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
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, Piscataway, N.J., 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, 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
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, Piscataway, N.J., 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
Energy-based anomaly detection for mixed data
K Do, T Tran, S Venkatesh
(2018), Vol. 57, pp. 413-435, Knowledge and information systems, London, Eng., C1
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
Variational Memory Encoder-Decoder
Hung Le, Truyen Tran, Thin Nguyen, Svetha Venkatesh
(2018), Vol. 31, Advanced in Neural Information Processing Systems 31 (NIPS 2018), Montreal, Canada, 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
Committee machine that votes for similarity between materials
Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Tuan-Dung Ho, Truyen Tran, Keisuke Takahashi, Hieu-Chi Dam
(2018), Vol. 5, pp. 830-840, IUCrJ, Chester, Eng., C1
Automatic feature learning for predicting vulnerable software components
H Dam, T Tran, T Pham, S Ng, J Grundy, A Ghose
(2018), IEEE transactions on software engineering, Piscataway, N.J., C1
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
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, New York, N.Y., 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, New York, N.Y., 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, Amsterdam, The Netherlands, 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, Piscataway, N.J., 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, Amsterdam, The Netherlands, 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
T Tran, Q Phung, S Venkatesh
(2016), Vol. 47, pp. 157-188, Knowledge and information systems, New York, N.Y., C1
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
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
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
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
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
C Karmakar, W Luo, T Tran, M Berk, S Venkatesh
(2016), Vol. 3, pp. 1-10, JMIR mental health, Toronto, Ont., 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, Amsterdam, The Netherlands, 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. 1-16, JMIR medical informatics, Toronto, Ont., C1
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
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
Outlier detection on mixed-type data: An energy-based approach
K Do, T Tran, D Phung, S Venkatesh
(2016), Vol. 10086 LNAI, pp. 111-125, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 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
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
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
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
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
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
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
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), Vol. 20, pp. 213-229, Journal of Machine Learning Research, 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'10 - Proceedings of the 2nd ACM SIGMM Workshop on Social Media, Co-located with ACM Multimedia 2010, 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, Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009, 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
Truyen Tran, Hung Bui, Svetha Venkatesh
(2005), nternational Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP2005), Australia, 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, Prof Nilmini Wickramasinghe, 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 Jeffrey Fiebig, Mr John Fouyaxis, Dr Kit Huckvale, Prof John Grundy, Nicole Cockayne, Dr Leonard Hoon, David Varley, Dr Tanya Petrovich, Mr Voytek Szapirko, Dr Hermant Ghayvat, Dr Anju Kissoon Curumsing, Matthew Macfarlane, Dr Scott Barnett, Prof Deborah Parker, Dr Tom McClean
ARC Industrial Transformation Research Hubs
- 2019: $361,888
- 2018: $449,083
- 2017: $601,698
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
- 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, Prof Nilmini Wickramasinghe, 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 Jeffrey Fiebig, Mr John Fouyaxis, Dr Kit Huckvale, Prof John Grundy, Nicole Cockayne, Dr Leonard Hoon, David Varley, Dr Tanya Petrovich, Mr Voytek Szapirko, Dr Hermant Ghayvat, Dr Anju Kissoon Curumsing, Matthew Macfarlane, Dr Scott Barnett, Prof Deborah Parker, Dr Tom McClean
- 2019: $328,745
Supervisions
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
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