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Machine learning for a better world

Machine learning is out to the world. Much of what previously programmed manually can now be learned through examples. 

Areas:

» Materials science: Exploring faster ways to compute materials properties and generate new kinds of materials.
» Computational chemistry: Exploring the molecular space and interactions.
» Quantum machine learning: Exploring the interplay between quantum computing and machine learning.
» Scientific knowledge representation and reasoning: Exploring the knowledge representation and reasoning in science, esp. how reasoning is shaping science and how to (semi-)automate the process. Studying NLP problems in scientific literature, including information extraction, knowledge base construction, creation and difussion of ideas.
» Explainable AI: Machine learning is often criticised as black-box which is perhaps the most critical point that blocks wide adoption. This research aims to improve the situation by creating a transparent-box.
» Recomemender systems: The goal of a recommender system is to deliver right services to right users. Most existing work is rather ad-hoc and ignores complex nature of the data. Research topics include modeling choices, discovering hidden patterns, incorporating contexts and side-information, social networks, multiple-domains, product hierarchies, as well as correlations between actors and items.
» Automated software engineering: Software is eating the world. Automated software engineering helps improving software development with better risk estimation, lower cost and higher code quality.
» Anomaly detection: Here we learn to detect unknown unknowns (e.g., outliers and anomalies).
» Process modeling: Processes are logics that underpin businesses, factories and operations. ML offers new ways for discovery of actionable insights from event log, as well as process prediction, optimization and planning.

Talks/Tutorials

Climate change: Challenges and AI-driven solutions, @Swinburne Vietnam, Hanoi,  Vietnam, Dec 2019.
AI for matters, Phenikaa University, Hanoi, Vietnam, Jan 2019.

Publications

Materials science:

Hybrid generative-discriminative models for inverse materials design, Phuoc Nguyen, Truyen Tran, Sunil Gupta, Svetha Venkatesh. arXiv preprint arXiv:1811.06060. Extention of:
Incomplete conditional density estimation for fast materials discovery, Phuoc Nguyen, Truyen Tran, Sunil Gupta, Svetha Venkatesh. SDM'19.
Incomplete conditional density estimation for fast materials discovery, Phuoc Nguyen, Truyen Tran, Sunil Gupta, Svetha Venkatesh. SDM'19.
Committee machine that votes for similarity between materials; Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Tuan-Dung Ho, Truyen Tran, Keisuke Takahashi and Hieu-Chi Dam. IUCrJ, 2018 Nov 1; 5(Pt 6): 830–840.

Computational chemistry:

Graph transformation policy network for chemical reaction prediction, Kien Do, Truyen Tran, Svetha Venkatesh, KDD'19.
Relational dynamic memory networks, Trang Pham, Truyen Tran, Svetha Venkatesh, arXiv preprint arXiv:1808.04247. Extension of:
Graph memory networks for molecular activity prediction, Trang Pham, Truyen Tran, Svetha Venkatesh, ICPR'18.
Neural reasoning for chemical-chemical interaction. Trang Pham, Truyen Tran, Svetha Venkatesh, NIPS 2018 Workshop on Machine Learning for Molecules and Materials.
Attentional multilabel learning over graphs: A message passing approach, K Do, T Tran, T Nguyen, S Venkatesh, Machine Learning, 2019.
Graph memory networks for molecular activity prediction, Trang Pham, Truyen Tran, Svetha Venkatesh, ICPR'18
Prelim version appears at NIPS Workshop on Deep learning for physical sciences, 2017
Extending: Graph classification via deep learning with virtual nodes Trang Pham, Truyen Tran, Hoa Dam, Svetha Venkatesh, Third Representation Learning for Graphs Workshop (ReLiG 2017).

