1. Luke Yerbury, A New Algorithm for Fitting ARMA Models to Big Time Series Data, Honours, 2020.
2. Thomas McCarthy McCann, Rollage: Ecient Rolling Average Algorithm to Estimate ARMA Models for Big Time Series Data, Honours, 2020.
3. Riley Cooper, A New State Aggregation Algorithm to Solve Large Markov Decision Processes, Honours, 2019.
4. Benjamin Moran, Exploration of Flu-tracking Approaches Using Time Series Models, 2017-2018.
5. Thomas McCallum, Optimal Observation Times, Fisher Information and Generating Functions, Honours, 2016-2017.
1. Luke Yerbury, Detecting Anomalies in Big Time Series Data, PhD, 2021-2024.
2. Scott Howard, Solution Algorithms for Large Markov Decision Processes, Honours, 2021-2022.
3. Oliver Di Pietro, Toeplitz Least Squares Problems, Fast Algorithms and Big Data, Honours, 2021.
4. Elizabeth Harris, Efficient Algorithms to Detect Outliers in Big Data, PhD, 2020-2023.
5. Vektor Dewanto, Policy Optimization in Reinforcement Learning, PhD.
6. George Dunn, Reinforcement Learning with a Weighted-Sum Reward Function, MPhil.
If you are interested in any of the following topics, please contact me via email to discuss them with you in a meeting:
1. Approximate solutions to large Markov Decision Processes
2. Reinforcement Learning
3. Modeling and analyzing big data time series data