Dr Iqbal Madakkatel
Research Associate, Machine Learning
School of Public Health
College of Health
Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.
Dr. Iqbal Madakkatel is a Research Associate at the Nutritional and Genetic Epidemiology Research Group, specializing in the application of Machine Learning in Epidemiology and Public Health. His work is at the intersection of data science and health research, leveraging advanced computational techniques to address pressing health challenges. His research focuses on feature selection and risk factor discovery, aiming to uncover critical insights that can inform public health interventions. In addition to applying advanced machine learning algorithms to identify key determinants of health outcomes, he also has a strong background in statistical modelling, which allows him to interpret complex datasets and draw meaningful conclusions. He is passionate about both developing new methodologies and applying existing ones to extract valuable information from health data. His work contributes to the broader understanding of disease patterns and health outcomes, ultimately aiming to improve population health. He is committed to advancing the field of health informatics through rigorous research and innovative approaches.
Having completed his Diploma and bachelor’s degree in computer engineering, he pursued further education and received his M.Sc. degree in information technology, with a focus on informatics. In 2021, he obtained a PhD in Data Science with a focus on Epidemiology/Public Health.
Before his foray into doctoral studies, he held multiple roles within the banking industry over the span of a decade. He possesses an array of professional certifications in project management, quality, and enterprise architecture, reflecting his commitment to professional growth and expertise in various aspects of the industry. He holds a Chartered Engineer membership at the Institution of Engineers (India). His extensive work experience and academic pursuits underscore his passion for continual learning, innovation, and problem-solving in the rapidly progressing landscape of big data analytics, intricate computational modelling, and the rising domain of artificial intelligence.
| Year | Citation |
|---|---|
| 2021 | Madakkatel, I., King, C., Zhou, A., Mulugeta, A., Lumsden, A., McDonnell, M., & Hyppönen, E. (2021). Identifying risk factors for COVID-19 severity and mortality in the UK Biobank. DOI |
| Date | Role | Research Topic | Program | Degree Type | Student Load | Student Name |
|---|---|---|---|---|---|---|
| 2023 | Co-Supervisor | - | Doctor of Philosophy | Doctorate | Full Time | Mr Leta Geleta |
| 2023 | Co-Supervisor | - | Doctor of Philosophy | Doctorate | Full Time | Mr Endeshaw Chekol Abebe |
| 2022 | Co-Supervisor | - | Doctor of Philosophy | Doctorate | Full Time | Mr Yigizie Yeshaw Mihiretie |