|Prof. Wray Buntine|
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 Phung|
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 Webb|
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|
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.
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|
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|
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
|Deep Learning, Bayesian and Statistical Machine Learning, Time Series Analytics, Digital Health, Computational Neuroscience and Neural Engineering|
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
|Text analysis, Pattern recognition and Social Network analysis|
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|
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.
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|
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
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|
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
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
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|
Dr Tu Nguyen
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
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|
Matthieu is a Research Fellow in Machine Learning.
|Time Series Analytics, Elastic Distances|
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|
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.|
Dr David Albrecht
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
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
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|