Dr Oyesola Ojewunmi
Assistant Professor - Genetic Epidemiology
London School of Hygiene & Tropical Medicine
Keppel Street
London
WC1E 7HT
United Kingdom
Oyesola is a Research Fellow in Genetic Epidemiology at the London School of Hygiene and Tropical Medicine (LSHTM), London, UK. He earned a PhD in biochemistry from the University of Lagos, Nigeria, with a distinction. Prior to joining LSHTM, Oyesola held a position as a Research Associate at King’s College London for three years, during which he spearheaded a groundbreaking genome-wide association study focusing on fetal haemoglobin in Nigerian patients with Sickle Cell Disease. Notably, his outstanding work earned him the Best Poster Presentation at the Haematology Away Day at King’s College London and the Best Oral Presentation at the London Genetics Network in 2022.
His research journey has equipped him with diverse skills essential for advancing genetic studies. This spans from hands-on wet lab-based experiments to in-depth statistical-genetic analyses, covering genome-wide association testing, haplotype association analysis, fine-mapping, and meta-analysis.
Actively contributing to the academic community through teaching, his engagement with undergraduate and postgraduate students involves providing training and supervision, showcasing his commitment to nurturing the next generation of researchers.
Affiliations
Teaching
- I have experience teaching students through one-on-one sessions, small group tutoring, and large class lectures to foster engaging discussions and promote curiosity-driven learning. I provide pastoral support to the MSc and PhD students within our group.
- I teach genome-wide association study (Genomics health data module) in the MSc Health Data Science programme.
- I supervised and mentored a LIDo's PhD student on rotation in our Department (February - June 2024).
Research
In my research we are developing innovative methods for conducting meta-analysis and fine-mapping of genome-wide association studies. I am passionate about sickle cell disease research, particularly using multi-omics approach and machine learning to understand underlying biology/genetics of sickle cell disease complications.