Tich Phuoc Tran is a research fellow in the Centre for Quantum Computation and Intelligent System (QCIS) at the University of Technology, Sydney (UTS). His research interests include network security, computer vision, knowledge discovery and data mining. Tran has a PhD in Computing Sciences from UTS and won the University Medal in 2006. Contact him at Tich.Tran@uts.edu.au. Dr. Tran has a very strong research background in the field of Data Mining and Machine Learning with applications in Network Security Detection, Image Processing and Face Recognition. He has been active in theoretical development of radial basis function networks, ensemble learning and support vector machines for nonlinear modeling and prediction. He has proposed an innovative Machine Learning algorithm called Boosted Subspace Probabilistic Neural Network (BSPNN) applied for Security Detection (Intrusion Detection Systems - IDS and Anti-Spam Filtering). This algorithm has empirically demonstrated detection rate with low false alarm rate and affordable computation expense. He has authored more than 15 publications in premier conferences and international journals. He has been an active reviewer for international journals, chair and local organizer for IT conferences. He has co-supervised 2 master-by-research students and 1 PhD candidate and has been teaching undergraduate and postgraduate subjects (Data Mining, Network Security, Web Technologies, Software Engineering, and Software Quality Process) since 2006. In addition to his research strength, Dr. Tran also has extensive industry experience in commercialization of research projects and software development. The followings are topics of his research interest. Data Mining Artificial Neural Networks and Support Vector Machine Ensemble Learning Multi-expert classification and voting techniques Web based support systems and Multi-objective decision making Intrusion Detection System and Intrusion Prevention System Global Positioning System (GPS) Image Processing and Face recognition Behavior Informatics and Educational Data Mining