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Prof Dinh Phung

STAFF PROFILE

Position

Professor of Computer Science

Faculty

Faculty of Sci Eng & Built Env

Department

Patt Recogntn & Data Analytics

Campus

Geelong Waurn Ponds Campus

Contact

dinh.phung@deakin.edu.au
+61 3 522 72082

Biography

Dinh Phung is a Professor of Computer Science and Deputy Director of the Centre for Pattern Recognition and Data Analytics, Deakin University. He is a leading researcher at the forefront of theoretical and applied machine learning research with a view to make lasting contributions to real-world problems. His research has attracted regular National Competitive Grant from the ARC (Australian Research Council) in the past 10 years.

Machine learning is the science behind big data, data mining, data science, deep learning and artificial intelligence. Over the last decade machine learning has grown to become a fundamental technology driving the Web 2.0 innovations in many areas of life and society. Machine Learning enables self-driving cars, Siri personal assistant in the iPhone, Nexflix movie recommendation engine, to name a few. Within the last five years, Phung has been the Chief Investigator (CI) in two Linkage Grants, two LIEF grants, and two Discovery Grants. He is currently the Lead CI for two ARC DP Grants whose goal is to advance the frontier of theoretical and applied machine learning research.

 

Read more on Dinh's profile

Knowledge areas

artificial intelligence, machine learning, deep learning, data science and analytics, graphical models, Bayesian statistics, ubiquitous computing, health analytics, autism analytics, social media analysis, multimedia and computer vision.

Publications

Filter by

2017

Using linguistic and topic analysis to classify sub-groups of online depression communities

T Nguyen, B O Dea, M Larsen, D Phung, S Venkatesh, H Christensen

(2017), Vol. 76, pp. 10653-10676, Multimedia tools and applications, Amsterdam, The Netherlands, C1-1

journal

Effective sparse imputation of patient conditions in electronic medical records for emergency risk predictions

B Saha, S Gupta, Q Phung, S Venkatesh

(2017), Vol. 53, pp. 179-206, Knowledge and information systems, London, Eng., C1

journal

A framework for mixed-type multi-outcome prediction with applications in healthcare

B Saha, S Gupta, D Phung, S Venkatesh

(2017), Vol. 21, pp. 1182-1191, IEEE journal of biomedical and health informatics, Piscataway, N.J., C1

journal

Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic data

P Vellanki, T Duong, S Gupta, S Venkatesh, D Phung

(2017), Vol. 51, pp. 127-157, Knowledge and information systems, London, Eng., C1

journal

A simultaneous extraction of context and community from pervasive signals using nested dirichlet process

T Nguyen, V Nguyen, F Salim, D Le, D Phung

(2017), Vol. 38, pp. 396-417, Pervasive and mobile computing, Amsterdam, The Netherlands, C1

journal

One-pass logistic regression for label-drift and large-scale classification on distributed systems

V Nguyen, T Nguyen, T Le, S Venkatesh, D Phung

(2017), pp. 1113-1118, Proceedings - IEEE International Conference on Data Mining, ICDM, E1

conference

Hierarchical semi-Markov conditional random fields for deep recursive sequential data

T Tran, D Phung, H Bui, S Venkatesh

(2017), Vol. 246, pp. 53-85, Artificial intelligence, Amsterdam, The Netherlands, C1

journal

Predicting healthcare trajectories from medical records: a deep learning approach.

T Pham, T Tran, D Phung, S Venkatesh

(2017), Vol. 69, pp. 218-229, Journal of biomedical informatics, Amsterdam, The Netherlands, C1

journal

Estimation of the prevalence of adverse drug reactions from social media

T Nguyen, M Larsen, B O'Dea, D Phung, S Venkatesh, H Christensen

(2017), Vol. 102, pp. 130-137, International journal of medical informatics, Amsterdam, The Netherlands, C1

journal

Column networks for collective classification

T Pham, T Tran, Q Phung, S Venkatesh

(2017), pp. 2485-2491, AAAI-17: Proceedings of the 31st Artificial Intelligence AAAI Conference, San Francisco, California, E1

conference

Energy-based localized anomaly detection in video surveillance

H Vu, T Nguyen, A Travers, S Venkatesh, D Phung

(2017), Vol. 10234, pp. 641-653, PAKDD 2017 : Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Jeju, South Korea, E1

conference

Model-based classification and novelty detection for point pattern data

B Vo, N Tran, D Phung, B Vo

(2017), pp. 2622-2627, 2016 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, E1

conference

Streaming clustering with Bayesian nonparametric models

V Huynh, D Phung

(2017), Vol. 258, pp. 52-62, Neurocomputing, Amsterdam, The Netherlands, C1

journal

Kernel-based features for predicting population health indices from geocoded social media data

T Nguyen, M Larsen, B O'Dea, D Nguyen, J Yearwood, D Phung, S Venkatesh, H Christensen

(2017), Vol. 102, pp. 22-31, Decision Support Systems, Amsterdam, The Netherlands, C1

journal

Discovering topic structures of a temporally evolving document corpus

A Beykikhoshk, O Arandjelovic, Q Phung, S Venkatesh

(2017), pp. 1-34, Knowledge and Information Systems, London, Eng., C1

journal

A framework for mixed-type multioutcome prediction with applications in healthcare

B Saha, S Gupta, D Phung, S Venkatesh

(2017), Vol. 21, pp. 1182-1191, IEEE journal of biomedical and health informatics, Piscataway, N.J., C1

journal

Academia versus social media: a psycho-linguistic analysis

T Nguyen, S Venkatesh, D Phung

(2017), pp. 1-10, Journal of computational science, Amsterdam, The Netherlands, C1

journal

Estimating support scores of autism communities in large-scale web information systems

N Thin, N Hung, S Venkatesh, D Phung

(2017), Vol. 10569 LNCS, pp. 347-355, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), E1

conference

Large-scale online kernel learning with random feature reparameterization

T Nguyen, T Le, H Bui, D Phung

(2017), pp. 2543-2549, IJCAI International Joint Conference on Artificial Intelligence, E1

conference
2016

Multiple task transfer learning with small sample sizes

B Saha, S Gupta, Q Phung, S Venkatesh

(2016), Vol. 46, pp. 315-342, Knowledge and information systems, Berlin, Germany, C1

journal

Data clustering using side information dependent Chinese restaurant processes

C Li, S Rana, Q Phung, S Venkatesh

(2016), Vol. 47, pp. 463-488, Knowledge and information systems, Berlin, Germany, C1

journal

Modelling human preferences for ranking and collaborative filtering: a probabilistic ordered partition approach

T Tran, Q Phung, S Venkatesh

(2016), Vol. 47, pp. 157-188, Knowledge and information systems, New York, N.Y., C1

journal

Stabilizing l1-norm prediction models by supervised feature grouping

I Kamkar, S Gupta, Q Phung, S Venkatesh

(2016), Vol. 59, pp. 149-168, Journal of biomedical informatics, Amsterdam, The Netherlands, C1

journal

Graph-induced restricted Boltzmann machines for document modeling

T Nguyen, T Tran, Q Phung, S Venkatesh

(2016), Vol. 328, pp. 60-75, Information sciences, Amsterdam, The Netherlands, C1

journal

DeepCare: a deep dynamic memory model for predictive medicine

T Pham, T Tran, Q Phung, S Venkatesh

(2016), Vol. 9652, pp. 30-41, The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016, Auckland, New Zealand, B1

chapter

Neural choice by elimination via highway networks

T Tran, D Phung, S Venkatesh

(2016), Vol. 9794, pp. 15-25, Trends and applications in knowledge discovery and data mining: PAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Auckland, New Zealand, April 19, 2016, revised selected papers, Cham, Switzerland, B1

chapter

A new transfer learning framework with application to model-agnostic multi-task learning

