My research interests are in 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 (RMT) to tackle high-dimensional data science problems. Focus is on understanding how classic statistical approaches break down when the dimensionality of the data becomes large and how these problems may be resolved using recent mathematical results [STAT3017 / STAT7017: Big Data Statistics].

(2) A postgraduate course on stochastic processes: Random walks, Martingales, Markov chains, Brownian motion, their properties and applications [STAT7004: Intro to Stochastic Processes]. Applications to coin flipping, card shuffling, card counting, and other classic problems.

Dale graduated from the University of Technology, Sydney (UTS) with a first class Honours in Mathematics in 2006. After his honours, Dale spent some time working in the finance industry. In 2012, he completed his Ph.D. in Mathematics at the University of New South Wales on the topic of Stochastic Partial Differential Equations (SPDE).

Dale has published extensively since 2012 with more than 20 of his papers published in top-tiered journals (A* and A ranked). He has also secured more than $3.1M in research funding through numerous competitive grants and external funded projects mostly as sole chief investigator.

• 2022 ANU Vice-Chancellor's Award for Teaching Excellence
• 2021 College Award for Teaching Excellence
• 2020 College Commendation for Outstanding Teaching