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Fakhar Abbas

National University of Singapore, COMSATS University Islamabad, Southwest Jiaotong University, Harbin Engineering University, QuickLink Communications PVT, Islamabad

Research areas: Engineering · Electrical and Electronic Engineering · Vehicular Ad Hoc Networks (VANETs) · Traffic control and management · Advanced Wireless Network Optimization

ORCID iD 0000-0002-6850-5713

About

Fakhar Abbas (SM IEEE) is a Senior Research Fellow at the Centre for Trusted Internet & Community, National University of Singapore (NUS). He earned his Ph.D. in Information and Communication Engineering from the Key Laboratory of Information Coding and Transmission at Southwest Jiaotong University, China, in 2020.

With over a decade of experience in academic research, teaching, and interdisciplinary collaboration, he has authored more than 45 peer-reviewed publications in international journals and conference proceedings, including publications in IEEE Transactions, Elsevier, and Springer Nature journals. He was recognized among the World's Top 2% Scientists (2026) in the Stanford–Elsevier annual ranking for research impact. He has contributed to government-funded and international research projects in artificial intelligence, digital trust, and communication engineering.

Before joining NUS, he held academic positions at COMSATS University Islamabad, Pakistan, where he taught undergraduate and postgraduate courses, supervised student research, and contributed to curriculum development. He is a lifetime-registered engineer with the Pakistan Engineering Council (PEC) and currently serves as an Area Editor for Diagnostics (MDPI), having previously served as a Guest Editor. He also contributes to the international research community through peer review and service on conference technical programme committees.

His research focuses on generative AI, deep learning, computer vision, deepfake detection and generation, fake news detection, AI governance, data science, next-generation wireless communications, IoT, network security, mobile edge computing, and deep reinforcement learning for radio resource management in V2X.

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Publications (44)