S Gupta, S Rana, B Saha, Q Phung, S Venkatesh

(2016), Vol. 49, pp. 933-973, Knowledge and information systems, London, England, C1

journal

Nonparametric discovery of movement patterns from accelerometer signals

T Nguyen, S Gupta, S Venkatesh, Q Phung

(2016), Vol. 70, pp. 52-58, Pattern recognition letters, Amsterdam, The Netherlands, C1

journal

Streaming variational inference for dirichlet process mixtures

H Huynh, Q Phung, S Venkatesh

(2016), Vol. 45, pp. 237-252, ACML 2015: Proceedings of the 7th Asian Conference on Machine Learning, Hong Kong, PRC, E1

conference

A framework for classifying online mental health related communities with an interest in depression

B Saha, T Nguyen, Q Phung, S Venkatesh

(2016), Vol. 20, pp. 1008-1015, IEEE journal of biomedical and health informatics, Piscatawy, N.J., C1

journal

Hierarchical Bayesian nonparametric models for knowledge discovery from electronic medical records

C Li, S Rana, D Phung, S Venkatesh

(2016), Vol. 99, pp. 168-182, Knowledge-based systems, Amsterdam, The Netherlands, C1

journal

Toxicity prediction in cancer using multiple instance learning in a multi-task framework

C Li, S Gupta, S Rana, W Luo, S Venkatesh, D Ashley, Q Phung

(2016), Vol. 9651, pp. 152-164, Advances in knowledge discovery and data mining: 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19-22, 2016 proceedings, part I, Berlin, Germany, B1

chapter

Sparse adaptive multi-hyperplane machine

K Nguyen, T Le, V Nguyen, Q Phung

(2016), Vol. 9651, pp. 27-39, Advances in knowledge discovery and data mining: 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19?22, 2016 Proceedings, Part I, New York, N.Y., B1

chapter

Consistency of the Health of the Nation Outcome Scales (HoNOS) at inpatient-to-community transition.

W Luo, R Harvey, T Tran, Q Phung, S Venkatesh, J Connor

(2016), Vol. 6, pp. 1-6, BMJ open, London, Eng., C1

journal

SECC: simultaneous extraction of context and community from pervasive signals

T Nguyen, V Nguyen, F Salim, Q Phung

(2016), pp. 1-9, PerCom 2016: Proceedings of the 14th IEEE International Conference on Pervasive Computing and Communications, Sydney, N.S.W., E1

conference

Dirichlet process mixture models with pairwise constraints for data clustering

C Li, S Rana, Q Phung, S Venkatesh

(2016), Vol. 3, pp. 205-223, Annals of data science, Berlin, Germany, C1

journal

Transfer learning for rare cancer problems via discriminative sparse gaussian graphical mode

B Saha, S Gupta, Q Phung, S Venkatesh

(2016), pp. 537-542, 2016 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, E1

conference

Collaborative filtering via sparse Markov random fields

T Tran, D Phung, S Venkatesh

(2016), Vol. 369, pp. 221-237, Information sciences, Amsterdam, The Netherlands, C1

journal

Budgeted semi-supervised support vector machine

T Le, P Duong, M Dinh, T Nguyen, T Nguyen, Q Phung

(2016), pp. 377-386, UAI 2016: Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, New York, N.Y., E1

conference

Scalable nonparametric Bayesian multilevel clustering

V Huynh, Q Phung, S Venkatesh, X Nguyen, M Hoffman, H Bui

(2016), pp. 289-298, UAI 2016: Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, New York, N.Y., E1

conference

Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection?

W Luo, E Huning, T Tran, D Phung, S Venkatesh

(2016), Vol. 2, pp. 1-15, Heliyon, Amsterdam, The Netherlands, C1

journal

Forecasting daily patient outflow from a ward having no real-time clinical data

S Gopakumar, T Tran, W Luo, D Phung, S Venkatesh

(2016), Vol. 4, pp. 1-16, JMIR medical informatics, Toronto, Ont., C1

journal

Preterm birth prediction : deriving stable and interpretable rules from high dimensional data

T Tran, W Luo, Q Phung, J Morris, K Rickard, S Venkatesh

(2016), pp. 1-13, MLHC 2016 : Proceedings on Conference on Machine Learning in Healthcare, Los Angeles, California, E1

conference

Discovering latent affective dynamics among individuals in online mental health-related communities

B Dao, T Nguyen, S Venkatesh, D Phung

(2016), pp. 473-478, ICME 2016 : Proceedings of the IEEE Multimedia and Expo International Conference, Seattle, Washington, E1

conference

Discriminative cues for different stages of smoking cessation in online community

T Nguyen, R Borland, J Yearwood, H Yong, S Venkatesh, D Phung

(2016), Vol. LNCS 10042, pp. 146-153, WISE 2016 : Proceedings of the 17th International Conference on Web Information Systems Engineering, Shanghai, China, E1

conference

Large-scale stylistic analysis of formality in academia and social media

T Nguyen, S Venkatesh, D Phung

(2016), Vol. LNCS 10042, pp. 137-145, WISE 2016 : Proceedings of the 17th International Conference on Web Information Systems Engineering, Shanghai, China, E1

conference

Guidelines for developing and reporting machine learning predictive models in biomedical research : a multidisciplinary view

W Luo, Q Phung, T Tran, S Gupta, S Rana, C Karmakar, A Shilton, J Yearwood, N Dimitrova, T Ho, S Venkatesh, M Berk

(2016), Vol. 18, pp. 1-10, Journal of medical internet research, Toronto, Ont., C1

journal

Textual cues for online depression in community and personal settings

T Nguyen, S Venkatesh, D Phung

(2016), Vol. 10086, pp. 19-34, ADMA 2016 : Proceedings of the Advanced Data Mining and Applications Conference, Gold Coast, Qld., E1

conference

Outlier detection on mixed-type data: an energy-based approach

D Do, D DO, T Tran, D Phung, S Venkatesh

(2016), Vol. 10086, pp. 111-125, ADMA 2016 : Proceedings of the 12th International Conference of Advanced Data Mining and Applications, Gold Coat, Queensland, E1

conference

Stabilizing linear prediction models using autoencoder

S Gopakumar, T Tran, D Phung, S Venkatesh

(2016), Vol. 10086, pp. 651-663, ADMA 2016 : Proceedings of the 12th International Conference for Advanced Data Mining and Applications, Gold Coast, Queensland, E1

conference

Exceptional contrast set mining: moving beyond the deluge of the obvious

D Nguyen, W Luo, D Phung, S Venkatesh

(2016), Vol. LNAI 9992, pp. 455-468, AI 2016 : Advances in artificial intelligence : Proceedings of the 29th Australian Joint Conference, Hobart, Tas., E1

conference

Forecasting patient outflow from wards having no real-time clinical data

S Gopakumar, T Tran, W Luo, D Phung, S Venkatesh

(2016), pp. 177-183, ICHI 2016: Proceedings of the IEEE International Conference on Healthcare Informatics, Chicago, Illinois, E1

conference

Learning multifaceted latent activities from heterogeneous mobile data

T Nguyen, V Nguyen, T Nguyen, S Venkatesh, M Kumar, D Phung

(2016), pp. 389-398, DSAA 2016 : Proceedings of the 3rd IEEE International Conference on Data Science and Advanced Analytics, Montreal, Canada, E1

conference

Effect of social capital on emotion, language style and latent topics in online depression community

B Dao, T Nguyen, S Venkatesh, D Phung

(2016), pp. 61-66, RIVF 2016 : Proceedings of the 2016 IEEE RIVF International Conference on Computing & Communication Technologies Research, Innovation, and Vision for the Future, Hanoi, Vietnam, E1

conference

Analysing the history of autism spectrum disorder using topic models

A Beykikhoshk, D Phung, O Arandjelovic, S Venkatesh

(2016), pp. 762-771, DSAA 2016 : Proceedings of the 3rd IEEE International Conference on Data Science and Advanced Analytics, Montreal, Canada, E1

conference

Computer assisted autism interventions for India

P Vellanki, S Greenhill, T Duong, D Phung, S Venkatesh, J Godwin, K Achary, B Varkey

