We have opportunities available for PhD research in the areas of Data Science, Data Mining, Machine Learning and Deep Neural Networks, among others. Our students are supported by a range of scholarships and top-ups and receive travel support during their study.
For more general information, read about the Graduate Research Program and Scholarships.
Our PhD community is socially and professionally active, regularly coming together for lunch-time meetings as well as focussed reading groups and an annual retreat.
Current featured PhD students
Angus works on scalability in time series classification, developing methods which can scale to millions or even billions of time series, supervised by Professor Geoff Webb and Dr Daniel Schmidt. His work has been published in Data Mining and Knowledge Discovery.
Caitlin's research translates state-of-the-art machine learning (ML) techniques into suitable methods for use in real-world studies. Supervised by Prof Wray Buntine and A/Prof Henry Linger, her applied work addresses the need for new ways to mine public opinion via social media analysis. Caitlin's hybrid methodologies integrate topic modelling and qualitative analysis. They have been used for the study of low-carbon energy, the US General election, EU data privacy policies, and measures to combat COVID-19. Additionally, Caitlin's research adopts a critical perspective to identify the contextual elements needed to integrating ML into contacting arrangements for defence platform sustainment. DSTG funds this work.
Duong Nhu's project focuses on automated epilepsy monitoring of short and long term EEG recordings, and wearable devices under the GRIP scholarship. Duong Nhu's supervisors are Dr. Levin Kuhlmann, Professor Patrick Kwan, Dr. Chang Wei Tan, and Dr. Amanda Gilligan. Duong has been awarded the MIME research funding, and the most outstanding poster presentation at the 1st Monash Young MedTech Innovators Symposium.
Dai’s research interest is in applying deep learning to Natural Language Processing and Information Extraction. Under supervision of Prof. Dinh Phung, Dai is working on search personalisation, graph embeddings and representation learning in general.
Dilini Sewwandi Rajapaksha
Dilini's Ph.D. research is on finding the new approaches of providing local explanations of the predictions generated by the black-box machine learning models. Her Ph.D research is supervised by Dr.Christoph Bergmeir and Prof. Wray Buntine. Recently she has developed a novel algorithm called LoRMIkA which provides local rule-based model agnostic explanations. Her research interests include model interpretability, data mining, machine learning, and time series forecasting.
Lynn's PhD research investigates how satellite earth observation images and machine learning techniques can be used to model environmental variables and monitor sustainable development indicators. Her research is supervised by Prof. Geoff Webb and Assoc. Prof. Chris Rudiger from Civil Engineering.
Marzie works on modelling and predicting key properties of next generation Magnesium alloys under the joint supervision of Prof Nick Birbilis (ANU), A/Prof Philip Nakashima (Monash, Faculty of Engineering), and Dr Mario Boley (Monash, FIT, Machine Learning). She is interested in statistical modelling techniques as well as deep learning methods.
Maurice Ntahobari's project focuses on applying machine learning techniques for epileptic seizure prediction. He is mainly looking at the best features in both short and long-time scale using long term human iEEG recordings. He aims in developing an efficient, low computational model that is applicable on implantable/wearable devices. His works are under supervision of Dr. Levin Kuhlmann, Dr Mario Boley and Dr Zhinoos Razavi Hesabi. Maurice has a Masters in Network Centric Computing and bachelor in computer science and systems.
Wth modern anaesthetic practices of TIVA (Total Intravenous Anaesthesia) and complicated regional NMB (Neuromuscular Blockade), accurate monitoring of a patient’s DoA (Depth of Anaesthesia) has gained importance. Revanth's research focuses on an EEG-based monitor that aids in the optimization of anaesthetic agent delivery with reduced incidence of intraoperative awareness using electrophysiologically-inspired algorithms, and reduction of artifacts in processed EEG to improve prediction accuracy. His research interests include machine learning and system design.
Cerebral Palsy typically affects motor control areas of a child's brain but is also associated with cognitive, behavioural and communication disorders. An absence of fidgety movement is highly correlated with Cerebral Palsy. William's project is developing an AI based methodology to examine videos of children, 12 to 14 weeks post birth, and classify them as containing either Normal or Not Normal Fidgety Movements.
Yun Zhao studies computational neuroscience, brain imaging and machine learning under the supervision of Dr Levin Kuhlmann and Dr Mario Boley. He aims to find an efficient mathematical approach to estimate human brain parameters based on clinical data and develop an automatic and universal toolbox for the medical community. Beyond that, he will apply the update rule of neural models to RNN, LSTM studies and try to boost the network training performance.
Former PhD students
Bhagya works on hospital readmission risk prediction for patients with chronic disease conditions under the supervision of Prof. Wray Buntine and Dr. Yuan-Fang Li. Her research interests include data mining, machine learning, medical risk prediction and representation learning.
Chaitanya studies prediction-making in environments that are in constant flux. In data-mining terms, he studies classification under concept drift. He is open to collaborations that explore real life applications of making predictions in complex changing environments such as financial markets, cryptocurrency mining, election analysis or sports odds. View his latest work.
Kasun’s research area focuses around leveraging recurrent neural networks on large scales of related time series in particular Machine learning for time series forecasting. His PhD research is supervised by Prof. Wray Buntine and Dr. Christoph Bergmeir.
He Zhao is a final-year Ph.D. student supervised by Prof. Wray Buntine and Dr. Lan Du. His research lies at the intersection of Bayesian statistics, machine learning, and data mining with applications in text analysis, social network analysis, and collaborative filtering. His research has been published in ICML, AISTATS, and ICDM. He reviews for NIPS, UAI, AAAI, and ACML.
Chang currently works on various Data Mining and Machine Learning projects. His thesis involves Scalable Time Series Data Mining with further research interest in Railway Maintenance and Wireless Sensor Networks.
Under supervision of Dr. Reza Haffari and Prof. Wray Buntine Poorya’s research interests lie at the intersection of deep learning and natural language processing. He has been awarded CSIRO’s Data61 top-up, MGE and MIPRS scholarships for his PhD course.
As a PhD student at the Faculty of Information Technology, Sameen’s research is primarily focused on using context to improve upon the current sentence-based Neural Machine Translation models.