Asif Yar

Asif Yar, PhD, is a researcher in the Department of Computing, focusing on deep learning, wireless sensor networks, and environmental monitoring. His work bridges machine learning and IoT technologies to address real-world challenges in sensor calibration and data accuracy.

• Research Themes: Deep learning methods, wireless sensor networks, extreme learning machines, environmental monitoring, and distributed optimization.
• Collaborations: Active in interdisciplinary research with affiliations in IIoT edge environments and air pollution monitoring systems.
• Citations: 1 citation (Scopus), with a h-index of 1.

His recent publications highlight advancements in self-calibration algorithms for large-scale wireless sensor networks using deep learning and attention-based GRUs, alongside novel approaches like temporal fusion transformers for air pollution monitoring in IIoT edge environments. He also explores distributed optimization frameworks using Apache Spark for scalable systems.

Asif Yar is currently accepting PhD students and contributes to the academic community through peer-reviewed conference publications and collaborative research projects.