Nitin Goyal | Computer Science | Research Excellence Award

Nitin Goyal | Computer Science | Research Excellence Award

Central University of Haryana | India

Dr. Nitin Goyal is a faculty member in the Department of Computer Science and Engineering at the Central University of Haryana, India, and holds a PhD in Computer Engineering from NIT Kurukshetra. His expertise lies in underwater wireless sensor networks (UWSN) with applications in military operations, along with research interests in machine learning, deep learning, IoT, and wireless sensor networks. With over 16 years of academic and teaching experience, he has published around 175 research works across SCI, SCOPUS, conference proceedings, and book chapters, filed nearly 30 patents, and edited multiple books with leading international publishers. He has successfully guided postgraduate and doctoral researchers, led an AICTE-funded project, delivered numerous expert and keynote lectures, chaired sessions at reputed international conferences, and actively contributes to the academic community as a Senior Member of IEEE, lifetime member of IAENG, guest editor, reviewer, and editorial board member of several high-impact journals.

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Junaid Akram | Computer Science | Research Excellence Award

Junaid Akram | Computer Science | Research Excellence Award

Unsw College | Australia

Dr. Junaid Akram is a researcher in decentralized systems and trustworthy AI, with a PhD from the University of Sydney. His work focuses on building secure, privacy-preserving, and reliable trust mechanisms for safety-critical, crowdsourced platforms, particularly in drone-based services for environmental monitoring and emergency response. His research integrates blockchain and decentralized public key infrastructures, verifiable credentials, adversarial machine learning, and data analytics to improve security, fairness, and resilience in multi-sided ecosystems. Through frameworks such as decentralized identity management, tamper-resistant reputation systems, anomaly detection, and robust graph learning, his contributions aim to reduce operational risk while enhancing participation and service quality in emerging digital platforms.

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