My research lies at the intersection of statistics, data science, computational biology, and bioinformatics. I am interested in developing and applying mathematical, statistical, and machine-learning approaches to understand complex biological and real-world datasets.
Research Areas
- Computational Biology and Bioinformatics
Development of computational and statistical approaches for analysing genomic and biological data. - Comparative Genomics and Genome Evolution
Investigation of whole-genome duplication, polyploidy, sequence similarity, synteny, and evolutionary relationships across species. - Machine Learning and Artificial Intelligence
Application of machine-learning and deep-learning methods to biological, genomic, health, and other high-dimensional datasets. - Statistical Modelling and Data Science
Development and application of statistical methods for complex data, including experimental design, predictive modelling, and reproducible data analysis. - Interdisciplinary Applications
Collaborative research involving genomics, health sciences, agriculture, environmental science, and other areas where quantitative methods can help address scientific questions.
A major goal of my research is to connect methodological development with practical scientific problems. I work closely with students and interdisciplinary collaborators, with an emphasis on reproducible research, open computational tools, and the translation of data into meaningful scientific insights.