Abdul Rehman

PROFESSIONAL PROFILE Undergraduate researcher specializing in deep learning for medical imaging, with experience in MRI segmentation, retinal disease classification, and dental implant detection using explainable AI EDUCATION BS Computer Science SEECS , Islamabad , 3.75/4.0 (2026) INTERNSHIP EXPERIENCE Gengini 01-Jun-2025 - 31-Aug-2025 Machine Learning Intern Deep Learning Lab, National Center of Artificial Intelligence (NCAI) 01-Jun-2025 - 31-May-2026 Research Assistant Brain Vision Lab 23-Jan-2026 - 23-Jan-2026 Research based project on identification of LLM Bias FINAL YEAR PROJECT AI-Based Automatic Frequency Offset Selection for bSSFP CINE MRI at 3.0 Tesla Balanced Steady State Precision (bSSFP) CMR sequence provides high contrast but is sensitive to off-resonance effects, requiring frequency scouting to minimize banding artifacts. Manual selection is time-consuming and subjective, warranting an AI-driven solution. Objective: To develop and validate an AI-based algorithm for automatic frequency offset selection in bSSFP CMR. Methods: • Study Design: Study in 100 patients at 3T. • AI Model: A deep learning algorithm trained on bSSFP scout data to predict optimal frequency offsets. • Comparison: AI-selected vs. manually selected offsets assessed for artifact reduction, image quality, and efficiency. • Outcomes: Primary: Optimal banding suppression. Secondary: Time efficiency and agreement with experts. Expected Impact: AI-driven frequency selection could improve image quality, standardize CMR workflows, and reduce scan time. TECHNICAL EXPERTISE Machine Learning & Deep Learning Hands-on experience in machine learning and deep learning techniques, including supervised and unsupervised learning, CNNs, GANs, and deep neural networks. Applied these methods to real-world research problems with strong performance across classification and segmentation tasks. Medical Imaging & Healthcare AI Specialized in AI-driven medical imaging, with experience in MRI segmentation, cardiac CINE MRI analysis, retinal disease classification, and dental implant detection. Developed and evaluated clinically relevant models using healthcare datasets and research-grade benchmarks.

Undergraduate researcher specializing in deep learning for medical imaging, with experience in MRI segmentation, retinal disease classification, and dental implant detection using explainable AI