My background is in applied mathematics and my primary research interest centers on developing algorithms and methodologies for data science using machine learning. More specifically, I am focused on developing algorithms for multidimensional multimodal big data which find primary application in medicine yet it is generalizable to other branches where bid data emerge.
My current research focuses on online tensor recovery methods (i.e. complication and decomposition) using simultaneous auxiliary information. They are mainly designed for multi-omic data (e.g. spatial transcriptomic data, genomic data and etc.).
The proposed methods will have collateral benefits for the scientific community and for the diagnosticians. The former may investigate new approaches and the latter may utilize these methods with the purpose of developing online/user-friendly platforms for the end-users.