Our expertise is in implementing computational methods and machine learning models to solve biomedical research questions. Our current area of focus is digital health and infectious diseases.

We utilize cutting-edge AI and genomics technologies with significant outcomes for the academic and clinical communities to discover new treatments and improve healthcare. Our approach employs machine learning methods to automatically learn complex features from individual data types, and harmonize heterogeneous multimodal information.  

Solving complex diseases require the integration of multi-modal big data, each contributing prominent features of biological significance. Existing integrative approaches merge multi-modal data during post-processing, which risks losing quantitative information of individual modalities, leading to erroneous analysis. We address this problem by analyzing the large volumes of sequencing, image, and digital health data in its raw form using machine learning. A critical advantage of the design is that it limits significant assumptions, as the user inputs data in its primary form. This reduces information loss, increasing sensitivity to weak signals in the data, robustness, and reproducibility.