Welcome to Zaman’s Academic Website!

I am a Computer Engineering graduate with strong research interests at the intersection of Artificial Intelligence, Machine Learning, and hardware-centric intelligent systems. My academic work focuses on developing non-invasive human sensing and occupancy detection systems using Wi-Fi Channel State Information (CSI), where I integrate signal processing, machine learning, and embedded systems to solve real-world problems.

During my undergraduate research, I designed and implemented a Wi-Fi-enabled occupancy detection system using Raspberry Pi and Nexmon, involving real-time CSI extraction, noise reduction, feature engineering, and ML-based classification. This work led to the collection and publication of a CSI dataset on the IEEE platform, and a research paper currently submitted to a high-impact journal, with me as the first author.

In parallel, I have hands-on experience in machine learning, deep learning, and computer vision, including regression models, CNN-based systems, GANs (CycleGAN), and real-time inference pipelines. My hardware background includes FPGA design, embedded Linux, UART-based systems, and edge AI deployment, enabling me to bridge the gap between theory and implementation.

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