(2016), pp. 618-622, OzCHI 2016 : Connected futures : Proceedings of the 28th Australian Conference on the Human-Computer Interaction, Launceston, Tas., E1

conference

Faster training of very deep networks via p-norm gates

T Tran, T Pham, Q Phung, S Venkatesh

(2016), pp. 3542-3547, ICPR 2016: Proceedings of the 23rd International Conference on Pattern Recognition, Cancun, Mexico, E1

conference

Distributed data augmented support vector machine on spark

T Nguyen, V Nguyen, T Le, D Phung

(2016), pp. 498-503, 2016 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, E1

conference

Dual space gradient descent for online learning

T Le, T Nguyen, V Nguyen, D Phung

(2016), Vol. 29, pp. 1-9, NIPS 2016 : Advances in neural information processing systems : Proceedings of the 30th Conference on Neural Information Processing Systems, Barcelona, Spain, E1

conference

Stable clinical prediction using graph support vector machines

I Kamkar, S Gupta, C Li, D Phung, S Venkatesh

(2016), pp. 3332-3337, 2016 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, E1

conference

MCNC: multi-channel nonparametric clustering from heterogeneous data

T Nguyen, V Nguyen, S Venkatesh, D Phung

(2016), pp. 3633-3638, 2016 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, E1

conference

Clustering for point pattern data

N Tran, B Vo, D Phung, B Vo

(2016), pp. 3174-3179, 2016 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, E1

conference
2015

TOBY play-pad application to teach children with ASD - a pilot trial

D Moore, S Venkatesh, A Anderson, S Greenhill, Q Phung, T Duong, D Cairns, W Marshall, A Whitehouse

(2015), Vol. 18, pp. 213-217, Developmental neurorehabilitation, London, Eng., C1

journal

Stabilized sparse ordinal regression for medical risk stratification

T Tran, D Phung, W Luo, S Venkatesh

(2015), Vol. 43, pp. 555-582, Knowledge and information systems: an international journal, Berlin, Germany, C1

journal

Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso

I Kamkar, S Gupta, D Phung, S Venkatesh

(2015), Vol. 53, pp. 277-290, Journal of biomedical informatics, Amsterdam, The Netherlands, C1

journal

Continuous discovery of co-location contexts from Bluetooth data

T Nguyen, S Gupta, S Venkatesh, Q Phung

(2015), Vol. 16, pp. 286-304, Pervasive and mobile computing, Amsterdam, The Netherlands, C1

journal

Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

T Tran, T Nguyen, Q Phung, S Venkatesh

(2015), Vol. 54, pp. 96-105, Journal of biomedical informatics, Amsterdam, The Netherlands, C1

journal

Web search activity data accurately predict population chronic disease risk in the USA

T Nguyen, T Tran, W Luo, S Gupta, S Rana, Q Phung, M Nichols, L Millar, S Venkatesh, S Allender

(2015), Vol. 69, pp. 693-699, Journal of epidemiology and community health, London, Eng., C1

journal

Visual object clustering via mixed-norm regularization

X Zhang, D Pham, Q Phung, W Liu, B Saha, S Venkatesh

(2015), pp. 1030-1037, WACV 2015: Proceedings of the 15th IEEE Winter Conference on Applications of Computer Vision, Waikoloa, Hawaii, E1

conference

Mixed-norm sparse representation for multi view face recognition

X Zhang, D Pham, S Venkatesh, W Liu, Q Phung

(2015), Vol. 48, pp. 2935-2946, Pattern recognition, Amsterdam, The Netherlands, C1

journal

A predictive framework for modeling healthcare data with evolving clinical interventions

S Rana, S Gupta, Q Phung, S Venkatesh

(2015), Vol. 8, pp. 162-182, Statistical analysis and data mining, London, Eng., C1

journal

Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset

W Luo, T Nguyen, M Nichols, T Tran, S Rana, S Gupta, Q Phung, S Venkatesh, S Allender

(2015), Vol. 10, pp. 1-13, PLoS One, San Francisco, Calif., C1

journal

Stabilizing high-dimensional prediction models using feature graphs.

S Gopakumar, T Tran, T Nguyen, Q Phung, S Venkatesh

(2015), Vol. 19, pp. 1044-1052, IEEE journal of biomedical and health informatics, Champaign, III., C1

journal

Autism blogs: expressed emotion, language styles and concerns in personal and community settings

T Nguyen, T Duong, S Venkatesh, Q Phung

(2015), Vol. 6, pp. 312-323, IEEE transactions on affective computing, Piscataway, N.J., C1

journal

Using Twitter to learn about the autism community

A Beykikhoshk, O Arandjelovic, D Phung, S Venkatesh, T Caelli

(2015), Vol. 5, pp. 1-17, Social Network Analysis and Mining, New York, N.Y., C1

journal

A Bayesian nonparametric approach to multilevel regression

T Nguyen, Q Phung, Venkatesh, H Bui

(2015), Vol. 9077, pp. 330-342, PAKDD 2015: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, Ho Chi Minh City, Vietnam, B1

chapter

Tensor-variate restricted Boltzmann machines

T Nguyen, Q Phung, T Tran, S Venkatesh

(2015), Vol. 3, pp. 2887-2893, AAAI 2015: The Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, Tex., E1

conference

Small-variance asymptotics for bayesian nonparametric models with constraints

C Li, S Rana, Q Phung, S Venkatesh

(2015), Vol. 9078, pp. 92-105, Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Vietnam, B1

chapter

Fast one-class support vector machine for novelty detection

T Le, Q Phung, K Nguyen, Venkatesh

(2015), Vol. 9078, pp. 189-200, Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, Vietnam, B1

chapter

Learning conditional latent structures from multiple data sources

H Huynh, Q Phung, L Nguyen, Venkatesh, H Bui

(2015), pp. 343-354, Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Vietnam, B1

chapter

Collaborating differently on different topics: a multi-relational approach to multi-task learning

S Gupta, S Rana, Q Phung, S Venkatesh

(2015), Vol. 9077, pp. 303-316, Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Vietnam, B1

chapter

Stabilizing sparse cox model using statistic and semantic structures in electronic medical records

S Gopakumar, T Nguyen, T Tran, Q Phung, Venkatesh

(2015), Vol. 9078, pp. 331-343, Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Vietnam, B1

chapter

Hierarchical dirichlet process for tracking complex topical structure evolution and its application to autism research literature

A Beykikhoshk, O Arandjelovic, S Venkatesh, Q Phung

(2015), pp. 550-562, Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Vietnam, B1

chapter

Bayesian nonparametric approaches to abnormality detection in video surveillance

T Nguyen, Q Phung, D Pham, Venkatesh

(2015), Vol. 2, pp. 21-41, Annals of data science, Berlin, Germany, C1

journal

Topic model kernel classification with probabilistically reduced features

T Nguyen, D Phung, S Venkatesh

(2015), Vol. 13, pp. 323-340, Journal of data science, New York, N.Y., C1

journal

Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis

A Beykikhoshk, O Arandjelovic, D Phung, S Venkatesh

(2015), pp. 1354-1361, ASONAM 2015: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Paris, France, E1

conference

Differentiating sub-groups of online depression-related communities using textual cues

T Nguyen, B O Dea, M Larsen, Q Phung, S Venkatesh, H Christensen

(2015), Vol. 9419, pp. 216-224, Web Information Systems Engineering ? WISE 2015, New York, N.Y., B1

chapter

Stable feature selection with support vector machines

I Kamkar, S Gupta, Q Phung, S Venkatesh

(2015), Vol. 9457, pp. 298-308, AI 2015: Advances in artificial intelligence. 28th Australasian Joint Conference Canberra, ACT, Australia, November 30 - December 4, 2015 Proceedings, Berlin, Germany, B1

chapter

Understanding toxicities and complications of cancer treatment: a data mining approach

