Research interests

  • Pattern recognition in streaming data (Anomaly detection, clustering, Internet of Things, Ambient Intellligence, Cybersecurity)


Masud Moshtaghi received his B.Sc. and M.Sc in software engineering (Tehran University) and a Ph.D in computer science (The University of Melbourne). His main area of research is in developing new machine learning techniques to solve emerging challenges in different domains including network security, environmental monitoring and aged care.

Recent publications

  1. Moshtaghi M, Monazam Erfani S, Leckie C, Bezdek JC. Exponentially Weighted Ellipsoidal Model for Anomaly Detection. International Journal of Intelligent Systems. John Wiley & Sons. 2017.
  2. Moshtaghi M, Leckie C, Karunasekera S. A framework for distributed data analysis for IoT. Internet of Things: Principles and Paradigms. 2016.
  3. Kowsar Y, Moshtaghi M, Velloso E, Kulik L, Leckie C. Detecting unseen anomalies in weight training exercises. 28th Australian Conference on Computer-Human Interaction (OzCHI). 2016.
  4. Zameni M, Moshtaghi M, Leckie C. Efficient Query Processing on Road Traffic Network. 2016 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATION WORKSHOPS (PERCOM WORKSHOPS). IEEE. 2016.
  5. Monazam Erfani S, Baktashmotlagh M, Moshtaghi M, Nguyen V, Leckie C, Bailey J, Kotagiri R. Robust domain generalisation by enforcing distribution invariance. 25th International Joint Conference on Artificial Intelligence (IJCAI). 2016, Vol. 2016-January.
  6. Bezdek J, Moshtaghi M, Runkler T, Leckie C. The Generalized C Index for Internal Fuzzy Cluster Validity. IEEE TRANSACTIONS ON FUZZY SYSTEMS. IEE Institute of Electronic Engineers. 2016, Vol. 24, Issue 6.
  7. Moshtaghi M, Zukerman I. A Utility Model for Tailoring Sensor Networks to Users. 23rd International Conference on User Modeling, Adaptation, and Personalization (UMAP). Springer Verlag. 2015, Vol. 9146. Editors: Ricci F, Bontcheva K, Conlan O, Lawless S.
  8. Zukerman I, Kim SN, Kleinbauer T, Moshtaghi M. Employing distance-based semantics to interpret spoken referring expressions. COMPUTER SPEECH AND LANGUAGE. Academic Press - Elsevier Science. 2015, Vol. 34, Issue 1.
  9. Moshtaghi M, Bezdek JC, Leckie C, Karunasekera S, Palaniswami M. Evolving Fuzzy Rules for Anomaly Detection in Data Streams. IEEE TRANSACTIONS ON FUZZY SYSTEMS. IEE Institute of Electronic Engineers. 2015, Vol. 23, Issue 3.
  10. Doan M, Rajasegarar S, Salehi M, Moshtaghi M, Leckie C. Profiling pedestrian distribution and anomaly detection in a dynamic environment. International Conference on Information and Knowledge Management, Proceedings. 2015, Vol. 19-23-Oct-2015.
  11. Moshtaghi M, Zukerman I, Russell RA. Statistical models for unobtrusively detecting abnormal periods of inactivity in older adults. USER MODELING AND USER-ADAPTED INTERACTION. Springer. 2015, Vol. 25, Issue 3.
  12. Kim SN, Zukerman I, Kleinbauer T, Moshtaghi M. A Comparative Study of Weighting Schemes for the Interpretation of Spoken Referring Expressions. Australasian Language Technology Association Workshop. 2014.
  13. Salehi M, Leckie C, Moshtaghi M, Vaithianathan T. A Relevance Weighted Ensemble Model for Anomaly Detection in Switching Data Streams. 18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD). Springer International Publishing. 2014, Vol. 8443.
  14. Moshtaghi M, Leckie C, Karunasekera S, Rajasegarar S. An adaptive elliptical anomaly detection model for wireless sensor networks. Computer Networks. Elsevier Science. 2014, Vol. 64.
  15. Rajasegarar S, Gluhak A, Imran MA, Nati M, Moshtaghi M, Leckie C, Palaniswami M. Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks. Pattern Recognition. Pergamon-Elsevier Science. 2014, Vol. 47, Issue 9.

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile