We apply informatics methods, machine learning and data science at the immune interface of health and diseases, with a focus on infections such as HIV, dengue, and tuberculosis; autoimmune diseases, such as lupus and arthritis; rare diseases and cancer, by:
- benchmarking informatics methods and tools towards reproducible analyses
- developing algorithms, using graph analysis and applying machine learning to uncover patterns in data, and detect rare clones for disease diagnostics and disease progression
- integrating analytics to real-world data for medical software development in order to inform and support clinical decisions and drug discovery