PhD Candidates

Qian researches the scheduling and routing of robotic sprayers in agriculture”. She is being funded and co-supervised by Data61 and co-supervised by OPTIMA CI Andreas Ernst and OPTIMA AI Simon Bowly.

Xinyu’s project titled “Human-Centric Approach to Sustainable Microgrid Management” aims to prioritize the role of human factors in the operation and efficiency of microgrids. By placing humans at the center of the operation and management process, this project aims to tailor microgrid operation and management strategy to better match user behavior, needs, and preferences, thus enhancing system efficiency and sustainability. He is supervised by OPTIMA AI Dr. Hao Wang, Dr. Buser Say, and Dr. Frits de Nijs.

Py is a PhD student at the University of Melbourne supervised by Dr Ellie Hajizadeh, Prof Michael Kirley and Dr Dominic Robe. Py is interested in combining various optimisation and machine-learning tools to tackle challenging problems in science and engineering. His current project focuses on applying the methods to inverse design multiscale polymeric materials with desired properties. His previous work modelled complex logistic system and developed hybrid algorithms to optimise its efficiency.

Nuwani’ s project title is “Optimal Predictor Selection for High-dimensional Nonparametric Forecasting”. The project’s primary goal is to discover more objective and principled way(s) of variable selection in a situation where there are many possible predictors to a forecasting model. Non-linear transfer function models with additivity constraints imposed, and Neural Network models are the forecasting models of interest. The studied models will be experimented with using half-hourly electricity demand data from the Australian Energy Market Operator and groundwater level data from the Australian Groundwater Explorer. OPTIMA CI Prof. Rob Hyndman supervises her at Monash University.

Lakshan has been a PhD student at Monash University since 2020 under the supervision of Dr Reza Razzaghi and OPTIMA CI Prof. Rob Hyndman. His project title is: “Data-Driven Exploration of Power System Strength”. His project concentrates on characterising and monitoring the system strength in modern power systems as inverter-based resources become more prominent. He is working on synthetic data produced by a power system simulator, and the next phase is to work with real sensor data. He aims to optimise the placement of a new technology of sensors (called non-fundamental Phasor Measurement Units) within a power grid to predict unstable dynamic phenomena that occur in weak grids.

Jinquiang’s project title is “Efficient Incrementality in Learning Solvers.” OPTIMA Deputy Director Prof. Peter Stuckey supervise him.

Henk’s project title is  “Solving discrete optimization problems using Satisfiability solvers”.  Discrete optimization problems occur in nature and society, and are at the heart of many industrial and operational applications. Monash University is world-leading in developing efficient solving technologies, which it makes accessible through the MiniZinc modeling language. The Satisfiability (SAT) solving technology  has been a top contender in the yearly MiniZinc Challenge. Inspired by this success, the development of a native MiniZinc interface for SAT solvers was initiated in early 2020, which has already explored new approaches. Finally, since there is no standard yet, this interface aims to bridge the disparate Satisfiability and general discrete optimization communities.” Henk is supervised by OPTIMA CI Assoc. Prof. Guido Tack.

Hanan’s project is titled “Dynamic Constrained Multi-Objective Optimization Using Evolutionary Algorithms”. Many real-world optimization problems have multiple objectives, constraints, and are dynamic by nature. In this research, we aim to develop an understanding of what makes evolutionary algorithms perform differently when solving dynamic constrained multi-objective optimization problems, and design landscape-aware algorithms. Hanan is supervised by by OPTIMA CI Professor Michael Kirley.

Drew’s project is titled “Long term energy grid planning with considered fluctuating renewable sources and energy storage”. He is supervised by OPTIMA CI Andreas Ernst, co-supervised by OPTIMA AI Ariel Liebman & Pierre Lebodic.

Daniel Bustos is a PhD candidate at the Faculty of Science, The University of Melbourne. His research focuses on the development of optimization approaches to vehicle routing problems arising in drayage operations.