Explainable AI

Explainable Software Analytics, HK Dam, T Tran, A Ghose, ICSE 2018 New Ideas and Emerging Results
Deepr: A Convolutional Net for Medical Records, Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh, IEEE Journal of Health and Biomedical Informatics, 2017, Doi: 10.1109/JBHI.2016.2633963.
Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional DataTruyen Tran, Wei Luo, Dinh Phung, Jonathan Morris, Kristen Rickard, Svetha Venkatesh, Conference on Machine Learning in Healthcare, LA, USA Aug 2016.
Stabilizing Linear Prediction Models using Autoencoder, Shivapratap Gopakumara, Truyen Tran, Dinh Phung, Svetha Venkatesh, International Conference on Advanced Data Mining and Applications (ADMA 2016).
Stabilizing Sparse Cox Model using Statistic and Semantic Structures in Electronic Medical Records. Shivapratap Gopakumar, Tu Dinh Nguyen, Truyen Tran, Dinh Phung, and Svetha Venkatesh, PAKDD'15, HCM City, Vietnam, May 2015.
Stabilizing high-dimensional prediction models using feature graphs, Shivapratap Gopakumar, Truyen Tran, Tu Dinh Nguyen, Dinh Phung, and Svetha Venkatesh, IEEE Journal of Biomedical and Health Informatics, 2014 DOI:10.1109/JBHI.2014.2353031S 
Stabilizing sparse Cox model using clinical structures in electronic medical records, S Gopakumar, Truyen Tran, D Phung, S Venkatesh, 2nd International Workshop on Pattern Recognition for Healthcare Analytics, August 2014
HealthMap: A visual platform for patient suicide risk review, S Rana, W Luo, Truyen Tran, D Phung, S Venkatesh, R Harvey, HISA Big Data, Melbourne April, 2014
Stabilized sparse ordinal regression for medical risk stratification, Truyen Tran, Dinh Phung, Wei Luo, and Svetha Venkatesh, Knowledge and Information Systems, 2014, DOI: 10.1007/s10115-014-0740-4.
Recommender systems

Preference Relation-based Markov Random Fields in Recommender Systems, Shaowu Liu, Gang Li,Truyen Tran, Jiang Yuan, Machine Learning, April 2017, Volume 106, Issue 4, pp 523–546. This is an extension of the ACML'15 paper.
Neural Choice by Elimination via Highway Networks, Truyen Tran, Dinh Phung and Svetha Venkatesh,  PAKDD workshop on Biologically Inspired Techniques for Data Mining (BDM'16), April 19-22 2016, Auckland, NZ.
Collaborative filtering via sparse Markov random fields, Truyen Tran, Dinh Phung, Svetha Venkatesh, Information Sciences, doi:10.1016/j.ins.2016.06.027.
Preference Relation-based Markov Random Fields, Shaowu Liu, Gang Li,Truyen Tran, Jiang Yuan, ACML'15, November 20-22, 2015, Hong Kong.
Modelling Human Preferences for Ranking and Collaborative Filtering: A Probabilistic Ordered Partition ApproachTruyen Tran, D. Phung, and S. Venkatesh (extension of the SDM'11 paper). Knowledge and Information Systems . May 2015
Ordinal random fields for recommender systems, Shaowu Liu, Truyen Tran, Gang Li, Yuan Jiang, ACML'14, Nha Trang, Vietnam, Nov 2014.
Latent patient profile modelling and applications with Mixed-Variate Restricted Boltzmann Machine, Tu D. Nguyen, Truyen Tran, D. Phung, and S. Venkatesh,  In Proc. of 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’13), Gold Coast, Australia, April 2013.
Thurstonian Boltzmann machines: Learning from multiple inequalities, Truyen Tran, D. Phung, and S. Venkatesh, In Proc. of 30th International Conference in Machine Learning (ICML’13), Atlanta, USA, June, 2013.
Learning from Ordered Sets and Applications in Collaborative Ranking, Truyen Tran, Dinh Phung and Svetha Venkatesh, in Proc. of. the 4th Asian Conference on Machine Learning (ACML2012), Singapore, Nov 2012.
Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis, Truyen Tran, Dinh Phung and Svetha Venkatesh, in Proc. of. the 4th Asian Conference on Machine Learning (ACML2012), Singapore, Nov 2012.
A Sequential Decision Approach to Ordinal Preferences in Recommender Systems, Truyen Tran, Dinh Phung, Svetha Venkatesh, in Proc. of 25-th Conference on Artificial Intelligence (AAAI-12), Toronto, Canada, July 2012.
Mixed-Variate Restricted Boltzmann Machines, Truyen Tran, Dinh Phung and Svetha Venkatesh, in Proc. of. the 3rd Asian Conference on Machine Learning (ACML2011), Taoyuan, Taiwan, Nov 2011.
Probabilistic Models over Ordered Partitions with Applications in Document Ranking and Collaborative Filtering
T. Truyen, D. Phung, and S. Venkatesh, in Proc. of SIAM Int. Conf. on Data Mining (SDM11), April, Arizona, USA, 2011.
Ordinal Boltzmann Machines for Collaborative Filtering. Truyen Tran, Dinh Q. Phung and Svetha Venkatesh. In Proc. of 25th Conference on Uncertainty in Artificial Intelligence, June, 2009, Montreal, Canada. Runner-up for the best paper award.
Preference Networks: probabilistic models for recommendation systems, Truyen Tran, Dinh Q. Phung and Svetha Venkatesh, In Proc. of 6th Australasian Data Mining Conference: AusDM 2007, 3-4 Dec, Gold Coast, Australia. 
Automated software engineering

