Home page for Khoa Nguyen

Khoa Nguyen PhD

Team Leader | Senior Research Scientist

Data61, CSIRO

Australian Technology Park

Level 5, 13 Garden Street

Eveleigh NSW 2015, Australia

Tel: +61 2 9490 5576

Email: khoa.nguyen AT data61.csiro.au

Dr. Khoa Nguyen is a research team leader and a senior research scientist in Analytics and Decision Sciences Program at Data61 (CSIRO). He is leading a research team working on predictive analytics for asset management (including Structural Health Monitoring) and energy demand forecasting using machine learning techniques. He holds a PhD in computer science from the University of Sydney.

His research interests are machine learning and data mining with a focus on anomaly detection, tensor decomposition, online learning, spectral graph embedding, clustering and predictive modeling. He has been driving several industrial projects that investigate machine learning for real-world problems such as damage detection in civil structures (including the iconic Sydney Harbour Bridge), pothole detection in road pavements, fault detection and diagnosis for HVAC systems and energy demand forecasting.

PUBLICATIONS (Google scholar)

  • H. Luo, Z. Bao, G. Cong, J.S. Culpepper, N.L.D. Khoa, “Let Trajectories Speak Out the Traffic Bottlenecks”, accepted in ACM Transactions on Intelligent Systems and Technology (TIST), 2021. (IF: 2.86)

  • F. Cameron-Muller, D. Weeraddana R. Chalapathy, N.L.D. Khoa, “Dual-Stage Bayesian Sequence to Sequence Embeddings for Energy Demand Forecasting”, accepted in Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2021). (ERA rank A, acceptance rate 20%)

  • D.H. Tran, Q.Z. Sheng, W.E. Zhang, S. Hamad, N.L.D. Khoa, N.H. Tran, “Challenges and Opportunities on Deep Conversational Recommender Systems”, accepted in IEEE Computer, 2021. (IF: 4.42)

  • D.H. Tran, Q.Z. Sheng, W.E. Zhang, A. Aljubairy, M. Zaib, S.A. Hamad, N.H. Tran, N.L.D. Khoa, “HeteGraph: Graph Learning in Recommender Systems via Graph Convolutional Networks”, Neural Computing and Applications (NCAA), 2021. (IF: 4.77)

  • D. Weeraddana, N.L.D. Khoa, L. O'Neil, W. Wang and C. Cai, “Energy consumption forecasting using a stacked nonparametric Bayesian approach”, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD 2020), 2020. (ERA rank A, acceptance rate 28%)

  • R. Chalapathy, N.L.D. Khoa, and S. Chawla, “Robust Deep Learning Methods for Anomaly Detection”, the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2020), pp. 3507–3508, USA, 2020. (ERA rank A*)

  • D.H. Tran, A. Aljubairy, M. Zaib, Q.Z. Sheng, W.E. Zhang, N.H. Tran, N.L.D. Khoa, “HeteGraph: A Convolutional Framework for Graph Learning in Recommender Systems”, the 2020 International Joint Conference on Neural Networks (IJCNN 2020), pp. 1-8, UK. (ERA rank A)

  • S.T.K. Lin, Y. Lu, M. Makki Alamdari, N.L.D. Khoa, “Field Test Investigations for Condition Monitoring of a Concrete Culvert Bridge Using Vibration Responses”, Structural Control and Health Monitoring (SCHM), 2020. (IF: 3.50)

  • D. Weeraddana, H. Hapuarachchi, L. Kumarapperuma, N.L.D. Khoa and C. Cai, “Long-term water pipe condition assessment: semiparametric model with Gaussian process using survival analysis”, the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), pp 487-499, Singapore, 2020. (ERA rank A, acceptance rate 21%)

  • Lin STK; Lu Y; Alamdari MM; N.L.D. Khoa, “Concrete culvert bridge condition monitoring using acceleration responses: A case study”, Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring (IWSHM), USA, pp. 227-233, 2019.

