Data Science and Visual Analytics

An overview of VisIRR. The user can start by issuing a query (A) (e.g., the keyword ‘dis- ease’). VisIRR visualizes retrieved documents (circles) in a scatter plot and a table view (B) along with a topic cluster summary (E). A node size encodes the citation count. Users can rate documents on a 5-star rating scale in order to indicate their particular interest. Based on preference rating, VisIRR provides a list of recommended items (C), which are also projected back to the existing scatter plot view as rectangles, so that a consistent topical perspective can be maintained. For better understanding, the user can apply computational zoom-in on recommended items in order to obtain a much clearer summary (D).

Massive amounts of data are created by research, medicine, and society through large-scale scientific simulations, high-throughput and multi-modal experimental devices, web and social media usage, and ever increasing number of sensors in the environment, manufacturing, and even in our daily lives. From engineering to medicine, manufacturing to social sciences, the challenge has shifted from generating sufficient amounts of data to understanding and using it, hence resulting in the rapid emergence of the field of Data Science. CSE faculty are prominent leaders in the interdisciplinary field of Data Science, particularly in developing new data and visual analytics approaches to analyze and transform large and complex data sets into knowledge and actionable information.


Specific topic areas of interest within CSE include: 

  • Data mining and visualization
  • Data and AI security
  • Graph analytics
  • Health analytics
  • Network science
  • Social and urban computing
  • Explainable and equitable AI

Current interdisciplinary research directions at CSE include developing scalable, interactive and interpretable tools for large-scale data and AI/ML models; understanding and managing dynamical systems, varying from understanding and fighting against the spread of diseases to improving urban infrastructure and strengthening safety and well-being on the web.

Research in data science and visual analytics at Georgia Tech involves many campus units — spanning colleges, schools, and individual labs. Together, these researchers work to create new collaborative opportunities, strengthen partnerships with industry and government, and maximize the societal impact of the transformative data science research conducted at Georgia Tech.


Related links:

Institute for Data Engineering and Science (IDEaS)

M.S. in Analytics


CSE Faculty specializing in Data Science and Visual Analytics research: