Dirk Kroese is a professor of Mathematics and Statistics at The University of Queensland, Brisbane, Australia. He is the co-author of several influential monographs on simulation and Monte Carlo methods, including Handbook of Monte Carlo Methods and Simulation and the Monte Carlo Method, (3rd edition). Dirk is a pioneer of the well-known Cross-Entropy method – an adaptive Monte Carlo technique which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance.
Rare events such as the state-wide power blackout in South Australia in September 2016, natural disasters such as floods and bushfires, or the ensuing chaos when parts of a complex interconnected systems such as the internet fail, are difficult for researchers to simulate or model. They're the primary interest of ACEMS Chief Investigator Dirk Kroese.
Splitting methods involve systems of particles that move around in space and time and have the possibility to die out or split into multiple particles at certain times. Such systems can be used to solve complicated estimation and optimization problems. This project aims to (1) create new and faster splitting algorithms; (2) improve the implementation of splitting algorithms, e.g., via distributed computing; (3) find new applications for optimization and estimation that are well-suited to be solved by splitting methods.
The project will deliver new theory and methods for fast and robust statistical learning, inference, and parameter estimation in various application fields such as risk management, decision making, health-care, manufacturing, financial engineering, and system reliability. By providing high-quality solutions that are out of reach of the current state of the art, the project will have large-scale impact on operational efficiency of real-life applications in both scientific and industrial domains.
Moka, S. Babu, Kroese D., & Juneja S.
(2018). Perfect Sampling for Gibbs Point Processes Using Partial Rejection Sampling (extended results). AustMS 2018 62nd Annual Meeting of the Australian Mathematical Society.
Moka, S. Babu, & Kroese D.
(2018). Perfect Sampling for Gibbs Point Processes Using Partial Rejection Sampling. Workshop on Advances and challenges in Monte Carlo Methods.
Spettl, A., Brereton T., Duan Q., Werz T., Krill C. E., Kroese D., et al.
(2016). Fitting Laguerre tessellation approximations to tomographic image data. Philosophical Magazine. 96(2), 166-189. doi: 10.1080/14786435.2015.1125540
Westhoff, D., van Franeker J. J., Brereton T., Kroese D., Janssen R. A. J., & Schmidt V.
(2015). Stochastic modeling and predictive simulations for the microstructure of organic semiconductor films processed with different spin coating velocities. Modelling and Simulation in Materials Science and Engineering. 23(4), 45003. doi: 10.1088/0965-0393/23/4/045003