Data Science Core

"Big Data Analytics for Environmental Health Decisions in Emergency Response"

One of the essential components of the Texas A&M Superfund Research Center, the Data Science Core serves as basis for translating the data produced by the four research projects into useful knowledge for the community via data collection, quality control, analysis, and model generation. This core is utilizing state-of-the-art methods in data science, optimization, and machine learning; is developing and applying novel dimensionality reduction techniques; is maximizing productivity within the center establishing an ideal environment for data sharing and collaboration; and is establishing a platform for data dissemination for the center’s stakeholders. Directed by Stratos Pistikopoulos, in collaboration with co-investigator Fred Wright, the core contributes to achieving the center’s goals by supporting the work of the four interconnected, challenging research projects by coordinating data processing and integration.

 

Leadership:



Specific aims:

  1. Apply expertise and support in state-of-the-art methods in data science, optimization, and machine learning to all projects of the center at various stages of their completion.
  2. Develop and improve high-performance methods for simultaneous classification and regression with dimensionality reduction for use in multiple projects of the center.
  3. Develop and maintain a computational platform for collaboration and dissemination of methods across all projects of the center and to the wider community.