My main goal in teaching is to bridge the gap between the curricula of the two related fields of (big) data systems and data analytics and retrieval. In order to address the convergence in the application domains of multimedia and data and information management disciplines, I see an impending need for undergraduate and graduate curricula that will not only educate students separately in each individual domain, but will also provide a common perspective. Most importantly, I believe the curricula must introduce students to the concept of heterogeneity in data, data sources, and systems, as the convergence of different application domains generally means an increase in the heterogeneity of their underlying, relatively less-complex, domains.
Design and Development Team, PhD in Data Science, Analysis, and Engineering (DSAE)
This PhD program is designed to train scientists and engineers in development of new systems and algorithms for collecting, cleaning, storing, valuing, aggregating, fusing, summarizing, managing and drawing inferences from high dimension, high volume, heterogeneous data streams for knowledge discovery. The program’s educational objective is to develop each student’s ability to perform original research in the development and execution of data-driven methods for solving major societal problems. This includes the ability to identify research needs, adapt existing methods and create new methods as needed for data analytics and engineering.