D Nguyen, W Luo, S Venkatesh, Q Phung

(2015), Vol. 9457, pp. 431-443, AI 2015: Advances in artificial intelligence. 28th Australasian Joint Conference Canberra, ACT, Australia, November 30 - December 4, 2015 Proceedings, Berlin, Germany, B1

chapter

Learning entry profiles of children with autism from multivariate treatment information using restricted boltzmann machines

P Vellanki, Q Phung, T Duong, S Venkatesh

(2015), Vol. 9441, pp. 245-257, Trends and applications in knowledge discovery and data mining: PAKDD 2015 Workshops: BigPMA, VLSP, QIMIE, DAEBH Ho Chi Minh City, Vietnam, May 19?21, 2015 Revised Selected Papers, Berlin, Germany, B1

chapter

Nonparametric discovery of online mental health-related communities

D Dao, T Nguyen, S Venkatesh, Q Phung

(2015), pp. 1-10, DSAA 2015: Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, Paris, France, E1

conference

Exploiting feature relationships towards stable feature selection

I Kamkar, S Gupta, Q Phung, S Venkatesh

(2015), pp. 1-10, DSAA 2015: IEEE International Conference on Data Science and Advanced Analytics, Paris, France, E1

conference

What shall i share and with whom? A multi-task learning formulation using multi-faceted task relationships

S Gupta, S Rana, Q Phung, S Venkatesh

(2015), pp. 703-711, SDM 2015: Proceedings of the 15th SIAM International Conference on Data Mining, Vancouver, British Columbia, E1

conference

Multi-view subspace clustering for face images

X Zhang, Q Phung, S Venkatesh, D Pham, W Liu

(2015), pp. 1-8, DICTA 2015: Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications, Adelaide, S. Aust., E1

conference

Learning multi-faceted activities from heterogeneous data with the product space hierarchical Dirichlet processes

T Nguyen, V Nguyen, S Venkatesh, D Phung

(2015), Vol. 9794, pp. 128-140, Trends and Applications in Knowledge Discovery and Data Mining, Berlin, Germany, B1

chapter
2014

Social reader: towards browsing the social web

B Adams, D Phung, S Venkatesh

(2014), Vol. 69, pp. 951-990, Multimedia tools and applications, Secaucus, NJ, C1

journal

Mood sensing from social media texts and its applications

T Nguyen, Q Phung, B Adams, S Venkatesh

(2014), Vol. 39, pp. 667-702, Knowledge and information systems, Berlin, Germany, C1

journal

Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments

T Tran, W Luo, D Phung, R Harvey, M Berk, R Kennedy, S Venkatesh

(2014), Vol. 14, pp. 1-9, BMC psychiatry, London, Eng., C1

journal

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

S Gupta, T Tran, W Luo, D Phung, R Kennedy, A Broad, D Campbell, D Kipp, M Singh, M Khasraw, L Matheson, D Ashley, S Venkatesh

(2014), Vol. 4, pp. 1-7, BMJ open, London, England, C1

journal

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

S Rana, T Tran, W Luo, D Phung, R Kennedy, S Venkatesh

(2014), Vol. 38, pp. 377-382, Australian health review, Melbourne, Vic., C1

journal

Fixed-lag particle filter for continuous context discovery using Indian Buffet Process

C Nguyen, S Gupta, S Venkatesh, Dinh Phung

(2014), pp. 20-28, PerCom 2014 : proceedings of the IEEE Pervasive Computing and Communications 2014 international conference, Budapest, Hungary, E1

conference

Learning latent activities from social signals with hierarchical dirichlet processes

D Phung, T Nguyen, S Gupta, S Venkatesh

(2014), pp. 149-174, Plan, activity, and intent recognition : theory and practice, Boston, Mass., B1

chapter

Affective and content analysis of online depression communities

T Nguyen, D Phung, B Dao, S Venkatesh, M Berk

(2014), Vol. 5, pp. 217-226, IEEE Transactions on Affective Computing, Piscataway, N.J, C1

journal

Intervention-driven predictive framework for modeling healthcare data

S Rana, S Gupta, D Phung, S Venkatesh

(2014), Vol. 8443 Part 1, pp. 497-508, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Berlin, Germany, B1

chapter

Data-mining twitter and the autism spectrum disorder: a pilot study

A Beykikhoshk, O Arandjelovi?, D Phung, S Venkatesh, T Caelli

(2014), pp. 349-356, ASONAM 2014 : Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Beijing, China, E1

conference

Tree-based iterated local search for Markov random fields with applications in image analysis

T Tran, D Phung, S Venkatesh

(2014), Vol. 21, pp. 25-45, Journal of heuristics, Berlin, Germany, C1

journal

iPoll: Automatic polling using online search

T Nguyen, D Phung, W Luo, T Tran, S Venkatesh

(2014), Vol. 8786, pp. 266-275, Web Information Systems Engineering ? WISE 2014, Berlin, Germany, B1

chapter

Effect of mood, social connectivity and age in online depression community via topic and linguistic analysis

B Dao, T Nguyen, D Phung, S Venkatesh

(2014), Vol. 8786, pp. 398-407, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Berlin, Germany, B1

chapter

Affective, linguistic and topic patterns in online autism communities

T Nguyen, T Duong, D Phung, S Venkatesh

(2014), Vol. 8787, pp. 474-488, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Berlin, Germany, B1

chapter

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

S Gupta, D Phung, S Venkatesh

(2014), pp. 1-23, Multimedia Tools and Applications, New York, New York, C1

journal

Bayesian nonparametric multilevel clustering with group-level contexts

V Nguyen, D Phung, X Nguyen, S Venkatesh, H Bui

(2014), Vol. 32, pp. 288-269, ICML 2014 : Proceedings of the 31st International Conference on Machine Learning, Beijing, China, E1

conference

A bayesian nonparametric framework for activity recognition using accelerometer data

T Nguyen, S Gupta, S Venkatesh, D Phung

(2014), pp. 2017-2022, ICPR 2014 : Proceedings of the 22nd International Conference on Pattern Recognition, Stockholm, Sweden, E1

conference

Nonparametric discovery of learning patterns and autism subgroups from therapeutic data

P Vellanki, T Duong, S Venkatesh, D Phung

(2014), pp. 1828-1833, ICPR 2014 : Proceedings of the 22nd International Conference on Pattern Recognition, Stockholm, Sweden, E1

conference

Regularizing topic discovery in emrs with side information by using hierarchical bayesian models

C Li, S Rana, D Phung, S Venkatesh

(2014), pp. 1307-1312, ICPR 2014 : Proceedings of the 22nd International Conference on Pattern Recognition, Stockholm, Sweden, E1

conference

A framework for feature extraction from hospital medical data with applications in risk prediction

T Truyen, W Luo, P Dinh, S Gupta, S Rana, R Kennedy, A Larkins, S Venkatesh

(2014), Vol. 15, pp. 1-9, BMC Bioinformatics, London, Eng., C1

journal

A random finite set model for data clustering

Q Phung, B Vo

(2014), pp. 1-8, FUSION 2014 : Proceedings of the 17th International Conference on Information Fusion, Salamanca, Spain, E1

conference

Analysis of circadian rhythms from online communities of individuals with affective disorders

B Dao, T Nguyen, S Venkatesh, D Phung

(2014), pp. 463-469, DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics, Shanghai, China, E1

conference

Individualized arrhythmia detection with ECG signals from wearable devices

B Nguyen, W Lou, T Caelli, S Venkatesh, D Phung

(2014), pp. 570-576, DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics, Shanghai, China, E1

conference

Unsupervised inference of significant locations from WiFi data for understanding human dynamics

