Current position

  • Professor of Machine Learning and Data Science, Faculty of Information Technology, Monash University, Australia.
  • Research Director, Head of AI and Machine Learning Laboratory, Trusting Social, Melbourne, Australia (aka Credit AI) since 2017.

Research interest

  • Machine Learning, Generative Deep Learning, AI Systems, Bayesian Nonparametrics and Statistical Deep Networks
  • Probabilistic Graphical Models, Online learning
  • Early Intervention in Autism, Pervasive Healthcare and Health Analytics
  • Social Media, Ubiquitous Computing, Computer vision and Multimedia

Awards and Fellowships

  • National Competitive Grant (~ 6 millions) 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2009, 2008, 2007
  • Best Paper Award, Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2015 (with S. Gupta, S. Rana and S. Venkatesh)
  • Victorian International Education Awards, 2013 (member of TOBY India team) (see Deakin news).
  • Researcher of the Year Award (member of TOBY team), Smart Geelong Network, 2012.
  • Curtin Innovation Award (with S. Venkatesh and S. Greenhill), Curtin University, 2011 (Click here for more information).
  • Early Career Research and Development Award, Curtin University, 2010.
  • Curtin Research Targeted Fellowship, (4 years), Curtin University, 2006-2010.
  • Runner-up Best Paper Award (with T. Tran and S. Venkatesh), International Conference on Uncertainty in Artificial Intelligence (UAI), Canada, 2009.
  • International Research Fellowship, AI Center, SRI International, Menlo Park, 2006.

Qualifications

  • Doctor of Philosophy (Computer Science, 2005)
    Thesis: Probabilistic and Film Grammar Based Methods for Video Content Understanding
    Curtin University of Technology, Australia.
  • Bachelor of Computer Science (First Class Honours, 2001)
    Thesis: An Investigation into Audio for Content Annotation
    Curtin University of Technology, Australia.

Some Recent Services

Recent News

  • Our paper Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data has been accepted to KDD 2018
  • Our paper MGAN: Training Generative Adversarial Nets with Multiple Generators has been accepted to ICLR 2018
  • Our paper Learning Graph Representation via Frequent Subgraphs has been accepted to SDM 2018
  • Our paper GEN: Geometric Enclosing Networks has been accepted to IJCAI 2018
  • Our paper Dual Discriminator Generative Adversarial Nets has been accepted to NIPS 2017
  • Our paper Supervised Restricted Boltzmann Machines has been accepted to UAI 2017
  • Our paper Approximation Vector Machines for Large-Scale Online Learning has been accepted to the Journal of Machine Learning Research (JMLR 2017)
  • Our paper Multilevel Clustering via Wasserstein Means has been accepted to ICML 2017
  • Our PhD student, Hung Vu, won the Best Application Paper Award (Energy-Based Localized Anomaly Detection in Video Surveillance, PAKDD 2017)
  • Our paper Discriminative Bayesian Nonparametric Clustering has been accepted to IJCAI 2017
  • Our paper Large-scale Online Kernel Learning with Random Feature Reparameterization has been accepted to IJCAI 2017
  • Our paper GoGP: Fast Online Regression with Gaussian Processes has been accepted to ICDM 2017
  • Our paper Prediction of Population Health Indices from Social Media using Kernel-based Textual and Temporal Features has been accepted to WWW 2017
  • Our paper Hierarchical semi-Markov conditional random fields for deep recursive sequential data has been accepted to AIJ 2017 (Artificial Intelligence journal).
    • See my thesis (chapter 5) for an equivalent directed graphical model, which is the precusor of this work and where I had described the Assymetric Inside-Outside (AIO) algorithm in great detail. A brief version of this for directed case has also appeared in this AAAI'04's paper. The idea of semi-Markov duration modelling has also been addressed for directed case in these CVPR05 and AIJ09 papers.
  • Our paper Column Networks for Collective Classification has been accepted to AAAI 2017.
  • Our paper Dual Space Gradient Descent for Online Learning has been accepted to NIPS 2016.
  • Our paper Scalable Baysian Nonparametric Multilevel Clustering has been accepted to UAI 2016.
  • Our paper Budgeted Semi-supervised Support Vecitor Machine has been accepted to UAI 2016.
  • Our paper Nonparametric Budgeted Stochastic Gradient Descent has been accepted to AISTATS 2016.
  • Our paper One-pass Logistic Regression for Label-drift and Large-scale Classification on Distributed Systems has been accepted to ICDM 2016.
  • The Special Issue on Machine Learning journal for selected papers from ACML 2014 is now available here.
  • I was awarded the Australian Research Council (ARC) Discovery Grant entitled Bayesian Nonparametric Machine Learning for Modern Data Analytics to commence from 2016 (over 3 years, $410k).
  • Our paper Tensor-variate Restricted Boltzmann Machines has been accepted to AAAI 2015.
  • Our paper Collaborating Differently on Different Topics: A Multi-Relational Approach to Multi-Task Learning won the Best Paper Award at PAKDD 2015.
  • Our student's paper Stabilizing Sparse Cox Model using Statistic and Semantic Structures in Electronic Medical Records won the Best Student Paper Award Runner-Up at PAKDD 2015.
  • Our paper Streaming Variational Inference for Dirichlet Processes has been accepted to ACML 2015.
  • Our paper Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts has been accepted to ICML 2014.
  • I was awarded the Australian Research Council (ARC) Discovery Grant entitled Stay Well: Analyzing Lifestyle Data from Smart Monitoring Devices to commence from 2015 (over 3 years, $384k). This research proposal aims to develop theoretical machine learning foundations for analyzing data and signals collected from medical wearable devices.
  • I will be Program Chairs (with Hang Li) for the Asian Conference on Machine Learning (ACML), 2014.