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  3. Rashid Kamal

  • Rashid Kamal is a Lecturer (Assistant Professor) in Computer Science. He has over seven years of experience as a lecturer, researcher, and software developer in the fields of machine learning, data analysis, algorithms, and mobile application development. He holds a bachelor’s degree in computer science from IUIC University and a master’s degree in computer science from COMSATS University. He is currently pursuing a PhD at Ulster University, where his research is focused on behavioural modelling and opportunistic sensing.

    Kamal has taught a variety of computer science and programming courses at the undergraduate level in institutions in both Pakistan and the UK. These courses have covered topics such as machine learning, data structures, object-oriented programming, and mobile app development. His research has delved into areas like big data analysis, emotion classification, notification management on mobile devices, and algorithm optimization.

    Kamal has published seven peer-reviewed journal and conference papers.

  • As a lecturer, Kamal has taught courses in artificial intelligence, machine learning, data structures, object-oriented programming, visual programming, mobile app development, and modern programming languages. He has experience teaching at both undergraduate and graduate levels in universities in Pakistan and the UK. His academic journey includes tenures at Abasyn University, Ulster University, and Shaanxi University of Science and Technology, the latter being in collaboration with Ulster University. Kamal has mentored final year students on their projects, some of which have garnered accolades in national competitions. Additionally, he has collaborated with industry partners on projects such as the development of tourism platforms and AI chatbot applications.

  • Kamal’s research primarily centres on machine learning, big data analysis, and optimization algorithms. His peer-reviewed publications encompass topics such as Hadoop-based big data mining, emotion classification using crowd-sourced data, optimization of MPLS networks through particle swarm algorithms, real-time X opinion mining, and intelligent notification scheduling on smartphones. His ongoing PhD research delves into modelling human behaviour and employing opportunistic smartphone sensing to predict when a user is most receptive to receiving notifications. Kamal has received several research awards and has successfully secured research funding for projects in areas like AI, customer prediction, hardware optimization, and beyond.

  • For publications, projects, datasets, research interests and activities, view Rashid Kamal’s research profile on Pure, the University of Northampton’s Research Explorer.