T Nguyen, T Nguyen, W Luo, Venkatesh, Q Phung

(2014), pp. 232-235, MUM 2014 : Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia, Melbourne, Victoria, E1-1

conference

Speed up health research through topic modeling of coded clinical data

W Luo, Q Phung, T Nguyen, T Tran, S Venkatesh

(2014), pp. 1-4, IAPR 2014 : Proceedings of 2nd International Workshop on Pattern Recognition for Healthcare Analytics, Stockholm, Sweden, E1-1

conference

Keeping up with innovation: a predictive framework for modeling healthcare data with evolving clinical interventions

S Gupta, S Rana, Q Phung, S Venkatesh

(2014), pp. 235-243, SDM 2014: Proceedings of the 14th SIAM International Conference on Data Mining 2014, Philadelphia, Pennsylvania, E1-1

conference

A matrix factorization framework for jointly analyzing multiple nonnegative data sources

S Gupta, Q Phung, B Adams, S Venkatesh

(2014), Vol. 3, pp. 151-170, Data mining for service, Berlin, Germany, B1-1

chapter

Labeled random finite sets and the bayes multi-target tracking filter

B Vo, B Vo, D Phung

(2014), Vol. 62, pp. 6554-6567, IEEE transactions on signal processing, Piscataway, N.J., C1-1

journal

Stabilizing sparse Cox model using clinical structures in electronic medical records

T Tran, S Gopakumar, Q Phung, S Venkatesh

(2014), pp. 1-4, IAPR 2014: Proceedings of 2nd International Workshop on Pattern Recognition for Healthcare Analytics, Stockholm, Sweden, E1-1

conference
2013

Regularized nonnegative shared subspace learning

S Gupta, D Phung, B Adams, S Venkatesh

(2013), Vol. 26, pp. 57-97, Data mining and knowledge discovery, Boston, Mass., C1-1

journal

Event extraction using behaviors of sentiment signals and burst structure in social media

T Nguyen, D Phung, B Adams, S Venkatesh

(2013), Vol. 37, pp. 279-304, Knowledge and information systems, London, England, C1

journal

Sparse subspace clustering via group sparse coding

B Saha, D Pham, D Phung, S Venkatesh

(2013), pp. 130-138, SDM 2013 : Proceedings of the thirteenth SIAM International Conference on Data Mining, Austin, Texas, E1

conference

Clustering patient medical records via sparse subspace representation

B Saha, D Pham, D Phung, S Venkatesh

(2013), pp. 123-134, PAKDD 2013 : Advances in knowledge discovery and data mining : 17th Pacific-Asia Conference, Gold Coast, Australia, April 14-17, 2013 : proceedings, Gold Coast, Queensland, E1

conference

Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine

T Nguyen, T Tran, D Phung, S Venkatesh

(2013), pp. 123-135, PAKDD 2013 : Advances in knowledge discovery and data mining : 17th Pacific-Asia Conference, Gold Coast, Australia, April 14-17, 2013 : proceedings, Gold Coast, Queensland, E1

conference

Split-merge augmented Gibbs sampling for hierarchical dirichlet processes

S Rana, D Phung, S Venkatesh

(2013), pp. 546-557, PAKDD 2013 : Advances in knowledge discovery and data mining : 17th Pacific-Asia Conference, Gold Coast, Australia, April 14-17, 2013 : proceedings, Gold Coast, Queensland, E1

conference

TOBY : Early intervention in autism through technology

S Venkatesh, D Phung, T Duong, S Greenhill, B Adams

(2013), pp. 3187-3196, CHI 2013 : Changing perspectives : Proceedings of the 31st Annual Conference on Human Factors in Computing Systems, Paris, France, E1

conference

Exploiting side information in distance dependent Chinese restaurant processes for data clustering

C Li, D Phung, S Rana, S Venkatesh

(2013), pp. 1-6, ICME 2013 : Proceedings of the 14th IEEE International Conference on Multimedia and Expo, San Jose, California, E1

conference

Analysis of psycholinguistic processes and topics in online autism communities

T Nguyen, D Phung, S Venkatesh

(2013), pp. 1-6, ICME 2013 : Proceedings of the 14th IEEE International Conference on Multimedia and Expo, San Jose, California, E1

conference

Learning sparse latent representation and distance metric for image retrieval

T Nguyen, T Truyen, D Phung, S Venkatesh

(2013), pp. 1-6, ICME 2013 : Proceedings of the 14th IEEE International Conference on Multimedia and Expo, San Jose, California, E1

conference

Online social capital : mood, topical and psycholinguistic analysis

T Nguyen, B Dao, D Phung, S Venkatesh, M Berk

(2013), pp. 449-456, ICWSM 2013 : Proceedings of the 7th AAAI International Conference on Weblogs and Social Media, Cambridge, Massachusetts, E1

conference

Interactive browsing system for anomaly video surveillance

T Nguyen, D Phung, S Gupta, S Venkatesh

(2013), pp. 384-389, ISSNIP 2013 : Sensing the future : Proceedings of the IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Victoria, E1

conference

Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

T Nguyen, D Phung, S Gupta, S Venkatesh

(2013), pp. 47-55, PerCom 2013 : Proceedings of the 11th IEEE International Conference on Pervasive Computing and Commmunications, San Diego, California, E1

conference

Connectivity, online social capital, and mood : a Bayesian nonparametric analysis

D Phung, S Gupta, T Nguyen, S Venkatesh

(2013), Vol. 15, pp. 1316-1325, IEEE transactions on multimedia, Piscataway, N.J., C1

journal

Topic model kernel : an empirical study towards probabilistically reduced features for classification

T Nguyen, D Phung, S Venkatesh

(2013), pp. 124-131, ICONIP 2013 : Neural information processing : 20th International Conference, Daegu, Korea, November 3-7, 2013. Proceedings, Daegu, Korea, E1

conference

Thurstonian Boltzmann machines: learning from multiple inequalities

T Tran, D Phung, S Venkatesh

(2013), Vol. 28, pp. 46-54, ICML 2013 : Proceedings of the Machine Learning 2013 International Conference, Atlanta, Ga., E1-1

conference

Factorial multi-task learning : a Bayesian nonparametric approach

S Gupta, Q Phung, S Venkatesh

(2013), pp. 1694-1702, ICML 2013 : Proceedings of the Machine Learning 2013 International Conference, Atlanta, Ga., E1-1

conference

An integrated framework for suicide risk prediction

T Tran, Q Phung, W Luo, R Harvey, M Berk, S Venkatesh

(2013), pp. 1410-1418, KDD'13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, Chicago, Ill., E1-1

conference

Learning parts-based representations with nonnegative restricted boltzmann machine

T Nguyen, T Tran, Q Phung, S Venkatesh

(2013), Vol. 29, pp. 133-148, ACML 2013 : Proceedings of the 5th Asian Conference on Machine Learning, Canberra, Australia, E1-1

conference
2012

Detection of cross-channel anomalies

D Pham, B Saha, D Phung, S Venkatesh

(2012), Vol. 35, pp. 33-59, Knowledge and Information Systems, Berlin, Germany, C1

journal

A bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources

S Gupta, D Phung, S Venkatesh

(2012), pp. 200-212, SDM 2012 : Proceedings of the 12th SIAM International Conference on Data Mining, Anaheim, Calif., E1

conference

Improved subspace clustering via exploitation of spatial constraints

D Pham, B Saha, D Phung, S Venkatesh

(2012), pp. 550-557, CVPR 2012 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, R. I., E1

conference

Learning Boltzmann distance metric for face recognition

T Tran, D Phung, S Venkatesh

(2012), pp. 218-223, ICME 2012 : Proceedings of the 13th IEEE International Conference on Multimedia and Expo, Melbourne, Victoria, E1

conference

Embedded restricted Boltzmann machines for fusion of mixed data types and applications in social measurements analysis

