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Tom Stindl is a lecturer at the School of Mathematics and Statistics at UNSW Sydney, specializing in statistics and computing. His recent research interests revolve around Hawkes Processes, Statistical Inference, and Computational Statistics. Stindl's doctoral research focused on statistical inference of self-exciting point processes, supervised by Dr. Feng Chen at UNSW. His core academic pursuits involve developing efficient statistical inference methods for point process models, with specific emphasis on renewal and multivariate variants of Hawkes processes. He has worked on projects modeling financial data, seismology, crime, and bushfire risks using point processes. His teaching includes various statistical courses such as Statistical Modelling Computing, Statistical Inference for Data Scientists, and Statistical Analysis of Dependent Data.
Includes Business Intelligence, Enterprise Systems, and Cybersecurity Management streams.