Integrated genetic, clinical, and other phenotypic data related to multiple sclerosis and other neurological disorders. Conducted case/control GWAS and gene-based burden tests using common frequency genotype and rare variant whole exome sequencing data.Applied statistical methods to identify potential biomarkers for multiple sclerosis. Derived and analysed polygenic risk scores to assess how genetic risk influences clinical variables related to disease severity, subtypes and progression.

Completed PhD in Cancer Sciences on research thesis titled "Utilising genomics for risk prediction and targeted prevention of endometrial cancer" with a main focus on developing a polygenic risk score for predicting the risk of developing endometrial cancer.During this time, I also took on graduate teaching assistant position within the faculty, demonstrating and demonstrating laboratory practicals for undergraduate students.

Optimisation of protocols related to single cell RNA sequencing of menstrual blood to assess its feasibility as a diagnostic tool for endometrial pathologies. Key techniques included; primary human sample processing, FACS and MACS sorting and single cell RNA sequencing using 10x Chromium system.

Involved across several research projects as part of University of Oxford/Bayer HealthCare Alliance on Women's Health. Conducted experiments and relevant analyses to understand the role of inflammation and innate immune cells in endometriosis and identify novel drug targets.Key techniques included CyTOF and multicolour flow cytometry and related data analysis, clinical sample handling, cell isolation, primary cell culture, cellular assay preparation, histology techniques, protocol optimisation and panel design.

Voluntary work at the Molecular Biology and Medical Genetics lab of Near East University Hospital, Cyprus, assisting with routine laboratory tests and ongoing research projects. The routine tests included preimplantation genetic diagnosis, karyotyping for chromosomal abnormalities, and detection of infection by utilising DNA extraction and PCR techniques. Closely followed an ongoing GWAS project to identify SNPs associated with genetic disorders commonly found in the local population such as muscular dystrophy.