T Tran, D Phung, S Venkatesh

(2012), pp. 1814-1821, FUSION 2012 : Proceedings of the 15th International Conference on Information Fusion, Singapore, E1

conference

Pervasive multimedia for autism intervention

S Venkatesh, S Greenhill, D Phung, B Adams, T Duong

(2012), Vol. 8, pp. 863-882, Pervasive and mobile computing, Amsterdam, The Netherlands, C1

journal

Sparse subspace representation for spectral document clustering

B Saha, D Phung, D Pham, S Venkatesh

(2012), pp. 1092-1097, ICDM 2012 : Proceedings of the 12th IEEE International Conference on Data Mining, Brussels, Belgium, E1

conference

Large-scale statistical modeling of motion patterns : a Bayesian nonparametric approach

S Rana, D Phung, S Pham, S Venkatesh

(2012), pp. 1-8, ICVGIP 2012 : Proceedings of the 8th Indian Conference on Computer Vision, Graphics and Image Processing, Mumbai, India, E1

conference

A sequential decision approach to ordinal preferences in recommender systems

T Tran, D Phung, S Venkatesh

(2012), pp. 676-682, AAAI 2012 : Proceedings of the 26th National Conference on Artificial Intelligence, Toronto, Ont., E1

conference

Cumulative restricted Boltzmann machines for ordinal matrix data analysis

T Tran, D Phung, S Venkatesh

(2012), pp. 411-426, ACML 2012 : Proceedings of the 4th Asian Conference on Machine Learning, Singapore, E1

conference

Learning from ordered sets and applications in collaborative ranking

T Tran, D Phung, S Venkatesh

(2012), pp. 427-442, ACML 2012 : Proceedings of the 4th Asian Conference on Machine Learning, Singapore, E1

conference

A nonparametric Bayesian Poisson Gamma model for count data

S Gupta, D Phung, S Venkatesh

(2012), pp. 1815-1818, ICPR 2012 : Proceedings of 21st International Conference on Pattern Recognition, Tsubuka Science City, Japan, E1

conference

Multi-modal abnormality detection in video with unknown data segmentation

T Nguyen, D Phung, S Rana, D Pham, S Venkatesh

(2012), pp. 1322-1325, ICPR 2012 : Proceedings of 21st International Conference on Pattern Recognition, Tsubuka Science City, Japan, E1

conference

A sentiment-aware approach to community formation in social media

T Nguyen, D Phung, B Adams, S Venkatesh

(2012), pp. 527-530, ICWSM 2012 : Proceedings of the Sixth International Conference on Weblogs and Social Media, Dublin, Ireland, E1

conference

A slice sampler for restricted hierarchical beta process with applications to shared subspace learning

S Gupta, D Phung, S Venkatesh

(2012), pp. 316-325, UAI 2012 : Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, Catalina Island, California, E1

conference
2011

Cognitive intervention in autism using multimedia stimulus

S Venkatesh, S Greenhill, D Phung, B Adams

(2011), pp. 769-770, MM'11 : Proceedings of the 19th ACM Multimedia Conference and Co-Located Workshops, Scottsdale, Ariz., E1-1

conference

Eventscapes : visualizing events over time with emotive facets

B Adams, D Phung, S Venkatesh

(2011), pp. 1477-1480, MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops, Scottsdale, AZ, E1-1

conference

A context-sensitive device to help people with autism cope with anxiety

M Mohammedali, B Adams, D Phung, S Venkatesh

(2011), pp. 1201-1206, CHI EA 2011 : Proceedings of the 29th Conference on Human Factors in Computing Systems 2011, Vancouver, British Columbia, E1-1

conference

Prediction of age, sentiment, and connectivity from social media text

T Nguyen, D Phung, B Adams, S Venkatesh

(2011), pp. 227-240, WISE 2011 : Web Information Systems Engineering : 12th International Conference, Sydney, Australia, October 13-14 2011 : proceedings, Sydney, New South Wales, E1-1

conference

A Bayesian framework for learning shared and individual subspaces from multiple data sources

S Gupta, D Phung, B Adams, S Venkatesh

(2011), pp. 136-147, PAKDD 2011 : Advances in knowledge discovery and data mining : 15th Pacific-Asia Conference, Shenzhen, China, May 24-27, 2011, proceedings, part II, Shenzhen, China, E1-1

conference

Mixed-variate restricted Boltzmann machines

T Tran, D Phung, S Venkatesh

(2011), pp. 213-229, ACML 2011 : Proceedings of the 3rd Asian Conference on Machine Learning, Taoyuan, Taiwan, E1-1

conference

Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering

T Truyen, D Phung, S Venkatesh

(2011), pp. 426-437, SDM 2011 : Proceedings of the 11th SIAM International Conference on Data Mining, Mesa, Ariz., E1-1

conference

Towards discovery of influence and personality traits through social link prediction

T Nguyen, D Phung, B Adams, S Venkatesh

(2011), pp. 566-569, ICWSM-11 : Proceedings of the 5th AAAI International Conference on Weblogs and Social Media, Barcelona, Spain, E1-1

conference

A matrix factorization framework for jointly analyzing multiple nonnegative data sources

S Gupta, D Phung, B Adams, S Venkatesh

(2011), pp. 6-15, Proceedings of the 9th Workshop on Text Mining, in conjunction with the 11th SIAM International Conference on Data Mining, Mesa, Ariz., E1-1

conference

Detection of cross-channel anomalies from multiple data channels

D Pham, B Saha, D Phung, S Venkatesh

(2011), pp. 527-536, ICDM 2011 : Proceedings of the IEEE International Conference on Data Mining, Vancouver, B. C., E1-1

conference

Emotional reactions to real-world events in social networks

T Nguyen, D Phung, B Adams, S Venkatesh

(2011), pp. 53-64, New Frontiers in Applied Data Mining : Proceedings of the PAKDD 2011 International Workshops, Shenzhen, China, E1-1

conference
2010

Discovery of latent subcommunities in a blog's readership

B Adams, D Phung, S Venkatesh

(2010), Vol. 4, pp. 1-30, ACM transactions on the web, New York, N. Y., C1-1

journal

Nonnegative shared subspace learning and its application to social media retrieval

S Gupta, D Phung, B Adams, T Tran, S Venkatesh

(2010), pp. 1169-1178, KDD 2010 : Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, D. C., E1-1

conference

Hyper-community detection in the blogosphere

T Nguyen, D Phung, B Adams, T Tran, S Venkatesh

(2010), pp. 21-26, WSM 2010 : Proceedings of the 2nd ACM SIGMM Workshop on Social Media, Firenze, Italy, E1-1

conference

Classification and pattern discovery of mood in weblogs

T Nguyen, D Phung, B Adams, T Tran, S Venkatesh

(2010), pp. 283-290, PAKDD 2010 : Advances in knowledge discovery and data mining : 14th Pacific-Asia Conference, Hydrabad, India, E1-1

conference
2009

Efficient duration and hierarchical modeling for human activity recognition

T Duong, D Phung, H Bui, S Venkatesh

(2009), Vol. 173, pp. 830-856, Artificial intelligence, Amsterdam, Netherlands, C1-1

journal

Unsupervised context detection using wireless signals

D Phung, B Adams, S Venkatesh, M Kumar

(2009), Vol. 5, pp. 714-733, Pervasive and mobile computing, Amsterdam, Netherlands, C1-1

journal

Ordinal Boltzmann machines for collaborative filtering

T Truyen, D Phung, S Venkatesh

(2009), pp. 548-556, UAI 2009 : Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, E1-1

conference

Social reader : following social networks in the wilds of the blogosphere

B Adams, D Phung, S Venkatesh

(2009), pp. 73-80, WSM'09 : Proceedings of the 1st ACM SIGMM International Workshop on Social Media, Beijing, China, E1-1

