I am Asad Masood, a dedicated software engineer with an MSc in Advanced Computer Science from the University of Sheffield.
I am Asad Masood, a dedicated software engineer with an MSc in Advanced Computer Science from the University of Sheffield. I am currently a Research Software Engineer at the University of Leicester, where I lead informatics for UK-REACH, a large-scale respiratory study with over 24,000 participants, and I develop secure data pipelines and participant-facing web portals.
I enjoy collaborating across disciplines to translate complex data into practical tools, from Django-based study websites with e-consent to ML-driven health recommendations. My aim is to advance healthcare and research through robust software engineering, data science, and user-centered design.
P-STEP provides health guidance to people with long-term health conditions by providing air quality information on walking routes they undertake. The P-STEP app combines expert clinical and exercise advice – which takes into account a person’s long-term health condition. The app gathers data from satellites to provide users of the app with real-time information on the air quality and weather when walking outdoors.
Led informatics for UK-REACH, a research study on COVID-19 and healthcare worker attrition, managing data for 24K+ participants. Developed and maintained a Django-based study website and implemented e-consent for participant enrollment. Built Python scripts using NumPy, Pandas, and SQL to extract and validate research data from databases used in multiple published research articles.
Led informatics for UK-REACH, a research study on COVID-19 and healthcare worker attrition, managing data for 24K+ participants. Developed & maintained a Django-based study website and implemented e-consent for participant enrollment. Developed Python scripts using NumPy, Pandas, and SQL to extract and validate research data from databases used in multiple published research articles.
Initiated the development of an Android app (P-STEP) to motivate 80+ patients with chronic health conditions to walk, achieving 50%+ engagement in trials. Built an LSTM-based deep learning model to predict optimal walking times based on environmental conditions and step count history, improving activity recommendations. Fostered cross-disciplinary partnership, collaborating with a team of over 20 members, to ensure the app’s efficacy in achieving health goals.
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