AI, Graph Learning & Distributed Systems
Muhammad Tayyab Chaudhry, PhD
Associate Professor of Computer Science · COMSATS University Islamabad
Computer scientist and HEC-approved supervisor researching neural networks, graph learning, blockchain, edge computing and intelligent systems.
Lahore, Pakistan
Academic profiles
01
Research biography
Muhammad Tayyab Chaudhry brings more than 19 years of research and teaching experience to COMSATS University Islamabad. He earned his PhD from the University of Malaya and has worked across neural networks, graph learning, blockchain, IoT and edge computing, including scholarship published through venues such as ACM Computing Surveys.
Project with NSRI
Proposed research directionGraph Neural Networks for Fraud and Anomaly Detection in Financial or Blockchain Networks
This direction is designed to become a focused, student-executable research project. The final question, method, and deliverable are refined with the advisor and NSRI research team.
02
How they will advise
Academic standing
- PhD in Computer Science; MS in Networks and Distributed Systems
- Associate Professor of Computer Science · COMSATS University Islamabad
- Lahore, Pakistan
Open to advising
- Independent researchers capable of executing a project with limited supervision
- Graduate-level researchers
- NSRI Research Leads who have demonstrated prior research experience
Advisor role
- Suggest a specific research project or question
- Suggest extensions of my existing research
- Review student-generated project proposals in my field
- Provide occasional high-level scientific guidance
- Review completed work for scientific accuracy
- Consider deeper collaboration when a project becomes sufficiently strong
Potential outputs
- Statistical analysis or research report
- Model or benchmark
- Code or software tool
- Manuscript or preprint
- Conference poster or presentation
03
Selected research
Publication and scholarly profile links supplied by the researcher or their public academic record.
Chaudhry, M. T., et al. (2026). Incentive-Based Energy-Efficient Workload Scheduling of Mobile Edge Computing Using Blockchain. Energies, 19(11), 2592.
Chaudhry, M. T., Yousafzai, A., Zia, A., Abid, S. A., & Ahmad, F. (2025). Deadline-aware Workload Scheduling for Edge-enhanced IoT Devices: A Blockchain-enabled Approach to Incentive-based Computing. Peer-to-Peer Networking and…
Farooq, A., Chaudhry, M. T., et al. (2025). Innovative Neural Network Approach for Solving Time Dependent Kohn–Sham Equations with Jensen's Inequality. Punjab University Journal of Mathematics, 57(2), 165–179.
Ready to build with an NSRI research team?
Explore open research groups