conference

Flickr hypergroups

R Negoescu, B Adams, D Phung, S Venkatesh, D Gatica-Perez

(2009), pp. 813-816, MM'09 : Proceedings of the 17th ACM Multimedia Conference, Beijing, China, E1-1

conference

High accuracy context recovery using clustering mechanisms

D Phung, B Adams, K Tran, S Venkatesh, M Kumar

(2009), pp. 2-9, PerCom 2009 : Proceedings of the 7th Annual IEEE International Conference on Pervasive Computing and Communications, Galveston, Tex., E1-1

conference
2008

Sensing and using social context

B Adams, D Phung, S Venkatesh

(2008), Vol. 5, pp. 1-27, ACM transactions on multimedia computing communications and applications, New York, N. Y., C1-1

journal

The hidden permutation model and location-based activity recognition

H Bui, D Phung, S Venkatesh, H Phan

(2008), pp. 1345-1350, AAAI 2008 : Proceedings of the 23rd AAAI Conference on Artificial Intelligence, Chicago, Ill., E1-1

conference

Computable social patterns from sparse sensor data

D Phung, B Adams, S Venkatesh

(2008), pp. 69-72, LocWeb 2008 : Proceedings of the 1st International Workshop on Location and the web, Beijing, China, E1-1

conference

Constrained sequence classification for lexical disambiguation

T Truyen, D Phung, S Venkatesh

(2008), pp. 430-441, PRICAI 2008 : Trends in Artificial Intelligence : 10th Pacific Rim International Conference on Artificial Intelligence, Hanoi, Vietnam, December 15-19, 2008, proceedings, Hanoi, Vietnam, E1-1

conference

Learning discriminative sequence models from partially labelled data for activity recognition

T Truyen, H Bui, D Phung, S Venkatesh

(2008), pp. 903-912, PRICAI 2008 : Trends in Artificial Intelligence : 10th Pacific Rim International Conference on Artificial Intelligence, Hanoi, Vietnam, December 15-19, 2008, proceedings, Hanoi, Vietnam, E1-1

conference

Hierarchical semi-markov conditional random fields for recursive sequential data

T Truyen, D Phung, H Bui, S Venkatesh

(2008), pp. 1657-1664, NIPS 2008 : Advances in Neural Information Processing Systems 21 : Proceedings of the 2008 Conference, Vancouver, B. C., E1-1

conference

Indoor location prediction using multiple wireless received signal strengths

K Tran, D Phung, B Adams, S Venkatesh

(2008), pp. 187-192, AusDM 2008 : Proceedings of the 7th Australasian Data Mining Conference, Glenelg, S. Aust., E1-1

conference
2007

Preference Networks: probabilistic models for recommendation systems

T Tran, D Phung, S Venkatesh

(2007), pp. 195-202, AusDM 2007 : Proceedings of 6th Australasian Data Mining Conference., Gold Coast, N.S.W., E1-1

conference
2006

AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition

T Tran, Phung, H Bui, S Venkatesh

(2006), pp. 1686-1693, CVPR 2006 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, N.Y., E1-1

conference

A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment

D Tran, D Phung, H Bui, S Venkatesh

(2006), pp. 168-172, ICPR 2006 : Proceedings of the 18th International Conference on Pattern Recognition, Hong Kong, China, E1-1

conference

Human behavior recognition with generic exponential family duration modeling in the hidden semi-Markov model

T Duong, D Phung, H Bui, S Venkatesh

(2006), pp. 202-207, ICPR 2006 : Proceedings of the 18th International Conference on Pattern Recognition, Hong Kong, China, E1-1

conference

Extraction of social context and application to personal multimedia exploration

B Adams, D Phung, S Venkatesh

(2006), pp. 987-996, ACM-MM 2006 : Proceedings of the 14th Annual ACM International Conference on Multimedia, Santa Barbara, Calif., E1-1

conference
2005

Activity recognition and abnormality detection with the switching hidden semi-Markov model

T Duong, H Bui, D Phung, S Venkatesh

(2005), pp. 838-845, CVPR 2005 : Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, Calif., E1-1

conference

Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

N Nguyen, D Phung, S Venkatesh, H Bui

(2005), pp. 955-960, CVPR 2005 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, Calif., E1-1

conference

Factored state-abstract hidden Markov models for activity recognition using pervasive multi-modal sensors

D Tran, D Phung, H Bui, S Venkatesh

(2005), pp. 331-335, Proceedings of the 2005 Intelligent Sensors, Sensor Networks & Information Processing Conference, Melbourne, Vic., E1-1

conference

Topic transition detection using hierarchical hidden Markov and semi-Markov models

D Phung, T Duong, S Venkatesh, H Bui

(2005), pp. 11-20, MM'05 : Proceedings of the 13th ACM International Conference on Multimedia, Singapore, Singapore, E1-1

conference

Efficient Coxian duration modelling for activity recognition in smart environment with the hidden semi-Markov model

T Duong, D Phung, H Bui, S Venkatesh

(2005), pp. 277-282, Proceedings of the 2005 intelligent sensors, sensor networks and information processing conference, Melbourne, Vic., E1-1

conference
2004

Automatically learning structural units in educational videos with the hierarchical hidden Markov models

D Phung, S Venkatesh, H Bui

(2004), pp. 1605-1608, ICIP 2004 : Proceedings of the 2004 International Conference on Image Processing, Singapore, E1-1

conference

Hierarchical hidden Markov models with general state hierarchy

H Bui, D Phung, S Venkatesh

(2004), pp. 324-329, Proceedings of the National Conference on Artificial Intelligence, San Jose, Calif., E1-1

conference

Content structure discovery in educational videos using shared structures in the hierarchical hidden Markov models

D Phung, H Bui, S Venkatesh

(2004), pp. 1155-1163, Structural, syntactic, and statistical pattern recognition : joint IAPR international workshops SSPR 2004 and SPR 2004, Lisbon, Portugal, August 18-20, 2004 : proceedings, Berlin, Germany, B1-1

chapter
2003

Hierarchical topical segmentation in instructional films based on cinematic expressive functions

D Phung, S Venkatesh, C Dorai

(2003), pp. 287-290, MULTIMEDIA 2003 : Proceedings of the 11th ACM International Multimedia Conference and Exhibition, Berkeley, Calif., E1-1

conference

On the extraction of thematic and dramatic functions of content in educational videos

D Phung, S Venkatesh, C Dorai

(2003), pp. 449-452, ICME 2003 : Proceedings of the International Conference on Multimedia and Expo, Baltimore, Md., E1-1

conference
2002

High level segmentation of instructional videos based on content density

D Phung, S Venkatesh, C Dorai

(2002), pp. 295-298, MULTIMEDIA 2002 : Proceedings of the 10th ACM International Multimedia Conference and Exhibition, Juan-les-Pins, France, E1-1

conference

Narrative structure analysis with education and training videos for e-learning

Q Phung, C Dorai, S Venkatesh

(2002), pp. 835-838, ICPR 2002 : Proceedings of the 16th International Conference on Pattern Recognition, Quebec, Canada, E1-1

conference

Video genre categorization using audio wavelet coefficients

P Dinh, C Dorai, S Venkatesh

(2002), pp. 69-74, ACCV 2002 : Proceedings of the 5th Asian Conference on Computer Vision, Melbourne, Vic., E1-1

conference

Funded Projects at Deakin

Australian Competitive Grants

Assistive Technologies for Autism Support Harnessing Social Media

Prof Svetha Venkatesh, Prof Dinh Phung, Dr Aneesh Krishna, Dr Brett Adams

ARC Linkage - Projects Rnd 1

  • 2013: $153,358
  • 2012: $194,112

Robust and scalable change detection in geo-spatial data

Prof Svetha Venkatesh, Prof Dinh Phung, Dr Sonny Pham

Linkage PhD student, ARC Linkage - Projects Rnd 2

  • 2014: $179,628
  • 2013: $177,941
  • 2012: $85,674

Assistive Tools for Early Intervention in Autism

Prof Svetha Venkatesh, Prof Dinh Phung, Mrs Silvana Gaglia

ARC Linkage - Projects

  • 2017: $67,656
  • 2016: $133,312
  • 2015: $131,084
  • 2014: $64,389

Stay well: Analysing lifestyle data from smart monitoring devices.

