Profile image of Truyen Tran

A/Prof. Truyen Tran

STAFF PROFILE

Position

Head, AI, Health and Science

Faculty

Applied Artificial Intel Inst

Department

A2I2P

Campus

Geelong Waurn Ponds Campus

Qualifications

Graduate Certificate of Higher Education, Deakin University, 2016
Doctor of Philosophy, Curtin University, 2008
Postgraduate Diploma in Science, Curtin University, 2005
Bachelor of Science, University of Melbourne, 2001

Biography summary

Truyen Tran is an Associate Professor at Deakin University. He is member of Applied Artificial Intelligence Institute where he leads the work on deep learning and its application on health, genomics, software and materials science. His other research topics include probabilistic graphical models, recommender systems, learning to rank, anomaly detection, multi-relational databases, model stability, and mixed-type analysis. He publishes regularly in top venues such as CVPR, NIPS, UAI, AAAI, KDD, ICML, PAKDD, and ACML. Tran has received multiple recognition, awards and prizes including Best Paper Runner Up at UAI (2009), Geelong Tech Award (2013), CRESP Best Paper of the Year (2014), Third Prize on Kaggle Galaxy-Zoo Challenge (2014), Title of Kaggle Master (2014), Best Student Paper Runner Up at ADMA (2016), Distinguished Paper at ACM SIGSOFT (2015), and Best Student Paper Runner Up at PAKDD(2016). He obtained a Bachelor of Science from University of Melbourne and a PhD in Computer Science from Curtin University in 2001 and 2008, respectively.

Research interests

Artificial intelligence

Data science

Biomedical informatics

Teaching interests

Machine learning

Data science

Healthcare analytics

Units taught

Data Science Concepts (SIT-112)

Knowledge areas

Machine learning

Data mining

Artificial intelligence

Healthcare analytics

Biomedical informatics

Recommender systems

Data science

Research groups

Applied Artificial Intelligence Institute

Awards

2016: ADMA'16 Best student paper runner-up

2015: PAKDD'15 Best student paper runner-up

2015: ACM  SIGSOFT Distinguished Paper Award.

2014: CRESP Best paper ward

2014: Team third prize in the Galaxy Zoo challenge

2013: Team best tech ward in Geelong

2009: UAI'09 Best paper runner-up

Publications

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2022

GEFA: early fusion approach in drug-target affinity prediction

Tri Nguyen, Thin Nguyen, Thao Le, Truyen Tran

(2022), Vol. 19, pp. 718-728, IEEE/ACM Transactions on Computational Biology and Bioinformatics, New York, N.Y., C1

journal article

Performances of Different Machine Learning Algorithms for Predicting Saltwater Intrusion in the Vietnamese Mekong Delta Using Limited Input Data: A Study from Ham Luong River

T Tran, N Pham, Q Pham, T Pham, X Ngo, D Nguyen, P Nguyen, B Veettil

(2022), Vol. 49, pp. 391-401, Water Resources, Moscow, Russian Federation, C1

journal article

Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring

T Nguyen, T Nguyen, T Tran

(2022), Vol. 23, Briefings in bioinformatics, England, C1

journal article

Machine Learning-Aided Exploration of Ultrahard Materials

S Tawfik, P Nguyen, T Tran, T Walsh, S Venkatesh

(2022), Vol. 126, pp. 15952-15961, Journal of Physical Chemistry C, C1

journal article

EvSys: A Relational Dynamic System for Sparse Irregular Clinical Events

D Nguyen, P Nguyen, T Tran

(2022), Vol. 1013, pp. 267-279, W3PHAI 2021: Proceedings of the 5th International Workshop on Health Intelligence. AI for Disease Surveillance and Pandemic Intelligence Intelligent Disease Detection in Action, Virtual event, E1

conference

Learning Theory of Mind via Dynamic Traits Attribution

D Nguyen, P Nguyen, H Le, K Do, S Venkatesh, T Tran

(2022), Vol. 2, pp. 954-962, AAMAS 2022 : Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems 2022, Auckland, N.Z., E1

conference

Learning to Transfer Role Assignment Across Team Sizes

D Nguyen, P Nguyen, S Venkatesh, T Tran

(2022), Vol. 2, pp. 963-971, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, E1

