Emadeldeen Hamdan

Research Publications Presentations Experience Recognition Skills Contact

Efficient AI / Computer Vision / Medical Imaging

Emadeldeen Hamdan

I am a Ph.D. candidate in Electrical and Computer Engineering at the University of Illinois Chicago, developing efficient and generalizable AI systems for real-world computer vision and signal processing applications.

My research spans medical imaging, remote sensing, and biomedical signal analysis, with a focus on lightweight deep learning, CNNs, Vision Transformers, and state-space models (Mamba), transform-based learning using Hadamard and DCT representations, and hardware-aware AI. I am particularly interested in designing models that bridge algorithmic innovation and practical deployment, from MRI reconstruction and cancer imaging to wildfire detection and real-time signal processing.

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Portrait of Emadeldeen Hamdan
Ph.D. Candidate, UIC ECE Expected graduation: May 2027

Advisors

Research mentorship and guidance

I am grateful to work with advisors whose research has shaped my direction in efficient AI, signal processing, computer vision, and medical imaging.

Ahmet Enis Cetin Google Scholar Ulas Bagci Bagci Lab

Explore

Go deeper into my work.

Research Efficient AI, medical imaging, remote sensing, and hardware-aware ML. Publications Selected papers, links, and expandable summaries. Presentations Invited talks, demos, symposium presentations, and FastML contributions. Experience Research, teaching, editorial work, and engineering roles. Recognition Awards, leadership, and challenge results. Skills Tools and methods for building efficient AI systems. Contact Email, GitHub, LinkedIn, and Google Scholar.

Collaborators

Research collaborations and partner institutions.