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
Media appearances
CRESP Best paper award announcement, Dec 2014
Machines learn to pick up the cues, Oct 2014
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
Projects
Machine learning for diagnosis of bipolar and schizophrenia
Understanding and predicting preterm births
Suicide risk prediction from electronic medical records
Deep learning for anomaly detection
Human microbiota and depression: a large population-based investigation
Predicting hazardous software components using deep learning (led by University of Wollongong)
Publications
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
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
T Nguyen, T Nguyen, T Tran
(2022), Vol. 23, Briefings in bioinformatics, England, C1
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, Washington, D.C., C1
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
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
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
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
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
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
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
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
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
S Rana, W Luo, T Tran, S Venkatesh, P Talman, T Phan, D Phung, B Clissold
(2021), Vol. 12, Frontiers in Neurology, Switzerland, C1
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
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
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
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
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
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
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
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
Clustering by Maximizing Mutual Information Across Views
K Do, T Tran, S Venkatesh
(2021), pp. 9908-9918, ICCV 2021 : Proceedings of the IEEE International Conference on Computer Vision, Virtual Conference, E1
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
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
V Le, T Quinn, T Tran, S Venkatesh
(2020), Vol. 21, BMC Genomics, Sydney, AUSTRALIA, C1
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Energy-based anomaly detection for mixed data
K Do, T Tran, S Venkatesh
(2018), Vol. 57, pp. 413-435, Knowledge and Information Systems, C1
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
T Tran, D Phung, S Venkatesh
(2016), Vol. 47, pp. 157-188, Knowledge and Information Systems, C1
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
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
Collaborative filtering via sparse Markov random fields
T Tran, D Phung, S Venkatesh
(2016), Vol. 369, pp. 221-237, Information Sciences, C1
C Karmakar, W Luo, T Tran, M Berk, S Venkatesh
(2016), Vol. 3, JMIR Mental Health, Canada, C1
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
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
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
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
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
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
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
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
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
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
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
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
T Tran, T Nguyen, Q Phung, S Venkatesh
(2015), Vol. 54, pp. 96-105, Journal of biomedical informatics, Amsterdam, The Netherlands, C1
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
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
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
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
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
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
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
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
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
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
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
S Rana, T Tran, W Luo, D Phung, R Kennedy, S Venkatesh
(2014), Vol. 38, pp. 377-382, Australian health review, Melbourne, Vic., C1
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
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
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
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
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
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
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
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
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
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
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
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
T Tran, D Phung, S Venkatesh
(2012), pp. 1814-1821, FUSION 2012 : Proceedings of the 15th International Conference on Information Fusion, Singapore, E1
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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, 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, Nicole Cockayne, Dr Leonard Hoon, David Varley, Dr Tanya Petrovich, Matthew Macfarlane, Dr Anju Kissoon Curumsing, Ms Sharon Grocott, Prof Deborah Parker, Dr Tom McClean, Dr Scott Barnett, Mr Steven Strange, Prof Jean-Guy Schneider, Prof Nilmini Wickramasinghe, Dr Jessica Rivera Villicana, A/Prof Carsten Rudolph, 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
Leveraging digital technology to reduce the prevalence and severity of eating disorders in Australia
Prof Matthew Fuller-Tyszkiewicz, Prof Susan Paxton, Dr Scott Griffiths, A/Prof Truyen Tran, Dr Sian McLean, Dr Zali Yager, A/Prof Rachel Rodgers, Prof Cathrine Mihalopoulos, Prof Denise Meyer, Dr Alexandra Parker
MRFF Million Minds Mission Grant Opportunity
- 2022: $190,322
- 2021: $151,556
- 2019: $499,380
A Generic Framework for Verifying Machine Learning Algorithms
Prof Svetha Venkatesh, Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran
ARC - Discovery Projects
- 2023: $37,272
- 2022: $125,478
- 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, 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
- 2023: $254,910
- 2022: $999,770
- 2021: $1,431,416
Other Public Sector Funding
Al Algorithmic Assurance
Prof Svetha Venkatesh, 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, 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
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, Prof Sunil Gupta, A/Prof Santu Rana, Prof Svetha Venkatesh
Department of Defence
- 2022: $105,364
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, Prof Sunil Gupta, A/Prof Santu Rana, Prof Svetha Venkatesh
Defence Science and Technology Group - Department of Defence
- 2023: $73,000
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, 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, Nicole Cockayne, Dr Leonard Hoon, David Varley, Dr Tanya Petrovich, Matthew Macfarlane, Dr Anju Kissoon Curumsing, Ms Sharon Grocott, Prof Deborah Parker, Dr Tom McClean, Dr Scott Barnett, Mr Steven Strange, Prof Jean-Guy Schneider, Prof Nilmini Wickramasinghe, Dr Jessica Rivera Villicana, A/Prof Carsten Rudolph, Mr Fernando Escorcia, Dr Gnana Bharathy
NeoProducts Pty Ltd, Cancer Council Victoria Grant - Research, Black Dog Institute, Uniting NSW.ACT, Health Metrics, Dementia Australia (Alzheimer's Australia) Vic Inc, Interrelate Limited, Uniting AgeWell, goAct, Aged Care & Housing Group Inc, Unisono Pty Ltd
- 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, 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, 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: $1,050,000
Scale Facilitation AI Colab.
Prof Svetha Venkatesh, Prof Sunil Gupta, A/Prof Santu Rana, A/Prof Truyen Tran, A/Prof Shannon Ryan, Prof Kon Mouzakis, Prof Rajesh Vasa, Dr Scott Barnett, Dr Anj Simmons, Dr Rena Logothetis
Sanitex Global Pty Ltd
- 2023: $500,000
Supervisions
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
Thao Minh Le
Thesis entitled: Deep Neural Networks for Visual Reasoning
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
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
Trang Thi Minh Pham
Thesis entitled: Recurrent Neural Networks for Structured Data
Doctor of Philosophy (Information Technology), School of Information Technology
Shivapratap Gopakumar
Thesis entitled: Machine Learning in Healthcare: An Investigation into Model Stability
Doctor of Philosophy (Information Technology), School of Information Technology
Minh Tri Nguyen
Thesis entitled: Machine Learning for Drug-Target Interaction Prediction
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
Tang Thanh Nguyen
Thesis entitled: On Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical Efficiency
Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins
Xin Zhang
Thesis entitled: Sparse representation for face images
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