conference

Persistent-Transient Duality in Human Behavior Modeling

H Tran, V Le, S Venkatesh, T Tran

(2022), Vol. 2022-June, pp. 2527-2530, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, E1

conference
2021

Automatic Feature Learning for Predicting Vulnerable Software Components

H Dam, T Tran, T Pham, S Ng, J Grundy, A Ghose

(2021), Vol. 47, pp. 67-85, IEEE Transactions on Software Engineering, C1

journal article

Automatically recommending components for issue reports using deep learning

M Choetkiertikul, H Dam, T Tran, T Pham, C Ragkhitwetsagul, A Ghose

(2021), Vol. 26, Empirical Software Engineering, C1

journal article

PAN: Personalized Annotation-Based Networks for the Prediction of Breast Cancer Relapse

T Nguyen, S Lee, T Quinn, B Truong, X Li, T Tran, S Venkatesh, T Le

(2021), Vol. 18, pp. 2841-2847, IEEE/ACM Transactions on Computational Biology and Bioinformatics, United States, C1

journal article

A Spatio-Temporal Attention-Based Model for Infant Movement Assessment from Videos

B Nguyen-Thai, V Le, C Morgan, N Badawi, T Tran, S Venkatesh

(2021), Vol. 25, pp. 3911-3920, IEEE Journal of Biomedical and Health Informatics, United States, C1

journal article

Hierarchical Conditional Relation Networks for Multimodal Video Question Answering

T Le, V Le, S Venkatesh, T Tran

(2021), Vol. 129, pp. 3027-3050, International Journal of Computer Vision, C1

journal article

Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records

S Rana, W Luo, T Tran, S Venkatesh, P Talman, T Phan, D Phung, B Clissold

(2021), Vol. 12, Frontiers in Neurology, Switzerland, C1

journal article

Goal-driven long-term trajectory prediction

H Tran, V Le, T Tran

(2021), pp. 796-805, WACV 2021 : Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Waikoloa, Hawaii, E1

conference

Knowledge Distillation with Distribution Mismatch

D Nguyen, S Gupta, T Nguyen, S Rana, P Nguyen, T Tran, K Le, S Ryan, S Venkatesh

(2021), Vol. 12976, pp. 250-265, ECML PKDD 2021 : Machine Learning and Knowledge Discovery in Databases. Research Track, Bilbao, Spain, E1

conference

Fast Conditional Network Compression Using Bayesian HyperNetworks

P Nguyen, T Tran, K Le, S Gupta, S Rana, D Nguyen, T Nguyen, S Ryan, S Venkatesh

(2021), Vol. 12977, pp. 330-345, ECML PKDD 2021 : Machine Learning and Knowledge Discovery in Databases. Research Track, Bilbao, Spain, E1

conference

Variational Hyper-encoding Networks

P Nguyen, T Tran, S Gupta, S Rana, H Dam, S Venkatesh

(2021), Vol. 12976, pp. 100-115, ECML PKDD 2021 : Machine Learning and Knowledge Discovery in Databases. Research Track, Bilbao, Spain, E1

conference

Object-Centric Representation Learning for Video Question Answering

L Dang, T Le, V Le, T Tran

(2021), pp. 1-8, 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China, E1

conference

Hierarchical Object-oriented Spatio-Temporal Reasoning for Video Question Answering

L Dang, T Le, V Le, T Tran

(2021), pp. 636-642, IJCAI 2021 : Proceedings of the 30th International Joint Conference on Artificial Intelligence, Online from Montreal, Canada, E1

conference

DeepProcess: Supporting Business Process Execution Using a MANN-Based Recommender System

A Khan, H Le, K Do, T Tran, A Ghose, H Dam, R Sindhgatta

(2021), Vol. 13121, pp. 19-33, ICSOC 2021 : Proceedings of the 19th Service-Oriented Computing Iternational Computing, Virtual Event, E1

conference

Learning asynchronous and sparse human-object interaction in videos

R Morais, V Le, S Venkatesh, T Tran

(2021), pp. 16036-16045, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, Tenn., E1

conference

Clustering by Maximizing Mutual Information Across Views

K Do, T Tran, S Venkatesh

(2021), pp. 9908-9918, Proceedings of the IEEE International Conference on Computer Vision, E1

conference

Model-Based Episodic Memory Induces Dynamic Hybrid Controls

H Le, T George, M Abdolshah, T Tran, S Venkatesh

(2021), Vol. 36, pp. 1-26, NeurIPS 2021 : Proceedings of the 35th Conference on Neural Information Processing Systems, Virtual Conference, E1

conference
2020

Precision psychiatry with immunological and cognitive biomarkers: a multi-domain prediction for the diagnosis of bipolar disorder or schizophrenia using machine learning