  • H. Tian, N.L.D. Khoa, A. Anaissi, Y. Wang, F. Chen, “Concept Drift Adaption for Online Anomaly Detection in Structural Health Monitoring”, the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), pp. 2813–2821, China, 2019. (ERA rank A)

  • P. Cheema, N.L.D. Khoa, M. Kidd, G.A. Vio, “A Tensor-based Structural Health Monitoring Approach for Aeroservoelastic Systems”, AIAA Scitech 2019 Forum, 2019.

  • N.L.D. Khoa, H. Tian, Y. Wang, F. Chen, “Online Data Fusion Using Incremental Tensor Learning”, the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), pp. 357-369, China, 2019. (ERA rank A, acceptance rate 24.7%)

  • A. Anaissi, N.L.D. Khoa, T. Rakotoarivelo, M. Makki Alamdari, Y. Wang, “Smart Pothole Detection System Using Vehicle Mounted Sensors and Machine Learning”, Journal of Civil Structural Health Monitoring (JCSHM), Springer, pp. 1-12, 2019.

  • M. Makki Alamdari, A. Anaissi, N.L.D. Khoa, S. Mustapha, “Frequency Domain Decomposition Based Multi-Sensor Data Fusion for Assessment of Progressive Damage in Structures”, Structural Control and Health Monitoring (SCHM), vol. 26, issue 2, 2019.

  • Dai Hoang Tran, Zawar Hussain, Wei Zhang, N.L.D. Khoa, Nguyen H. Tran and Quan Z. Sheng, “Deep Autoencoder for Recommender Systems: Parameter Influence Analysis”, the 29th Australasian Conference on Information Systems (ACIS 2018), 2018.

  • N.L.D. Khoa, Y. Wang, S. Chawla, “Incremental Commute Time and its Online Applications”, Pattern Recognition (PR), vol. 88, pp. 101-112, 2018. (ERA rank A*)

  • A. Anaissi, N.L.D. Khoa, T. Rakotoarivelo, M. Makki Alamdari, Y. Wang, “Adaptive Online One-Class Support Vector Machines with Applications in Structural Health Monitoring”, in ACM Transactions on Intelligent Systems and Technology (TIST), vol. 9, issue 6, 2018.

  • M. Makki Alamdari, N.L.D. Khoa, Y. Wang, B. Samali, X. Zhu, "A Multi-Way Data Analysis Approach for Structural Health Monitoring of a Cable-Stayed Bridge", in the International Journal of Structural Health Monitoring (SHMIJ), 2018. (ERA rank A)

  • A. Anaissi, N.L.D. Khoa, Y. Wang, “Automated Parameter Tuning in One-Class Support Vector Machine: an Application for Damage Detection”, in the International Journal of Data Science and Analytics, 2018.

  • N.L.D. Khoa, M. Makki Alamdari, T. Rakotoarivelo, A. Anaissi, Y. Wang, “Structural Health Monitoring Using Machine Learning Techniques and Domain Knowledge Based Features”, a book chapter in J. Zhou, F. Chen (eds) “Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent”, pp. 409-435, Springer, 2018.

  • A. Anaissi, M. Makki Alamdari, T. Rakotoarivelo, N.L.D. Khoa, “A Tensor-Based Structural Damage Identification and Severity Assessment”, Sensors, vol 18 (1), 2018.

  • P. Runcie, M. Makki Alamdari, T. Pitman, N.L.D. Khoa, “Case Studies: Structural Health Monitoring for Real-Time Asset Management of Small Bridges”, in the 8th Australian Small Bridges Conference (ABC), 2017.

  • A. Diez-Olivan, J. A. Pagan, N.L.D. Khoa, R. Sanz, B. Sierra, “Kernel-based support vector machines for automated health status assessment in monitoring sensor data”, in the International Journal of Advanced Manufacturing Technology, Springer, 2017.

