Curriculum Vitae
A copy of my CV Available Here
Education
PhD in Electronic Engineering and Computer Science
April-2022 - “Aiming to wrap up by March 2025… unless my thesis decides to throw a plot twist!” 😄
Queen Mary University of London, United Kingdom
Thesis Topic 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-Present)
I am working on AI-based Cardiac Image Computing to solve cardiac-related problems using segmentation. I am incorporating 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-Present)
Developing AI-based approaches to segment corrosion on steelworks using images of transmission towers. The project aims to precisely predict the steelwork on various backgrounds and then estimate the proportion of the corrosion. The images are collected through drones, helicopters, and simulations (synthetic data) to train the algorithms. This project has 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)
Developing AI-based approaches to segment corrosion on steelworks using images of transmission towers. The project aims to precisely predict the steelwork on various backgrounds and then estimate the proportion of the corrosion. The images are collected through drones, helicopters, and simulations (synthetic data) to train the algorithms. This project has been shortlisted for the IAM Asset Management Excellence Awards 2023 - UK and IET Excellence and Innovation Awards in AI and Robotics.
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 emotions, using the existing facial expression algorithms to assess the emotional potency of art, and coming up with some unique 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.