B Fernandes, C Karmakar, R Tamouza, T Tran, J Yearwood, N Hamdani, H Laouamri, J Richard, R Yolken, M Berk, S Venkatesh, M Leboyer

(2020), Vol. 10, Translational Psychiatry, United States, C1

journal article

Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Networks Predict Gut Metabolites from Gut Microbiome

V Le, T Quinn, T Tran, S Venkatesh

(2020), Vol. 21, BMC Genomics, Sydney, AUSTRALIA, C1

journal article

Neural Stored-program Memory

Hung Le, Truyen Tran, Svetha Venkatesh

(2020), pp. 1-27, ICLR 2020 : Proceedings of the 8th International Conference on Learning Representations, Virtual Conference, Ethiopia, E1

conference

DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types

Adham Beykikhoshk, Thomas Quinn, Samuel Lee, Truyen Tran, Svetha Venkatesh

(2020), Vol. 13, pp. 1-10, GIW/ABACBS 2019 : Proceedings of the Joint 30th International Conference on Genome Informatics (GIW) & Australian Bioinformatics and Computational Biology Society (ABACBS) Annual Confernence, Sydney, N.S.W., E1

conference

Dynamic Language Binding in Relational Visual Reasoning

Thao Minh Le, Vuong Le, Svetha Venkatesh, Truyen Tran

(2020), pp. 818-824, IJCAI-PRICAI 2020 : Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Online from Yokohama, Japan, E1

conference

Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning

T Karimpanal, S Rana, S Gupta, T Tran, S Venkatesh

(2020), pp. 1-10, IJCNN 2020 : Proceedings of the 2020 International Joint Conference on Neural Networks, Glasgow, Scotland, E1

conference

Catastrophic forgetting and mode collapse in GANs

H Thanh-Tung, T Tran

(2020), pp. 1-28, IJCNN 2020 : Proceedings of the 2020 International Joint Conference on Neural Networks, Online from Glasgow, Scotland, E1

conference

Neural Reasoning, Fast and Slow, for Video Question Answering

T Le, V Le, S Venkatesh, T Tran

(2020), pp. 1-8, IJCNN 2020 : Proceedings of the 2020 International Joint Conference on Neural Networks, Online from Glasgow, Scotland, E1

conference

Hierarchical conditional relation networks for video question answering

T Le, V Le, S Venkatesh, T Tran

(2020), pp. 9969-9978, CVPR 2020 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Virtual from Seattle, Washington, E1

conference

Unsupervised Anomaly Detection on Temporal Multiway Data

D Nguyen, P Nguyen, K Do, S Rana, S Gupta, T Tran

(2020), pp. 1059-1066, SSCI 2020 : Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, Canberra, Australian Capital Territory, E1

conference

Self-Attentive associative memory

H Le, T Tran, S Venkatesh

(2020), pp. 5638-5647, ICML 2020 : Proceedings of the 37th International Conference on Machine Learning, Online, E1

conference
2019

A Deep Learning Model for Estimating Story Points

M Choetkiertikul, H Dam, T Tran, T Pham, A Ghose, T Menzies

(2019), Vol. 45, pp. 637-656, IEEE Transactions on Software Engineering, C1

journal article

Attentional multilabel learning over graphs: a message passing approach

K Do, T Tran, T Nguyen, S Venkatesh

(2019), Vol. 108, pp. 1757-1781, Machine Learning, New York, N.Y., C1

journal article

Incomplete conditional density estimation for fast materials discovery

P Nguyen, T Tran, S Gupta, S Rana, M Barnett, S Venkatesh

(2019), pp. 549-557, 2019 SIAM : Proceedings of the 2019 SIAM International Conference on Data Mining, Calgary, Alta., E1

