Dr Mehwish Nasim is a lecturer in Computing and Mathematical Sciences at the College of Science and Engineering at Flinders University. She is also an adjunct lecturer at the University of Adelaide, an associate investigator with ARC Centre of Excellence for Mathematical and Statistical Frontiers, and a visiting scientist at CSIRO, Australia. She is a member of the Australian Mathematics Society and Women in Maths Special Interest Group.
She did her Ph.D. in Computer Science from University of Konstanz, Germany. Her research lies at the intersection of applied mathematics and social psychology. She is particularly interested in network science, understanding grey-zone tactics, combating online misinformation, serious games, and decision making in the context of cyber security.
She is working on AI-enabled situational understanding models for combating misinformation, using graph-theoretic knowledge-based constructs, coupled with natural language processing techniques and social psychology. Her work has led to the design of agent-based network simulation models that can be deployed in modern wargames which can be used by defence and the government for training decision-makers to combat online misinformation during crisis.
She is also involved in multiple collaborative projects with researchers from Germany, UK and several Asian countries in order to develop technology for the emergent users.
Human Computer Interaction
PhD, University of Konstanz, Germany
Invited talks, refereed proceedings and other conference outputs
Bilal, A., Rextin A., Kakakhel A., & Nasim M.
(2017). Roman-txt: forms and functions of roman urdu texting. Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services. 15. doi: 10.1145/3098279.3098552
Tuke, J., Nguyen A., Nasim M., Mellor D., Wickramasinghe A., Bean N. G., et al.
(2018). Pachinko Prediction: A Bayesian method for event prediction from social media data. arXiv preprint arXiv:1809.08427.
Nasim, M., Rextin A., Hayat S., Khan N., & Malik M. Muddassir
(2017). Data analysis and call prediction on dyadic data from an understudied population. Pervasive and Mobile Computing. 41, 166–178. doi: 10.1016/j.pmcj.2017.08.002