Our People

Meet our members

Here are the experts who are innovating to expand our understanding of machine learning.

Senior Staff

Prof. Wray BuntineProf. Wray Buntine

Professor

Wray is known for his theoretical and applied work, probabilistic methods for document and text analysis, social networks, data mining and machine learning. He has previously worked at National ICT Australia , the Helsinki Institute for IT, NASA Ames Research Center, Uni of California, Berkeley, Google and startups in Wall Street and Silicon Valley. He sits on editorial boards and is a senior programme member for premier conferences such as IJCAI, UAI, ACML and SIGKDD.

Bayesian and Statistical Machine Learning, Relational and Structured Learning, and Natural Language Processing
Prof. Dinh PhungProf. Dinh Phung

Professor

Dinh Phung is a Professor of Machine Learning and Data Science at Monash Clayton. He is a leading researcher at the forefront of theoretical and applied machine learning, particularly deep generative models, Bayesian learning and graphical models, optimal transport and point process theory for machine learning. He publishes regularly in the areas of machine learning, AI and data science.

Deep Learning, Bayesian and Statistical Machine Learning, Relational and Structured Learning, and Natural Language Processing
Prof. Geoff WebbProf. Geoff Webb

Professor (Research)

Geoff is a leading machine learning researcher, focusing on pattern discovery, classification, learning from big data, and learning in the context of a changing world. He is a member of the ACM SIGKDD Executive and an advisor to data science start-ups BigML and FROOMLE. His many awards include IEEE Fellow and the inaugural Eureka Prize for Excellence in Data Science.

Non-Stationary Distributions, Time Series Analytics, and Bayesian and Statistical Machine Learning

All Staff

Associate Professor Vincent Lee

A/Prof. Vincent Lee

Assoc Professor

Vincent Lee is known for his theoretical and applied work spanning multi-disciplinary research field. He specialises in the design and development of adaptive signal and information processing systems using deep machine learning, evolutionary computation, smart FinTech, and explainable artificial intelligence techniques. He has extensive professional practice and managerial experience for MNCs in Australia, China, Singapore, and United Kingdom.

 
Associate Professor David Dowe

A/Prof. David Dowe

Assoc Professor

David Dowe's research includes Bayesian, information-theoretic, minimum message length (MML) and other approaches to statistics, machine learning, data science and deep learning. His interests include foundational theoretical issues and applications to areas including (e.g.) (astro)physics, ecology, energy, health, medicine, biology, psychology, psychiatry, etc. His interests also include contrasting human, non-human animal and electronic intelligence, and hybrids thereof.  He endorses artificial intelligence (AI) for the social good but is concerned about AI going awry and threats to humanity's survival.

Minimum message length (MML), Bayesianism, (algorithmic) information theory, applications, societal impact, technological singularity

Dr Daniel Schmidt

Dr Daniel Schmidt

Senior Lecturer

Daniel's primary area of research is Bayesian and information theoretic statistics, with an interest in sparse parameter estimation, model selection and the minimum message length principle of inductive inference. His other research interests include time series modelling and forecasting, neural networks, and statistical genomics for disease prediction.

Bayesian and Statistical Machine Learning

Dr Lan Du

Dr Lan Du

Senior Lecturer

Lan is a lecturer in data science. His research lies at the joint area of statistical machine learning, text analysis, relational learning and social network analysis. He is particularly interested in deep Bayesian models for discrete data analysis, such as Gamma belief nets and deep Poisson factor analysis. He is highly sought after to join program committees for high level conferences such as NIPS, ICML, AISTATS, AAAI, ACL.

Natural Language Processing, Bayesian and Statistical Machine Learning, Relational and Structured Learning

Dr Levin Kuhlmann

Senior Lecturer

Levin is a senior lecturer in data science and digital health. He is a data scientist, computational neuroscientist and neural engineer. His research areas include signal processing, control theory, machine learning, statistics and computational neuroscience applications to digital health, neural engineering, neuroimaging and medicine.

Deep Learning, Bayesian and Statistical Machine Learning, Time Series Analytics, Digital Health, Computational Neuroscience and Neural Engineering

Dr Christoph Bergmeir

Dr Christoph Bergmeir

Senior Research Fellow

Christoph's research interests are time series predictions using Machine Learning methods, recurrent neural networks and long short-term memory neural networks (LSTM), time series predictor evaluation as well as medical applications and software packages in the R programming language. He collaborates with external partners in diverse sectors such as healthcare and infrastructure maintenance. He is the recipient of an ARC DECRA fellowship.

Time Series Analytics, Deep Learning
 

Dr Jackie Rong

Lecturer

Jackie is a lecturer in data science. Her research lies at the joint area of pattern recognition, text analysis, big data analysis and social network analysis. She is particularly interested in supervised deep learning models for online review analysis, product and service recommendations. She also works in the area of big data validation and processing.

Text analysis, Pattern recognition and Social Network analysis

Dr Mahsa Salehi

Dr Mahsa Salehi

Lecturer

Mahsa is a Lecturer at Monash Clayton campus. Her primary research interests include anomaly detection, multi-dimensional time series analysis, data stream mining and subspace clustering. Secondary research interests include graph mining and deep learning.

Time Series Analytics and Non-Stationary Distributions

Dr Mario Boley

Lecturer

Mario is a data science researcher interested in all aspects of interpretable data analysis methods: learning-theoretic foundations, efficient algorithms, and concrete applications. For the latter, he is particularly focussed on supporting scientific discoveries. For example, he collaborates with materials science researchers to pursue the data-driven discovery of novel functional materials.