conference

Learning to remember more with less memorization

H Le, T Tran, S Venkatesh

(2019), ICLR 2019: Proceedings of the 7th International Conference on Learning Representations, New Orleans, Louisiana, E1

conference

Improving generalization and stability of generative adversarial networks

H Thanh-Tung, S Venkatesh, T Tran

(2019), ICLR 2019: Proceedings of the 7th International Conference on Learning Representations, New Orleans, Louisiana, E1

conference

Graph transformation policy network for chemical reaction prediction

K Do, T Tran, S Venkatesh

(2019), pp. 750-760, KDD 2019 : Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, Alaska, E1

conference

Towards effective AI-powered agile project management

H Dam, T Tran, J Grundy, A Ghose, Y Kamei

(2019), pp. 41-44, 2019 IEEE/ACM : 41st International Conference on Software Engineering: New Ideas and Emerging Results, Montreal, Canada, E1

conference

Lessons learned from using a deep tree-based model for software defect prediction in practice

H Dam, T Pham, S Ng, T Tran, J Grundy, A Ghose, T Kim, C Kim

(2019), pp. 46-57, MSR 2019 : Proceedings of the 16th IEEE/ACM International Conference on Mining Software Repositories, Montreal, Quebec, E1

conference

Learning regularity in skeleton trajectories for anomaly detection in videos

R Morais, V Le, T Tran, B Saha, M Mansour, S Venkatesh

(2019), pp. 11988-11996, CVPR 2019 : Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, California, E1

conference
2018

Predicting Delivery Capability in Iterative Software Development

M Choetkiertikul, H Dam, T Tran, A Ghose, J Grundy

(2018), Vol. 44, pp. 551-573, IEEE Transactions on Software Engineering, C1

journal article

Energy-based anomaly detection for mixed data

K Do, T Tran, S Venkatesh

(2018), Vol. 57, pp. 413-435, Knowledge and Information Systems, C1

journal article

Committee machine that votes for similarity between materials

D Nguyen, T Pham, V Nguyen, T Ho, T Tran, K Takahashi, H Dam

(2018), Vol. 5, pp. 830-840, IUCrJ, England, C1

journal article

Explainable software analytics

H Dam, T Tran, A Ghose

(2018), pp. 53-56, ICSE-NIER 2018 : Proceedings of the 40th International Conference on Software Engineering: New Ideas and Emerging Results, Gothenburg, Sweden, E1

conference

Resset: A Recurrent Model for Sequence of Sets with Applications to Electronic Medical Records

P Nguyen, T Tran, S Venkatesh

(2018), 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, E1

conference

Dual control memory augmented neural networks for treatment recommendations

H Le, T Tran, S Venkatesh

(2018), Vol. 10939, pp. 273-284, PAKDD 2018 : Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Melbourne, Vic., E1

conference

Dual memory neural computer for asynchronous two-view sequential learning

Hung Le, Truyen Tran, Svetha Venkatesh

(2018), pp. 1637-1645, ACM SIGKDD 2018 : Proceedings of the 24th International Conference on Knowledge Discovery & Data Mining, London, England, E1

conference

Graph memory networks for molecular activity prediction

T Pham, T Tran, S Venkatesh

(2018), pp. 639-644, ICPR 2018: Proceedings of the 24th International Conference on Pattern Recognition, Beijing, China, E1

conference

Knowledge graph embedding with multiple relation projections

K Do, T Tran, S Venkatesh

(2018), pp. 332-337, ICPR 2018: Proceedings of the 24th International Conference on Pattern Recognition, Beijing, China, E1

conference

Variational Memory Encoder-Decoder

Hung Le, Truyen Tran, Thin Nguyen, Svetha Venkatesh

(2018), Vol. [31], pp. 1515-1525, NeurIPS 2018 : Proceedings of the Advances in Neural Information Processing Systems Annual Conference, Montreal, Canada, E1

