Teaming for Interdisciplinary Research Pre-Seed Program

The demand for employees with data science expertise has risen sharply across all sectors of the U.S. economy. Yet, despite the best efforts of universities, industry, and government, the development of data science research and education programs faces significant challenges, including the lack of 1) deep understanding of the theoretical foundation of data science methods and techniques, 2) effective data science instruction and mentoring, and 3) a community and ecosystem for support and development. To surmount these challenges, we propose to develop the Harvard-UGA Collaborations on the Foundations of Data Science (HGCFODAS). The HGCFODAS fosters and supports: 1) interdisciplinary research and collaboration among mathematics, statistics, computer science, and electrical engineering as well as other arts, science, engineering fields to propel research development in data science; 2) interdisciplinary education and training efforts to provide a high quality workforce in data science; and 3) out-reach and partnership building between academia, government and industry to foster sustainable development in data science.

The foundations of data science lie at the intersection of four research fields: theoretical computer science, statistics, mathematics, and electrical engineering. A major challenge is that each of these largely-distinct disciplines has been built based on different cultures in the use of data science to reach conclusions from data. In this project, we will borrow strengths from all four distinct cultures, adopt a diverse set of tools, and develop a coherent foundation of data science to tackle the grand challenges.

Team Lead

Ping Ma
Department of Statistics
pingma@uga.edu

Team Members

Wenxuan Zhong
Department of Statistics

Tianming Liu
Department of Computer Science

TN Sriram
Department of Statistics

Yuan Ke
Department of Statistics

WenZhan Song
School of Electrical and Computer Engineering

Jin Ye
School of Electrical and Computer Engineering

Qian Xiao
Department of Statistics

Pengsheng Ji
Department of Statistics

Ray Bai
Department of Statistics