Curriculum Vitae
A copy of my CV Available Here
Education
PhD in Electronic Engineering and Computer Science (April-2022 May-2025)
Queen Mary University of London, United Kingdom
Thesis Topic Novel Methods for Deep Learning-Based Cardiac Image Segmentation
Master of Science in Electronics and Information Engineering (Sep-2019 Oct-2021)
Jeonbuk National University, Jeonju, Republic of Korea
Thesis Topic End-to-End Supervised Stereo Imaging- Based Method for Depth Estimation
Bachelor of Science in Electrical and Electronics Engineering (Sep-2014 June-2018)
Bahria University Islamabad, Pakistan
Thesis Topic Deep Learning Based Automated Extraction of Retinal Layers for Analyzing Retinal Anomalies
Research Experiences
1- PhD Graduate Student Researcher
Queen Mary’s Digital Environment Research Institute (April-2022 May-2025)
I worked on AI-based Cardiac Image Computing to solve cardiac-related problems using segmentation, where I incorporated different innovations into Cardiovascular Imaging using multiple state-of-the-art deep learning approaches, including CLIP, Compositional AI, designing loss functions for segmentation, and Multi-Modal AI.
2- Research Intern
KeenAI (June-2022 Dec-2024)
Developing AI-based approaches to segment corrosion on steelworks using images of transmission towers. The project aimed to precisely predict the steelwork on various backgrounds and then estimate the proportion of corrosion. The images are collected through drones, helicopters, and simulations (synthetic data) to train the algorithms. This project had been shortlisted for the IAM Asset Management Excellence Awards 2023 - UK and IET Excellence and Innovation Awards in AI and Robotics.
3- Research Assistant
NIHR Barts Biomedical Research Centre, London (December-2023 Sep-2024)
I worked on innovating cardiac digital twins to model acute coronary syndromes, transcatheter aortic valve replacement, and hypertension. My research themes included RNN-based blood pressure prediction, large-scale data cleaning, and developing an aorta segmentation algorithm.
4- Research Assistant
University of Cambridge, McDonald Institute for Archaeological Research (Oct-2021 April-2022)
I worked on computational recognition of facial expressions in visual arts. The project’s goals included gathering a small training dataset of sculptures to analyze their emotional content, utilizing existing facial expression algorithms to assess the emotional potency of art, and developing novel ideas to predict facial expressions. We proposed a multi-label classification approach to predict ambivalent facial expressions in sculptures.
5- MS-Graduate Student Researcher
Core Research Institute of Intelligent Robots, Jeonbuk National University, Republic of Korea (Sep-2019 Oct-2021)
I worked on different projects related to medical image processing and smart farming using state-of-the-art computer vision algorithms to solve problems like classification, object detection, depth estimation, and segmentation for precision agriculture and healthcare. My research aimed to design efficient deep learning modules for real-time applications by reducing the algorithms’ parameterized complexity and inference time.