Multi-Data (MData) Lab: Modeling the real world heterogeneity
Data in the real world is seldom compartmentalized as is the model in traditional approaches. For example, data from one phenomenon at a location (e.g. health disparities) often intersects with or relates to or impacts other phenomenon (e.g. presence of food deserts, percentage of teen pregnancies) in the same location. So this begs the question: why do we study these phenomena in separate silos in algorithmic pattern discovery? Tackling this question and crossing over the data silos into a messy heterogeneous world is at the heart of the research in the Multi-Data Lab. Multi-domain relevance is evident in all types of application areas such as health care informatics, road traffic and, cybersecurity. In addition, data often is multi-resolution, collected with multi-modal mechanisms, multi-dimensional and originates from multiple disciplines. This is the true essence of this lab in dealing with the heterogeneity across multiple aspects of data.
MData- copyright 2021