Omar Shaur Choudhry

Profile

Omar is a PhD candidate on the UKRI CDT AI-Medical (Artificial Intelligence for Medical Diagnosis and Care) programme and recipient of the ESPRC Studentship Award. He achieved a BSc in Computer Science with Artificial Intelligence (2020-2023), achieving various prizes, whilst making him the youngest candidate on the CDT.

He is a student representative for the UKRI CDT for Artificial Intelligence for Medical Diagnosis and Care, postgraduate representative for students in the School of Computing and teaching assistant for all levels of undergraduate computer science students. He serves on the CDT’s Engagement Committee to help connect the cohorts research to the wider public, schools, industry and more, magnified due to his position as a BeCurious Associate. He also works closely with the President of the Artificial Intelligence (AI) Society at Leeds.

He has had industry experience with the United Nations International Computing Centre (UNICC), Discover Financial Services and Chelsea and Westminster Hospital NHS Foundation Trust. He holds Director for Handle Academy Ltd and OC Solutions Ltd. He has also has extensive teaching, tutoring and volunteering experience in schools and other organisations.

Responsibilities

• Postgraduate Teaching Assistant
• Postgraduate Representative for the School of Computing
• Student Representative for the CDT

Research interests

Omar’s PhD research focuses on Surgical Skill Improvement through AI-driven Training Enhancements. The Lancet Commission on Global Surgery (LCGS) in 2015 highlighted the urgent need for increased volume and quality of surgery as an indispensable component of global health. It has been estimated that 11% of deaths in low- and middle-income countries (LMICs) are due to conditions treatable by surgery. 80% of these deaths are preventable as they are caused by the lack of surgeons, consisting of cases that require only general surgical emergencies such as surgical site infections or trauma. Current training camps for laparoscopic surgery in the UK are performed in a 1-to-1 nature, a trainer will spend around an hour with a trainee before they are required to practice alone with little to no oversight. To speed up this training process, an AI system able to analyse the trainees’ skills from a recorded video feed and provide them with feedback could reduce the learning curve and increase the throughput of surgical trainees. There are currently early talks with international training networks in LMICs, proposing to deploy such a system and evaluate internationally, where the lack of trained surgeons is more distinct. In this project, the aim is to produce an automated skill assessment system that allows tracking of the skill levels of trainees. The skill feedback will be analysed on a sub-task basis and highlight tasks where they may focus their training.

Omar has received the Brad-ATTAIN Thesis Sharing Competition prize at the University of Bradford, presenting this project to a general audience in a series of lightning talks.

He completed his BSc dissertation on Survival Analysis with Neural Networks for Chronic Heart Failure Patients.

Qualifications

• BSc Computer Science with Artificial Intelligence, University of Leeds

Professional memberships

• Student Membership - BCS, The Chartered Institute for IT
• Student Membership - IET, Institution of Engineering and Technology

Student education

Omar has been assisting in lab sessions across various modules at the School of Computing since 2022. He was the first undergraduate teaching assistant of his cohort. Upon completion of his BSc degree, he is now a postgraduate teaching assistant. He also co-supervises MEng projects within the School of Computing.