Big Data Management, Analysis, and Visualization

The organization and governance of large volumes of data. This concentration allows for retaining data obtained from a large number of sources—from a large city, to individuals, and anywhere in between—and ensures a high level of data quality for analytical purposes. The visualization of such data elegantly brings structure and simplicity to it.

Faculty:

Enrico  Bertini
Yi-Jen   Chiang
Rumi  Chunara
Harish  Doraiswamy
Semiha  Ergan
Juliana  Freire
Guido  Gerig
Claudio  Silva
Torsten   Suel
Paul  Torrens
Huy T. Vo

Labs: CUSP


Sample research projects:

Enrico Bertini, in conjunction with PhD students Christian Felix Da Silva and Anshul Pandey, developed RevEx. A collaboration with ProPublica, this tool visualizes Yelp data and has the ability to single out reviews under specific parameters or keywords. The tool can sort through millions of reviews at one time and visualize trends.

Learn more about RevEx and download the demo here

 

Guido Gerig, one of the department's newest professors, focuses on clinical neuroimaging to assist in studying such areas as early childhood brain development, children with autism, and infants at risk of schizophrenia. His methodology includes image processing, registration, atlas building, segmentation, shape analysis, and statistical analysis.

In this related paper, Gerig studies the early developing brain by displaying the longitudinal MRI scans of the same subject's brain at various ages, from two weeks to two years.

See more of the study here

 

Figure graphing the prevalence of activity-related interests and obesity in the US.

Figure graphing the prevalence of activity-related interests and obesity in the US.

Assistant Professor Rumi Chunara works at the intersection of Big Data and Public Health, using information gleaned from social media sites like Facebook and Twitter to predict epidemics, track obesity rates on a local level, and much more.

In Prof. Chunara's research on US obesity rates, for example, Facebook is used to cross-measure user interests and obesity prevalence within certain metroplitan populations. Activity-related interests across the US and sedentary-related interests across NYC were significantly associated with obesity prevalence.

Learn more about Prof. Chunara's study here

 

Semiha Ergan, an affiliate professor of the Computer Science and Engineering Department, is responsible for a project that performs data analysis on highly sensed buildings for understanding patterns in building performance. The data deals with HVAC systems and energy use in such buildings to assist in building management strategies.

Prof. Ergan is also the head of the Future Building Informatics and Visualization Lab (biLab).

Learn more about the project here