Dale graduated from the University of Technology, Sydney (UTS) with a first class Honours in Pure Mathematics in 2006. After his honours, Dale spent some time working in the finance industry. In early 2012, he completed his Ph.D. in Pure Mathematics at the University of New South Wales on the topic of stochastic partial differential equations (SPDE).
I am happy to supervise students on topics in probability theory, stochastic processes, and their application to large-scale data science problems. I am also keen to co-supervise students who are interested in exploring problems that combine probability theory with analysis, algebra, or geometry.
Probability theory, stochastic processes, and their application to large-scale data science problems.
I currently teach the following two courses that I sprinkle with my own interests and examples:
(1) A research-led course that looks at the application of random matrix theory in high-dimensional statistics and machine learning applied to very large datasets. Focus is on understanding how classic approaches break down when the dimensionality of the data becomes large and how these problems may be resolved using recent mathematical results [Big Data Statistics].
(2) A course on stochastic processes: Markov chains, Brownian motion, their properties and applications [Intro to Stochastic Processes].
Room 3.48, College of Business & Economics Building (26C)