Professor Rao Kotagiri

  • Room: Level: 07 Room: 710
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • Machine Learning, Artificial Intelligence, Data Mining, Big Data Analytics, Information Retrieval, Cloud Computing, Network Security. (Artificial Intelligence, Machine Learning, Data mining, Databases, Information Retrieval)

Personal webpage

http://www.cloudbus.org/rao/

Biography

Current research interests 

Machine Learning and Data mining
Robust Agent Systems
Information Retrieval
Intrusion Detection
Logic Programming and Deductive Databases
Distributed Systems
Bioinformatics and Medical Imaging

Recent publications

  1. Moghaddam SK, Buyya R, Kotagiri R. ACAS: An anomaly-based cause aware auto-scaling framework for clouds. Journal of Parallel and Distributed Computing. Academic Press. 2019, Vol. 126. DOI: 10.1016/j.jpdc.2018.12.002
  2. Sarwar T, Kotagiri R, Zalesky A. Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography?. Magnetic Resonance in Medicine. John Wiley & Sons. 2019, Vol. 81, Issue 2. DOI: 10.1002/mrm.27471
  3. Hashem T, Kulik L, Kotagiri R, Zhang R, Soma SC. Protecting privacy for distance and rank based group nearest neighbor queries. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS. Springer. 2019, Vol. 22, Issue 1. DOI: 10.1007/s11280-018-0570-5
  4. Ganji M, Chan J, Stuckey PJ, Bailey J, Leckie C, Kotagiri R, Park L. Semi-supervised blockmodelling with pairwise guidance. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2019, Vol. 11052 LNAI. DOI: 10.1007/978-3-030-10928-8_10
  5. Zameni M, He M, Moshtaghi M, Ghafoori Z, Leckie C, Bezdek JC, Kotagiri R. Urban sensing for anomalous event detection: Distinguishing between legitimate traffic changes and abnormal traffic variability. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2019, Vol. 11053 LNAI. DOI: 10.1007/978-3-030-10997-4_34
  6. Wang X, Qi J, Kotagiri R, Sun Y, Li B, Zhang R. A joint optimization approach for personalized recommendation diversification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2018, Vol. 10939 LNAI. DOI: 10.1007/978-3-319-93040-4_47
  7. Correa O, Kulik L, Tanin E, Kotagiri R. Activity-based ride-sharing in action. GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems. 2018. DOI: 10.1145/3274895.3274991
  8. Hussain M, Bhuiyan A, Ishikawa H, Smith RT, Schuman JS, Kotagiri R. An automated method for choroidal thickness measurement from Enhanced Depth Imaging Optical Coherence Tomography images. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS. Pergamon. 2018, Vol. 63. DOI: 10.1016/j.compmedimag.2018.01.001
  9. Kotagiri R. Automatic Optical Coherence Tomography Imaging Analysis for Retinal Disease Screening Using Machine Learning Techniques. Proceedings - IEEE International Conference on Data Mining, ICDM. 2018, Vol. 2018-November. DOI: 10.1109/ICDM.2018.00011
  10. Hussain M, Bhuiyan A, Luu C, Smith RT, Guymer R, Ishikawa H, Schuman JS, Kotagiri R. Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm. PLOS ONE. Public Library of Science. 2018, Vol. 13, Issue 6. DOI: 10.1371/journal.pone.0198281
  11. Qi J, Zhang R, Jensen CS, Kotagiri R, He J. Continuous Spatial Query Processing: A Survey of Safe Region Based Techniques. ACM COMPUTING SURVEYS. Association for Computing Machinery. 2018, Vol. 51, Issue 3. DOI: 10.1145/3193835
  12. Kumar D, Ghafoori Z, Bezdek J, Leckie C, Kotagiri R, Palaniswami M. Dealing with Inliers in Feature Vector Data. INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS. World Scientific Publishing Co. 2018, Vol. 26. DOI: 10.1142/S021848851840010x
  13. Zhang X, Salehi M, Leckie C, Luo Y, He Q, Zhou R, Kotagiri R. Density biased sampling with locality sensitive hashing for outlier detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2018, Vol. 11234 LNCS. DOI: 10.1007/978-3-030-02925-8_19
  14. Rodriguez Sossa M, Kotagiri R, Buyya R. Detecting performance anomalies in scientific workflows using hierarchical temporal memory. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE. Elsevier Science. 2018, Vol. 88. DOI: 10.1016/j.future.2018.05.014
  15. Liu Q, Ghosh S, Li J, Wong L, Kotagiri R. Discovering pan-correlation patterns from time course data sets by efficient mining algorithms. 12th International Conference on Advanced Data Mining and Applications (ADMA). Springer Wien. 2018, Vol. 100, Issue 4. DOI: 10.1007/s00607-018-0606-9

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