conference
2017

Preference relation-based markov random fields for recommender systems

S Liu, G Li, T Tran, Y Jiang

(2017), Vol. 106, pp. 523-546, Machine Learning, C1

journal article

Predicting the delay of issues with due dates in software projects

M Choetkiertikul, H Dam, T Tran, A Ghose

(2017), Vol. 22, pp. 1223-1263, Empirical Software Engineering, C1

journal article

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, C1

journal article

Deepr: A Convolutional Net for Medical Records

P Nguyen, T Tran, N Wickramasinghe, S Venkatesh

(2017), Vol. 21, pp. 22-30, IEEE Journal of Biomedical and Health Informatics, United States, C1

journal article

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, United States, C1

journal article

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

Deep learning to attend to risk in ICU

P Nguyen, T Tran, S Venkatesh

(2017), Vol. 1891, pp. 25-29, KDH 2017 : Proceedings of the 2nd International Workshop on Knowledge Discovery in Healthcare Data 2017, Melbourne, Victoria, E1

conference
2016

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

book 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

book chapter

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

T Tran, D Phung, S Venkatesh

(2016), Vol. 47, pp. 157-188, Knowledge and Information Systems, C1

journal article

Graph-induced restricted Boltzmann machines for document modeling

T Nguyen, T Tran, D Phung, S Venkatesh

(2016), Vol. 328, pp. 60-75, Information Sciences, C1

journal article

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

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

(2016), Vol. 6, BMJ Open, England, C1

journal article

Collaborative filtering via sparse Markov random fields

T Tran, D Phung, S Venkatesh

(2016), Vol. 369, pp. 221-237, Information Sciences, C1

journal article

Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records

C Karmakar, W Luo, T Tran, M Berk, S Venkatesh

(2016), Vol. 3, JMIR Mental Health, Canada, C1

journal article

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, England, C1

journal article

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. 2-17, JMIR Medical Informatics, Canada, C1

journal article

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

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

(2016), Vol. 18, Journal of Medical Internet Research, Canada, C1

journal article

Preference relation-based markov random fields

S Liu, G Li, T Tran, J Yuan

(2016), pp. 1-16, ACML 2015: Proceedings of the 7th Asian Conference on Machine Learning, Hong Kong, E1

conference

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

DeepSoft: a vision for a deep model of software

H Dam, T Tran, J Grundy, A Ghose

(2016), pp. 944-947, FSE 2016: Proceedings of the 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, Seattle, Washington, 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

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

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
2015

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

book chapter

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 article

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 article

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 article

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 article

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 article

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

Characterization and prediction of issue-related risks in software projects

M Choetkiertikul, T Tran, H Dam, A Ghose

(2015), pp. 280-291, MSR 2015 : Proceedings of the 12th Working Conference on Mining Software Repositories, Florence, Italy, E1

conference

Who will answer my question on Stack Overflow?

M Choetkiertikul, D Avery, H Dam, T Tran, A Ghose

(2015), pp. 155-164, ASWEC 2015 : Proceedings of the 24th Australasian Software Engineering Conference, Adelaide, South Australia, E1

conference

Predicting delays in software projects using networked classification

M Choetikertikul, H Dam, T Tran, A Ghose

(2015), pp. 353-364, ASE 2015 : Proceedings of the 30th IEEE/ACM International Conference on Automated Software Engineering, Lincoln, Nebraska, E1

conference

Preference relation-based Markov random fields for recommender systems

S Liu, G Li, T Tran, Y Jiang

(2015), Vol. 45, pp. 157-172, ACML 2015 - 7th Asian Conference on Machine Learning, Hong Kong, E1-1

conference
2014

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

book chapter

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 article

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 article

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 article

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 article

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 article

Ordinal random fields for recommender systems

S Liu, T Tran, G Li, Y Jiang

(2014), Vol. 39, pp. 283-298, ACML 2014: Proceedings of the Sixth Asian Conference on Machine Learning, Nha Trang, Vietnam, E1

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

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

Ipoll: Automatic polling using online search

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

(2014), Vol. 8786 LNCS, pp. 266-275, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), E1-1

conference
2013

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

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

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

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

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

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
2011

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
2010

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

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
2008

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
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
2005

Boosted Markov networks for activity recognition

T Truyen, H Bui, S Venkatesh

(2005), pp. 289-294, Proceedings of the 2005 Intelligent Sensors, Sensor Networks & Information Processing Conference, Melbourne, Vic., 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

Human activity learning and segmentation using partially hidden discriminative models

