I am a researcher in machine learning, computer vision, and medical image analysis, with a PhD in Electrical Engineering from Chalmers University of Technology. My work focuses on geometry-aware deep learning for structured spatial prediction in 3D medical images, including vascular centerline extraction, image-to-graph learning, segmentation, and detection, with publications in leading conferences such as MICCAI and AAAI. I am proficient in Python and PyTorch, with experience designing scalable models for complex datasets.
PhD Electrical Engineering
Chalmers University of Technology (Gothenburg, Sweden)
Jan 2021 - Jun 2026
MSc. Complex Adaptive Systems
Chalmers University of Technology (Gothenburg, Sweden)
Sep 2018 - Jun 2020
BE Electrical Engineering
National University of Sciences and Technology (Islamabad, Pakistan)
Sep 2012 - Jun 2016
Extensive experience building custom Transformer, CNN, RNN, and GNN models for vessel centerline detection, image segmentation, object detection, registration, and diverse ML tasks including semi/self-supervised, reinforcement learning, and NLP.
Experience with Python, PyTorch, TensorFlow, CUDA, C++, and MATLAB.
Skilled in creating custom datasets for image segmentation and spatial graphs, handling large-scale multimodal data (CT, MRI, endoscopy, images, text) for model training and validation using extensive evaluation criteria.
Git, Bash, Conda, Docker, Linux, Cloud GPUs (SLURM, Google Cloud), Experiment tracking (Neptune, Weights & Biases), Android software development.