Prof Dinh Phung, Prof Svetha Venkatesh

ARC - Discovery Projects

  • 2017: $114,741
  • 2016: $129,091
  • 2015: $132,328

Nonparametric Machine Learning for Modern Data Analytics

Prof Dinh Phung

ARC - Discovery Projects

  • 2017: $123,567
  • 2016: $69,980

Seeing is believing: wearable cameras for self-management in people with heart failure

Prof Ralph Maddison, Prof Kylie Ball, A/Prof Chris Neil, Prof Dinh Phung

NHF Vanguard Grant - National Heart Foundation of Australia

  • 2017: $73,046

ARC Research Hub for Digital Enhanced Living

Prof John Grundy, Prof Svetha Venkatesh, Prof Anthony Maeder, Prof Kon Mouzakis, Prof Alison Hutchinson, Prof Michael Berk, Prof Ralph Maddison, Prof Abbas Kouzani, Prof Rajesh Vasa, Prof Rafael Calvo, Prof Helen Christensen, Prof Patricia Williams, Prof Dinh Phung, Prof John Yearwood, Prof Susan Gordon, Prof David Powers, Prof Nilmini Wickramasinghe, A/Prof Niranjan Bidargaddi, Dr Santu Rana, Dr Truyen Tran, Dr Sunil Gupta, Dr Wei Luo, Dr Mohamed Abdelrazek, Dr Felix Tan, Prof Henning Langberg, A/Prof Lars Kayser, A/Prof Finn Kensing, Prof Freimut Bodendorf, Prof John Hansen, Prof James Warren, Dr Roopak Sinha, Prof A Smeaton, Mr Ian Aitken, Mr Fonda Voukelatos, Mr Jeffrey Fiebig, Mr Dean Serroni, Mr Christopher Farguhar, Mr Ramesh Nagarajan, Ms Jennifer Biggin, Mr John Fouyaxis, Mr Emmanuel Gerasimou, David Varley

ARC Industrial Transformation Research Hubs

  • 2017: $505,426

Other Public Sector Funding

Barwon Health Partnership - Advanced data analytics for care management of chronic disease

Prof Svetha Venkatesh, Prof Dinh Phung

  • 2016: $50,000
  • 2015: $100,000
  • 2014: $20,000
  • 2013: $50,000
  • 2012: $50,000

Deep anomaly detection from multiple sensors and applications in situation awareness

Prof Dinh Phung, Prof Svetha Venkatesh

  • 2017: $10,000
  • 2016: $15,000

Adversarial Machine Learning for Cyber

Dr Jun Zhang, Prof Dinh Phung, Dr Sheng Wen, Dr Ben Rubinstein

  • 2017: $5,604

Industry and Other Funding

Assistive Technologies for Autism Support Harnessing Social Media

Prof Svetha Venkatesh, Prof Dinh Phung, Dr Aneesh Krishna, Dr Brett Adams

  • 2014: $12,500
  • 2013: $80,000
  • 2012: $121,013

Robust and scalable change detection in geo-spatial data

Prof Svetha Venkatesh, Prof Dinh Phung, Dr Sonny Pham

  • 2016: $16,500
  • 2015: $33,000
  • 2014: $33,000
  • 2013: $16,500

Therapy Outcomes By You (TOBY) Playpad randomised controlled trial to test the effectiveness of the application for early intervention in Autism.

Prof Svetha Venkatesh, Prof Dinh Phung

  • 2013: $60,000
  • 2012: $70,000

Assistive Tools for Early Intervention in Autism

Prof Svetha Venkatesh, Prof Dinh Phung, Mrs Silvana Gaglia

  • 2017: $80,000
  • 2016: $40,000
  • 2015: $80,000

Network anomaly detection

Prof Dinh Phung, Prof Svetha Venkatesh

  • 2015: $223,580
  • 2014: $26,740

Leveraging Social Media for Improving Mental Health

Prof Svetha Venkatesh, Prof Dinh Phung, Ms Trang Thi Minh Pham

  • 2016: $3,000
  • 2015: $3,000

Adobe Systems Incorporated research support

Prof Dinh Phung

  • 2017: $12,776
  • 2016: $17,893

Bayesian Learning with Unbounded Capacity from Heterogeneous and Set-Valued Data

Prof Dinh Phung

  • 2017: $36,843
  • 2016: $38,570

Improving mental health through social media

Dr Thin Nguyen, Prof Dinh Phung, Prof Svetha Venkatesh, Dr Bridianne O'Dea, Dr Mark Larsen, Prof Helen Christensen

  • 2017: $20,000
  • 2016: $20,000

Digital Enhanced Living Project

Prof Kon Mouzakis, Prof Rajesh Vasa, Dr Niroshinie Fernando, Dr Mohamed Abdelrazek, Prof Svetha Venkatesh, Prof Dinh Phung, Prof John Grundy

  • 2017: $680,000
  • 2016: $501,220

Large-scale Neural Embedding

Prof Dinh Phung, Dr Thin Nguyen

  • 2017: $9,235

Other Funding Sources

Computational infrastructure for developing deep machine learning models.

Prof Ian Reid, Prof Svetha Venkatesh, Prof Peter Corke, Prof Mohammed Bennamoun, A/Prof Stephen Gould, Prof Anton van den Hengel, Prof Chunhua Shen, Dr Anthony Dick, A/Prof Gustavo Cameiro, Prof Dinh Phung, Dr Niko Suenderhauf, A/Prof Ajmal Mian

  • 2016: $29,094

Supervisions

Principal Supervisor
2017

Huu Viet Huynh

Thesis entitled: Towards Scalable Bayesian Nonparametric Methods for Data Analytics

Doctor of Philosophy (Information Technology), School of Information Technology

2016

Adham Beykikhoshk

Thesis entitled: Knowledge Discovery and Probabilistic Models of Online Autism Spectrum Disorder Data

Doctor of Philosophy (Information Technology), School of Information Technology

2015

Tien Vu Nguyen

Thesis entitled: Bayesian nonparametric multilevel modelling and applications

Doctor of Philosophy (Information Technology), School of Information Technology

Tu Dinh Nguyen

Thesis entitled: Structured representation learning from complex data

Doctor of Philosophy (Information Technology), School of Information Technology

Cong Thuong Nguyen

Thesis entitled: Bayesian nonparametric learning of contexts and activities from pervasive signals

Doctor of Philosophy (Information Technology), School of Information Technology

Cheng Li

Thesis entitled: Exploiting side information in Bayesian nonparametric models and their applications

Doctor of Philosophy (Information Technology), School of Information Technology

Associate Supervisor
2017

Shivapratap Gopakumar

Thesis entitled: Machine Learning in Healthcare: An Investigation into Model Stability

Doctor of Philosophy (Information Technology), School of Information Technology

2016

Iman Kamkar

Thesis entitled: Building Stable Predictive Models for Healthcare Applications: A Data-Driven Approach

Doctor of Philosophy (Information Technology), School of Information Technology

Bo Dao

Thesis entitled: Social Media as Sensor for Healthcare: A Machine Learning Approach

Doctor of Philosophy (Information Technology), School of Information Technology

Xin Zhang

Thesis entitled: Sparse representation for face images

Doctor of Philosophy (Information Technology), School of Information Technology

Pratibha Vellanki

Thesis entitled: Applied Machine Learning for Personalised Early Intervention in Autism

Doctor of Philosophy (Information Technology), School of Information Technology