  • M. Makki Alamdari, N.L.D. Khoa, T. Rakotoarivelo, H. Kalhori, J. Li, "Structural Health Monitoring in the Sydney Harbour Bridge Using Spectral Moments", in the 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-8), 2017. (ERA rank A)

  • N.L.D. Khoa, A. Anaissi, Y. Wang, “Smart Infrastructure Maintenance Using Incremental Tensor Analysis”, in the 26th ACM International Conference on Information and Knowledge Management (CIKM 2017), pp. 959-967, Singapore, 2017. (ERA rank A)

  • A. Anaissi, N.L.D. Khoa, T. Rakotoarivelo, M. Makki Alamdari, Y. Wang, “Self-Advised Incremental One-Class Support Vector Machines: an Application in Structural Health Monitoring”, in the International Conference on Neural Information Processing (ICONIP 2017), pp. 484-496, China, 2017. (ERA rank A)

  • A. Anaissi, N.L.D. Khoa, Y. Wang, F. Chen, A. Braytee, P. Runcie, “Adaptive One-Class Support Vector Machine for Damage Detection in Structural Health Monitoring”, in the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017), pp. 42-57, Korea, 2017. (ERA rank A, acceptance rate 28.2%)

  • M. Makki Alamdari, T. Rakotoarivelo, N.L.D. Khoa, “A Spectral-Based Clustering for Structural Health Monitoring of the Sydney Harbour Bridge”, in Journal of Mechanical Systems and Signal Processing (MSSP), vol. 87, pp. 384-400, 2017. (ERA rank A*)

  • P. Cheema, N.L.D. Khoa, M. Makki Alamdari, W. Liu, Y. Wang, F. Chen, P. Runcie, “On Structural Health Monitoring Using Tensor Analysis and Support Vector Machine with Artificial Negative Data”, in the 25th ACM International on Conference on Information and Knowledge Management (CIKM 2016), pp. 1813-1822, Indianapolis, USA, 2016. (ERA rank A, acceptance rate 19.8%)

  • N.L.D. Khoa, S. Chawla, “Incremental Commute Time Using Random Walks and Online Anomaly Detection”, in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD 2016), pp. 49-64, Riva del Garda, Italy, 2016. (ERA rank A, acceptance rate 28%)

  • N.L.D. Khoa, M. Makki Alamdari, P. Runcie, V.V. Nguyen, “Damage Identification on Bridges Using Ambient Vibration Testing”, the Fifth International Symposium on Life-Cycle Civil Engineering (IALCCE 2016), Delft, Netherlands, 2016.

  • A.D. Oliván, N.L.D. Khoa, M. Makki Alamdari, Y. Wang, F. Chen, P. Runcie, “A Clustering Approach for Structural Health Monitoring on Bridges”, Journal of Civil Structural Health Monitoring (JCSHM), Vol 6, Issue 3, pp. 429-445, Springer, 2016.

  • Mustapha, S., Hu, Y., Khoa, N.L.D., Makki Alamdari, M., Runcie, P., Dackermann, U., Nguyen, V.V., Li, J. and Ye, L., “Pattern Recognition Based on Time Series Analysis Using Vibration Data for Structural Health Monitoring in Civil Structures”, Electronic Journal Of Structural Engineering, Vol 14, Issue 1, pp. 106-115, 2015.

  • M. Makki Alamdari, N.L.D. Khoa, P. Runcie, S. Mustapha, U. Dackermann, V.V. Nguyen, L. Jianchun, X. Gu, “Application Of Unsupervised Support Vector Machine For Condition Assessment Of Concrete Structures”, in a mini Symposium in Second International Conference on Performance-based and Lifecycle Structural Engineering (PLSE 2015), Brisbane, Australia, 2015.

  • N.L.D. Khoa, B. Zhang, Y. Wang, W. Liu, F. Chen, S. Mustapha and P. Runcie, “On Damage Identification in Civil Structures Using Tensor Analysis”, in the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015), Ho Chi Minh, Vietnam, 2015. (ERA rank A, acceptance rate 7% for full paper)

  • S. Mustapha, Y. Hu, U. Dackermann, V.V. Nguyen, N.L.D. Khoa, P. Runcie, J. Li, L. Ye, “Structural health monitoring in civil structures based on the time series analysis”, in the 9th Austroads Bridge Conference (ABC 2014), Sydney, 2014.