Data Analytics
Machine Learning
Human-centred AI

Weiqing (Teresa) Wang

Dr Teresa Wang

Lecturer

Teresa’s major research interests are deep graph learning and recommender systems. More specifically, in graph embedding and user modelling. From the data level, she is interested in learning from discrete and sparse data vectors, spatio-temporal data analysis, multi-media and multi-view data analysis and integration.

Relational and Structured Learning and Time Series Analytics
 

Dr Trung Le

Lecturer

Trung’s research consists of both theoretical and practical aspects. Particularly, his research revolves optimisation in machine learning, deep generative networks, deep domain adaptation or transfer learning, bayes inference, and adversarial learning whose gained theories have been applied to application domains including online learning, abnormality analytics, cyber security, and software vulnerability detection.

Deep Generative models,
Optimisation for ML, Kernel methods, Deep learning and Anomaly  Detection
Dr. Chang Wei Tan 

Dr Chang Wei Tan

Research  Fellow

Chang Wei is a Research Fellow in Machine Learning. His main research interests include time series analysis and scaling up time series classification algorithms to large time series data sets. He is also interested in deep learning, applying machine learning to engineering and digital health applications.

Time series Analytics, Deep learning
Dr. Ethan Zhao 

Dr Ethan Zhao

Research  Fellow

Ethan's research lies between machine learning and statistics. He is particularly interested in Bayesian modelling and inference problems for large-scale complex data, such as those in languages, graphs, collaborative filtering, and images. Currently, he is working on leveraging deep neural networks to boost capacity, accuracy, efficiency, scalability, and robustness for probabilistic modelling and posterior inference. The goal of his research is to automate: representation learning, uncertainty analysis, and understanding of dynamics and mechanisms, in complex real-world data.

Bayesian Statistics,
Deep Generative Model
Dr. Mahdi Abolghasemi 

Dr Mahdi Abolghasemi

Research  Fellow

Mahdi is interested in communicating science and applying his knowledge to real-world problems.  His research interests include forecasting, decision making, business analytics, and his research tools are data science and machine learning models, statistical and time series models, Bayesian Network, and optimization techniques.

Forecasting, Supply Chain Management, Applied Data Science, Judgement and Decision making

Dr Viet Huynh

Dr Viet Huynh

Research Fellow

Viet’s current research interests include developing large-scale learning algorithms for probabilistic graphical models with complex and large-scale data, and applying optimal transport theory to understand challenging problems in machine learning and deep learning.

Deep Learning, and Bayesian and Statistical Machine Learning

Tu Nguyen

Dr Tu Nguyen

Research Fellow

Tu will join Monash from Jan 2019 as a Research Fellow in Machine Learning.

Tu’s research covers the domains of deep learning including supervised (CNN, RNN) and unsupervised, generative (RBM, DBM, VAE, GAN) models, kernel methods for online learning and high-performance algorithms for distributed computing.

Deep Learning and Relational and Structured Learning

Dr David Jin

Dr David Jin

Research Fellow

Yuan is a Research fellow in machine learning at Monash. He has done research in areas including crowdsourcing, recommendation systems, and text analysis. His current research focuses on developing state-of-the-art methods in graphical learning, deep self-supervised and generative learning for text analysis.

Crowdsourcing, Text Analysis, Deep Learning
Dr Matthieu Herrmann

Dr Matthieu Herrmann

Research Fellow

Matthieu is a Research Fellow in Machine Learning.
His primary interest is scaling up Time Series Classification algorithms to long series and large datasets.  Most of his recent work focuses on efficient elastic distances and their application to classification. He also works as a software developer, implementing the Time Series library Tempo.

Time Series Analytics, Elastic Distances
Quang Bui

Dr Quang Bui

Research Fellow

Quang is a data scientist with a focus on statistics and time series modelling. He applies data science and machine learning to build and deploy real-world forecasting solutions for industry partners. He has collaborated with businesses in the energy and e-commerce sector.

Time Series Analytics, Statistics, Data Visualisation
Dr Van Nguyen

Dr Van Nguyen

Research Fellow

Van is a Research Fellow at the Department of Data Science and AI, Monash University. My research interests are CyberSecurity, Domain Adaptation, Computer Vision, Deep Learning, and Machine Learning. My current research is about "Deep Learning for Software Security".

CyberSecurity, Software vulnerability detection, Fine-grained vulnerability detection, Function identification, and Deep Learning.

Former members

Dr David Albrecht

Dr David Albrecht

Senior Lecturer

David is the Associate, Dean Learning and Teaching. His current research projects include detecting leaks in water networks, modelling transcriptomics data, identification of ballistic fragments and trajectories in corpses, and detecting falls and inactivity in home monitoring systems.

Bayesian and Statistical Machine Learning, and Relational and Structured Learning

Dr François Petitjean

Dr François Petitjean

Senior Research Fellow

François is a Senior Research Fellow in Machine Learning and the recipient of an ARC DECRA fellowship. His main research interests currently include scaling up machine learning to large time series datasets, creating land-cover maps from series of high-resolution satellite images and modelling high-dimensional joint and conditional probabilities from categorical data.

Non-Stationary Distributions, Time Series Analytics, and Bayesian and Statistical Machine Learning

Dr. Michael Kamp

Dr Michael Kamp

Research  Fellow

Michael is a machine learning researcher interested in theoretically sound and practically useful learning approaches. He works on efficient parallelizations for learning algorithms, kernel methods, convex optimization, and deep learning, as well as on their theoretical foundations. He has applied these techniques to autonomous driving, financial time series, and anomaly detection for cybersecurity.

Distributed Optimisation, Parallel Computing, Statistical Learning Theory, Deep Learning for Cyber Security, Co-regularization, Convex Optimization and Online Learning