Profile image of Truyen Tran

Dr Truyen Tran

STAFF PROFILE

Position

Lecturer In Computer Networks

Faculty

Faculty of Sci Eng & Built Env

Department

School of Info Technology

Campus

Geelong Waurn Ponds Campus

Qualifications

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

Biography summary

Truyen Tran is a lecturer at Deakin University. He is member of Centre for Pattern Recognition and Data Analytics (PRaDA) where he leads the work on deep learning, healthcare analytics and software analytics. 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 PAKDD (2015), Distinguished Paper at ACM SIGSOFT (2015), and Deakin Thought Leader (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. He spent 3 years at Curtin University before moving to Deakin in 2012.

Research interests

Artificial intelligence

Data science

Biomedical informatics

Teaching interests

Machine learning

Data science

Healthcare analytics

Knowledge areas

Machine learning

Data mining

Artificial intelligence

Healthcare analytics

Biomedical informatics

Recommender systems

Data science

Awards

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

Filter by

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

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

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

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, C1

journal

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

T Pham, T Tran, D Phung, S Venkatesh

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

journal

Predicting delivery capability in iterative software development

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

(2017), pp. 1-22, IEEE transactions on software engineering, C1

journal

Column networks for collective classification

T Pham, T Tran, Q Phung, S Venkatesh

(2017), pp. 2485-2491, AAAI-17: Proceedings of the 31st Artificial Intelligence AAAI Conference, Palo Alto, Calif., E1

conference
2016

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

T Tran, Q Phung, S Venkatesh

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

journal

Graph-induced restricted Boltzmann machines for document modeling

T Nguyen, T Tran, Q Phung, S Venkatesh

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

journal

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

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, New York, N.Y., B1

chapter

Neural choice by elimination via highway networks

T Tran, D Phung, S Venkatesh

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

chapter

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, C1

journal

Collaborative filtering via sparse Markov random fields

T Tran, D Phung, S Venkatesh

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

journal

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, pp. 1-10, JMIR mental health, C1

journal

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, C1

journal

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

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

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

journal

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

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

(2016), pp. 1-13, MLHC 2016 : Proceedings on Conference on Machine Learning in Healthcare, [Los Angeles, Calif.], 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, New York, N.Y., E1

conference

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

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

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

journal

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, Cham, Switzerland, 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, Cham, Switzerland, 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, Piscataway, N.J., 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, Piscataway, N.J., E1

conference
2015

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, C1

journal

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

T Tran, T Nguyen, Q Phung, S Venkatesh

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

journal

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

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

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

journal

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

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

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

journal

Stabilizing high-dimensional prediction models using feature graphs.

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

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

journal

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, Palo Alto, Calif., E1

conference

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), Berlin, Germany, B1

chapter

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, Piscataway, N.J., 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, Piscataway, N.J., 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, Piscataway, N.J., E1

conference
2014

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, C1

journal

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

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

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

journal

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

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

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

journal

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, C1

journal

iPoll: Automatic polling using online search

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

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

chapter

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, C1

journal

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
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, Berlin, Germany, 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, Piscataway, N.J., 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, New York, N.Y., 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, [The Conference], 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, Piscataway, N.J., 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, Melbourne, Vic., 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, Philadelphia, Pa., 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, New York, N. Y., 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, New York, N. Y., 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, Berlin, Germany, 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, Arlington, Va., 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, Berlin, Germany, 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, Berlin, Germany, 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, Red Hook, N. Y., 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, Piscataway, N.J., E1-1

conference
2005

Boosted Markov networks for activity recognition

Truyen Tran, Hung Bui, Svetha Venkatesh

(2005), nternational Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP2005), 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

Grants

Industry and Other Funding

Predicting software components containing safety hazards using deep learning

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

  • 2017: $52,397

Supervisions

Principal Supervisor
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
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