  • N.L.D. Khoa, B. Zhang, Y. Wang, F. Chen and S. Mustapha, “Robust Dimensionality Reduction and Damage Detection Approaches in Structural Health Monitoring”, in International Journal of Structural Health Monitoring (SHMIJ), SAGE Publications, vol. 13, issue 4, pp. 406-417, 2014. (ERA rank A)

  • N.L.D. Khoa and S. Chawla, “A Scalable Approach to Spectral Clustering with SDD Solvers,” in Journal of Intelligent Information Systems (JIIS, Springer), 2013.

  • S. Tamura, B. Zhang, Y. Wang, F. Chen, N.L.D. Khoa, “Supervised and unsupervised machine learning approaches for bridge damage prediction,” in International Workshop in Structural Health Monitoring (IWSHM), pp. 182-189, 2013.

  • N.L.D. Khoa, “Large Scale Anomaly Detection and Clustering Using Random Walks,” PhD thesis, University of Sydney, Australia, 2012.

  • N.L.D. Khoa and S. Chawla, “Large Scale Spectral Clustering Using Resistance Distance and Spielman-Teng Solvers,” in The 15th International Conference on Discovery Science (DS 2012), Springer, pp. 7–21, 2012.

  • N.L.D. Khoa and S. Chawla, “Online Anomaly Detection Systems Using Incremental Commute Time,” in CoRR abs/1107.3894, 2011.

  • N.L.D. Khoa, T. Babaie, S. Chawla, and Z. Zaidi, “Network Anomaly Detection Using a Commute Distance Based Approach,” in The International Workshop on Domain Driven Data Mining (DDDM 2010) joint with The 10th IEEE International Conference on Data Mining (ICDM 2010), pp. 943-950, 2010.

  • N.L.D. Khoa and S. Chawla, “Robust Outlier Detection Using Commute Time and Eigenspace Embedding,” in The 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2010), Springer, pp. 422–434, 2010. (ERA rank A, acceptance rate 10% for full paper)

  • N.L.D. Khoa and S. Chawla, “Unifying Global and Local Outlier Detection Using Commute Time Distance,” in Technical Report 638, School of Information Technologies, University of Sydney, 2009.

  • N.L.D. Khoa, “Stock Price Forecasting Using Computational Intelligence Approaches,” Master thesis, Ritsumeikan University, Japan, 2007.

  • N.L.D. Khoa, K. Sakakibara, and I. Nishikawa, “Stock Price Forecasting Using Neural Network Ensemble Techniques,” in The 6th IEEE International Conference on Research, Innovation and Vision for the Future - in Computing & Communications Technologies (RIVF 2008), École Nationale Supérieure des Telecommunications, pp. 86-91, 2008.

  • N.L.D. Khoa, M. Noishiki, K. Sakakibara, and I. Nishikawa, “Stock Price Forecasting Using Neural Networks with Inputs Selected by Genetic Algorithm,” in The 5th International Conference on Research, Innovation, and Vision for the Future - in Information & Communications Technologies (RIVF 2007), Studia Informatica Universalis, pp. 95-100, 2007.

  • N.L.D. Khoa, K. Sakakibara, and I. Nishikawa, “Stock Price Forecasting Using Back Propagation Neural Networks with Time and Profit Based Adjusted Weight Factors,” in The SICE-ICASE International Joint Conference 2006, pp. 5484-5488, 2006.

  • D.N. Hieu, N.L.D. Khoa, T.N.H. Huy, and N.T. Truc, “Applying Data Mining Techniques To Build an Intelligent Search Engine,” in The 5th Ho Chi Minh city University of Technology Young Researcher Conference, 2005.

  • N.L.D. Khoa, “Translating SQL (Structured Query Language) into Relational Algebra,” Bachelor thesis, Ho Chi Minh City University of Technology, Vietnam, 2002 (in Vietnamese).

CODES

  • approx_resistance.m: generate a matrix from a graph which we can quickly calculate the approximate effective resistances between any two nodes in the graph. This makes use of a near-linear time SDD solver.

Last update on 27/08/2019