Dr Lim Mei Kuan

School of IT

+603 5515 9738
Room 2-4-37

Personal statement

Mei Kuan LIM is a lecturer attached to the School of Information Technology at Monash University Malaysia. She graduated from Universiti Malaysia Sarawak, Malaysia in 2007 with a First Class Honours in Computer Science. In 2015, she received her Doctoral degree from University of Malaya, Malaysia, under the scholarship of Yayasan Khazanah. She further completed her post-doctoral studies in the Department of Artificial Intelligence in the University of Malaya until 2016. Previously, she was a researcher in MIMOS Berhad from 2004 to 2007, conducting research and development in video surveillance, in particularly intelligent video analytics solutions. She was also a visiting researcher in Kingston University, United Kingdom in 2012 and 2013.  Her research interests include swarm intelligence, data and video analytics, computer vision and machine learning. She has also served as reviewer for several conferences and journals, and as an organizing committee in several conferences such as ACPR 2015 and VCIP 2013.

Academic degrees

  • Doctor of Philosophy (Computer Science, Computer Vision) (2015), University of Malaya, Malaysia
  • Degree in Computer Science (with First Class Honours) (2007), Universiti Malaysia Sarawak

Professional affiliations

Member of International Professional Bodies

  • IEEE Computer Society

Research Interests

Her primary research focus is in applying Artificial Intelligence algorithms, in particularly utilizing the Swarm Intelligence approaches to understand and solve problems in complex systems. She is currently engaged in social media analytics, utilizing visual and textual information to analyse the underlying behaviours of social media that may in turn lead to collective intelligence.

Research Projects

Title: Identifying behaviour from social media images

This project aims to investigate if images from social media contains footprint of the mood of individuals. In particularly, this project involves crawling of social media images and classifying those using deep learning models. Ideally, this project would be able to detect and make predictions of users’ behaviour or pattern by transforming and extracting signals from dizzying amounts of publicly available images. Automated detection and analysis of social media may potentially help to identify depressed or otherwise at-risk individuals through large-scale passive monitoring of social media, and in the future may complement existing screening procedures.


Units taught

FIT1051 - Programming Fundamentals in Java

FIT9131 - Programming Foundations in Java

No content

Current supervision

Ali Moltajaei Farid
Novel Approaches to Tracking Swarms of Robots
August 2018 - Present
Monash University Malaysia

Local Award/Recognition/Exhibition/Stewardship

Update Coming Soon



  • V.J. Kok, M. K. Lim, C. S. Chan, Crowd behaviour analysis: A review where physics meets biology, Neurocomputing, 177, pp. 342-362, 2016. (ISI Q1)
  • M. K. Lim, C. S. Chan, D. Monekosso, P. Remagnino, Refined Particle Swarm Intelligence Method for Abrupt Motion Tracking, Information Sciences, pp. 267-287, 2014. (ISI Q1)
  • M. K. Lim, S. Tang, C. S. Chan, iSurveillance: Intelligent Framework for Multiple Events Detection in Surveillance Videos, Expert Systems and Applications, 41(10), pp. 4704-4715, 2014. (ISI Q1)
  • M. K. Lim, C. S. Chan, D. Monekosso, P. Remagnino, Detection of Salient Regions in Crowded Scenes, IET Electronic Letters, pp. 363-365, 2014. (ISI Q3)
  • M. Kiran, A. H. Abdalla, M. K. Lim&Y. J. Yap, Execution Time Prediction of Imperative Paradigm Tasks for Grid Scheduling Optimization, International Journal of Computer Science and Network Security, 9(2), pp. 155-163, 2009.


  • M. K. Lim, V. J. Kok, C. C. Loy, C. S. Chan, Identifying Anomalies in Crowded Scenes via Global Similarity Structure, Proceedings of the International Conference on Pattern Recognition, pp. 3957-3962, 2014.
  • M. K. Lim, C. S. Chan, D. Monekosso, P. Remagnino, Swarm-based Abrupt Motion Tracking, IEEE workshop on Visual Object Tracking Challenge, pp. 98-111, 2013.
  • M. K. Lim, C. S. Chan, D. Monekosso, P. Remagnino, SwATracK: A Swarm Intelligence-based Abrupt Motion Tracker, Proceedings of the Thirteenth International Conference on Machine Vision Applications, pp. 3740- 3744, 2013.
  • M. K. Lim, S. L. Tang, N. Samudin & K. M. Liang,   Real-time Estimation of Point-of-Interest in Video Surveillance Based on an Analysis of Visual Fixation, Proceedings of the Tenth International Conference on Information Sciences, Signal Processing and their application, pp. 357-360,  2010.
  • S. L. Tang, Z. Kadim, K. M. Liang & M. K. Lim, Hybrid blob and particle filter tracking approach for robust object tracking, Proceedings of the International Conference on Computational Science, pp. 2549-2557, 2010.
  • S. L. Teng, C. S. Chan, M. K. Lim & W. K. Lai, Hybrid Particle Swarm Optimisation for Data Clustering, The Second International Conference on Digital Image Processing, pp. 75460E-75460E-6, 2010.
  • S. L. Tang, K. M. Liang, M. K. Lim, Z. Kadim, & A. A. B. Al-Deen, Colour-based Object Tracking in Surveillance Application, IAENG International Conference on Computer Science, pp. 459-464, 2009.
  • M. Kiran, M. W. Kan, M. K. Lim, K. M. Liang & W. K. Lai, Implementing Image Processing Algorithms Using ‘Hardware in the Loop Approach’ for Xilinx FPGA, International Conference on Electronics Design, pp. 1-6, 2008.
  • W. K. Lai, E. L. Ng, T. H. B. Maul & M. K. Lim, Investigations into Particle Swarm Optimization for Multi-Class Shape Recognition, International Conference on Neural Information Processing, pp. 599-606, 2008.
  • M. Kiran, A. H. Abdalla, M. K. Lim & Y. J. Yap, A Prediction module to optimize scheduling in a Grid computing Environment, International Conference on Computer and Communication Engineering, pp. 888-893, 2008.
  • M. K. Lim, Y. J. Yap, M. Kiran & W. K. Lai, Execution Time Prediction of Imperative Paradigm Tasks for Grid Scheduling Optimization, Enabling Grid for E-Science (EGEE) 3rd User Forum, 2008.