Rafia Ahsan

- Built a deployment-ready end-to-end AI pipeline for early prediction of stroke treatment outcomes from multimodal MRI data, delivering full analysis in less than 5 minutes, working in a Linux environment. - Designed lesion segmentation & thrombus detection modules using nnU-Net and Attention-LSTM, improving accuracy. - Developed an unsupervised 3D image registration model enabling multimodal alignment of MRI data.

- Ensured reliability and quality control in large-scale telecom projects. - Collaborated with cross-functional teams to streamline communication and reduce operational errors.

− Research in deep learning and computer vision, specializing in multi-size vehicle detection from satellite imagery. − Utilized deep learning techniques with the PyTorch framework to optimize detection model for multi-size vehicle identification from satellite imagery. Project Title: Multi-Vehicle Detection in Satellite Images using Deep Learning based Detection Network

− Led medical imaging research, specializing in deep learning for tumor classification and segmentation. − Conducted comparative analysis of deep learning-based object detection models for tumor diagnosis. − Published findings in the ISMRM conference, contributing to advancements in AI driven medical imaging.

Key Courses: -Advances in Machine Vision -3D Data Analysis -Reinforcement Learning -Virtual and Mixed Reality -Image and Video Processing

Key Courses: -Machine Vision -Statistical Machine Learning -Signal Processing -Image Processing -Electronics for Transmission -Design of Integrated Circuits -Embedded Systems

Key Courses: -Digital Logic Design -Digital System Design -Digital Signal Processing -Communication Systems -Electronics -Electric Circuit Analysis -Analog Filter Design -Control System -Introduction to Computer Programming -Object Oriented Programming -VLSI Design - Embedded Systems -Multivariab