Automatically recommending components for issue reports using deep learning, M Choetkiertikul, HK Dam, T Tran, T Pham, C Ragkhitwetsagul, A Ghose, Empirical Software Engineering 26 (2), 1-39, 2021.
Towards effective AI-powered agile project management, HK Dam, T Tran, J Grundy, A Ghose, Y Kamei, ICSE'19 New Ideas and Emerging Results.
Lessons learned from using a deep tree-based model for software defect prediction in practice, HK Dam, T Pham, SW Ng, T Tran, J Grundy, A Ghose, T Kim, CJ Kim, MSR'19
Automatic feature learning for predicting vulnerable software components, Hoa Khanh Dam, Truyen Tran, Trang Pham,  Shien Wee Ng, John Grundy, Aditya Ghose, IEEE Transactions on Software Engineering, 2019
Predicting components for issue reports using deep learning with information retrieval, M Choetkiertikul, HK Dam, T Tran, T Pham, A Ghose, International Conference on Software Engineering (ICSE'18) - Poster Track
Explainable Software Analytics, HK Dam, T Tran, A Ghose, ICSE 2018 New Ideas and Emerging Results
A deep learning model for estimating story points, M Choetkiertikul, HK Dam, T Tran, T Pham, A Ghose, T Menzies, IEEE Transactions on Software Engineering, 2018
Graph Classification via Deep Learning with Virtual Nodes Trang Pham, Truyen Tran, Hoa Dam, Svetha Venkatesh, Third Representation Learning for Graphs Workshop (ReLiG 2017).
Predicting delivery capability in iterative software development, Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Aditya Ghose, John Grundy, IEEE Transactions on Software Engineering, DOI: 10.1109/TSE.2017.2693989, April 2017.
Predicting the delay of issues with due dates in software projects, Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Aditya Ghose, Empirical Software Engineering, doi:10.1007/s10664-016-9496-7, Feb 2017
Column Networks for Collective Classification, T Pham, T Tran, D Phung, S Venkatesh, AAAI'17
DeepSoft: A vision for a deep model of software, Hoa Khanh Dam, Truyen Tran, John Grundy and Aditya Ghose, FSE, Vision and Reflections Track, 2016.
A deep language model for software code, Hoa Khanh Dam, Truyen Tran and Trang Pham, FSE Workshop on NL+SE 2016.
Who will answer my question on Stack Overflow?, Morakot Choetkiertikul, Daniel Avery, Hoa Khanh Dam, Truyen Tran and Aditya Ghose, 24th Australasian Software Engineering Conference (ASWEC 2015), Adelaide, Australia, September 28 - October 1, 2015.
Predicting delays in software projects using networked classification, Morakot Choetikertikul, Hoa Khanh Dam, Truyen Tran, Aditya Ghose, 30th IEEE/ACM International Conference on Automated Software Engineering, November 9–13, 2015 Lincoln, Nebraska, USA.
Characterization and prediction of issue-related risks in software projects, Morakot Choetikertikul, Hoa Khanh Dam, Truyen Tran, Aditya Ghose, MSR'15, May 16–17. Florence, Italy. Winner of ACM SIGSOFT Distinguished Paper Award.
Anomaly detection

Unsupervised anomaly detection on temporal multiway data, Duc Nguyen, Phuoc Nguyen, Kien Do, Santu Rana, Sunil Gupta, Truyen Tran, 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (SSCI 2020).
Learning regularity in skeleton trajectories for anomaly detection in videos, Romero Morais, Vuong Le, Budhaditya Saha, Truyen Tran, Moussa Reda Mansour, Svetha Venkatesh, CVPR'19.
Energy-Based Anomaly Detection for Mixed Data, Kien Do, Truyen Tran, Svetha Venkatesh, Knowledge and Information Systems, 2018. 
Multilevel Anomaly Detection for Mixed Data, K Do, T Tran, S Venkatesh, arxiv preprint arxiv 1610.06249.
Outlier Detection on Mixed-Type Data: An Energy-based Approach, K Do, T Tran, D Phung, S Venkatesh, International Conference on Advanced Data Mining and Applications (ADMA 2016). Best student runner-up paper award.
Process modeling

Memory–augmented neural retworks for predictive process analytics, Asjad Khan, Hung Le, Kien Do, Truyen Tran, Aditya Ghose, Hoa Dam, Renuka Sindhgatta, arXiv preprint arXiv:1802.00938.
Forecasting Daily Patient Outflow From a Ward Having no Real-Time Clinical Data, Shivapratap Gopakumara, Truyen Tran, Wei Luo, Dinh Phung, Svetha Venkatesh, JMIR, Vol 4, No 3 (2016): Jul-Sept.