practical applications in healthcare and environmental monitoring. An analysis of his recent publications reveals a strong focus on developing nanoscale sensors for environmental, food, and clinical applications. His research demonstrates expertise in plasmonics, nanozyme catalysis, and materials engineering for sensor development. The publications cover diverse applications including CO 2 capture, kidney disease diagnostics, glucose monitoring, and environmental pollutant detection, highlighting the interdisciplinary nature of his work. Dr. Mahasivam has received recognition through numerous publications in high-impact journals across materials science, chemistry, and biomedical engineering fields. He actively supervises multiple research projects focused on nanosensor development, including several titled 'NanoZyme sensors for Color-based Environmental, Food, and Clinical Analytes' with various project dates from 2023-2025. His supervision portfolio indicates a strong commitment to training the next generation of researchers in nanotechnology and sensor development. His research laboratory appears to focus on developing practical nanoscale solutions for real-world diagnostic challenges, with particular emphasis on color-based detection systems that enable point-of-care applications without complex instrumentation.

• Nanotechnology
• Materials Chemistry
• Nanozyme Sensors
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Dr. Tom Stindl is a Lecturer at the School of Mathematics & Statistics , UNSW Sydney. His research focuses on statistical inference for self-exciting point processes, particularly the renewal Hawkes process and its marked/multivariate variants, with applications in finance, seismology, crime, and bushfire modeling. Core research areas: Hawkes Processes, Computational Statistics, and Spatiotemporal Modeling. Supervised PhD/MRes/Honours students including Jason Lambe, Zhe Han, and Toby Mufford. Developed R packages RHawkes and MRHawkes for statistical modeling. His recent publications (2017–2025) analyze Hawkes processes for earthquake declustering, financial risk forecasting, and Bayesian ETAS modeling. He received the Early Career Teaching Excellence award from UNSW's Faculty of Science for courses like MATH3821 and DATA3001 . A current Australian Research Council Discovery Project (2024–2026, $463,452) explores Hawkes processes with challenging data.

• Statistics
• Computational Statistics
• Self-Exciting Point Processes
• +۶ مورد دیگر