T Truyen, H Bui, S Venkatesh

(2005), pp. 87-95, HAREM 2005 : Proceedings of the International Workshop on Human Activity Recognition and Modelling, Oxford, U. K., E1-1

conference

Funded Projects at Deakin

Australian Competitive Grants

ARC Research Hub for Digital Enhanced Living

Prof Kon Mouzakis, Prof Svetha Venkatesh, Prof Anthony Maeder, Prof Alison Hutchinson, Prof Michael Berk, Prof Ralph Maddison, Prof Abbas Kouzani, Prof Rajesh Vasa, Prof Helen Christensen, Prof Patricia Williams, Prof John Yearwood, Prof Susan Gordon, Prof David Powers, A/Prof Niranjan Bidargaddi, A/Prof Santu Rana, A/Prof Truyen Tran, A/Prof Sunil Gupta, Dr Wei Luo, A/Prof Mohamed Abdelrazek, Dr Felix Tan, Prof Henning Langberg, A/Prof Lars Kayser, Prof Finn Kensing, Prof Freimut Bodendorf, Prof James Warren, Dr Roopak Sinha, Prof A Smeaton, Mr Fonda Voukelatos, Mr John Fouyaxis, Prof John Grundy, Dr Kit Huckvale, Dr Leonard Hoon, David Varley, Nicole Cockayne, Dr Tanya Petrovich, Dr Anju Kissoon Curumsing, Matthew Macfarlane, Dr Scott Barnett, Ms Sharon Grocott, Dr Tom McClean, Prof Deborah Parker, Mr Steven Strange, Prof Jean-Guy Schneider, Dr Jessica Rivera Villicana, A/Prof Carsten Rudolph, Prof Nilmini Wickramasinghe, Mr Fernando Escorcia, Dr Gnana Bharathy

ARC Industrial Transformation Research Hubs

  • 2021: $388,477
  • 2020: $385,381
  • 2019: $399,716
  • 2018: $449,083
  • 2017: $601,698

A Generic Framework for Verifying Machine Learning Algorithms

Prof Svetha Venkatesh, A/Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran

ARC - Discovery Projects

  • 2022: $115,439
  • 2021: $122,286

Optimising treatments in mental health using AI

Prof Helen Christensen, Prof Svetha Venkatesh, Prof Henry Cutler, Ms Ros Knight, Dr Martin Laverty, A/Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran, Dr Thomas Quinn, Prof Rajesh Vasa, Prof Kon Mouzakis

MRFF (DISER) - Applied Artificial Intelligence Research in Health

  • 2022: $999,770
  • 2021: $1,431,416

Other Public Sector Funding

Al Algorithmic Assurance

Prof Svetha Venkatesh, A/Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran, Dr Anh Cat Le Ngo, Dr Phuoc Nguyen, Mr Stephan Jacobs, Dr Dang Nguyen

Department of Defence

  • 2021: $248,140
  • 2020: $208,820
  • 2019: $80,640

Defence Applied Al Experiential CoLab

Prof Svetha Venkatesh, A/Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran

DSTO Grant - Research - Defence Science & Technology Organisation

  • 2021: $100,000
  • 2020: $873,495

In relation to Assuring an off-the-shelf AI algorithm

A/Prof Sunil Gupta, A/Prof Truyen Tran, A/Prof Santu Rana, Prof Svetha Venkatesh, Dr Phuoc Nguyen, Mr Tiep-Trong Nguyen, Mr Stephan Jacobs

Defence Science and Technology Group - Department of Defence

  • 2022: $85,000
  • 2021: $168,034

Coupled self-supervised learning and deep reasoning for improved processing of noisy and dynamic multimodal data from multiple sources.

