My background was in applied mathematics, and my primary research interest centered on developing algorithms and methodologies for data science using machine learning. More specifically, I focused on developing algorithms for multidimensional, multimodal big data that found primary application in medicine, yet were generalizable to other branches where big data emerged.
My research focused on online tensor recovery methods (i.e., completion and decomposition) using simultaneous auxiliary information. These methods were mainly designed for multi-omic data (e.g., spatial transcriptomic data, genomic data, etc.).
The proposed methods had collateral benefits for the scientific community and for diagnosticians. The former could investigate new approaches, and the latter could utilize these methods for the purpose of developing online, user-friendly platforms for end-users.