A/Prof Truyen Tran, A/Prof Shannon Ryan, A/Prof Sunil Gupta, A/Prof Santu Rana, Prof Svetha Venkatesh

Department of Defence

  • 2022: $105,364

Industry and Other Funding

Predicting software components containing safety hazards using deep learning

Dr Hoa Khanh Dam, Prof Aditya Ghose, A/Prof Truyen Tran, Prof John Grundy

Samsung Advanced Institute of Technology - Global Research Outreach Program

  • 2017: $52,397

ARC Research Hub for Digital Enhanced Living

Prof Kon Mouzakis, Prof Svetha Venkatesh, Prof Anthony Maeder, Prof Alison Hutchinson, Prof Michael Berk, Prof Ralph Maddison, Prof Abbas Kouzani, Prof Rajesh Vasa, Prof Helen Christensen, Prof Patricia Williams, Prof John Yearwood, Prof Susan Gordon, Prof David Powers, A/Prof Niranjan Bidargaddi, A/Prof Santu Rana, A/Prof Truyen Tran, A/Prof Sunil Gupta, Dr Wei Luo, A/Prof Mohamed Abdelrazek, Dr Felix Tan, Prof Henning Langberg, A/Prof Lars Kayser, Prof Finn Kensing, Prof Freimut Bodendorf, Prof James Warren, Dr Roopak Sinha, Prof A Smeaton, Mr Fonda Voukelatos, Mr John Fouyaxis, Prof John Grundy, Dr Kit Huckvale, Dr Leonard Hoon, David Varley, Nicole Cockayne, Dr Tanya Petrovich, Dr Anju Kissoon Curumsing, Matthew Macfarlane, Dr Scott Barnett, Ms Sharon Grocott, Dr Tom McClean, Prof Deborah Parker, Mr Steven Strange, Prof Jean-Guy Schneider, Dr Jessica Rivera Villicana, A/Prof Carsten Rudolph, Prof Nilmini Wickramasinghe, Mr Fernando Escorcia, Dr Gnana Bharathy

Interrelate Limited, Cancer Council Victoria Grant - Research, Black Dog Institute, goAct, Uniting NSW.ACT, Health Metrics, Uniting AgeWell, NeoProducts Pty Ltd, Unisono Pty Ltd, Dementia Australia (Alzheimer's Australia) Vic Inc, Aged Care & Housing Group Inc

  • 2022: $793,130
  • 2020: $553,025
  • 2019: $378,745

Scale Faciliation CoLab

Dr Scott Barnett, Dr Yasmeen George, Mr Andrew Vouliotis, Dr Srikanth Thudumu, Dr Leonard Hoon, A/Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran, Prof Rajesh Vasa, Prof Kon Mouzakis, Prof Svetha Venkatesh, Dr Rena Logothetis

Third Chapter Pty Ltd (Sanitex)

  • 2022: $600,000
  • 2021: $400,000

Fidgety Movement Detection for Robust and Interpretable Cerebral Palsy Risk Assessment

A/Prof Truyen Tran, Prof Svetha Venkatesh

Cerebral Palsy Alliance Grant - Research

  • 2022: $51,003

Al Enabled Eyelid Detection

A/Prof Truyen Tran, Mr Hoang Dang

Medmont International Pty Ltd

  • 2022: $10,000

SaniteX - Re-open

Dr Scott Barnett, Mr Andrew Vouliotis, Dr Srikanth Thudumu, Dr Rena Logothetis, Dr Leonard Hoon, A/Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran, Prof Rajesh Vasa, Prof Kon Mouzakis, Prof Svetha Venkatesh

Sanitex Global Pty Ltd

  • 2022: $650,000

Supervisions

Principal Supervisor
2022

Dung Nguyen

Thesis entitled: Towards Social Artificial Intelligence: Roles and Theory of Mind

Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins

Thanh Tung Hoang

Thesis entitled: Toward Generalizable Deep Generative Models

Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins

2021

Thao Minh Le

Thesis entitled: Deep Neural Networks for Visual Reasoning

Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins

2020

Thai Hung Le

Thesis entitled: Memory and attention in deep learning

Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins

Duc Kien Do

Thesis entitled: Novel Deep Architectures for Representation Learning

Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins

2019

Trang Thi Minh Pham

Thesis entitled: Recurrent Neural Networks for Structured Data

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

2017

Shivapratap Gopakumar

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

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

Associate Supervisor
2021

Tang Thanh Nguyen

Thesis entitled: On Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical Efficiency

Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins

Romero Fernando Almeida Barata De Morais

Thesis entitled: Human Behaviour Understanding in Computer Vision

Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins

2016

Xin Zhang

Thesis entitled: Sparse representation for face images

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

2015

Tu Dinh Nguyen

Thesis entitled: Structured representation learning from complex data

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