Our approach is team-based translational research. Working in partnership with communities, academia, government and industry, our innovations apply statistical rigor and the creative application of social and decision analytics.
The Science of All Data
Today’s data revolution is not just about big data, it’s about data of all sizes and types. We are not limited by traditional quantitative techniques—we are “doing data science” to solve real-world problems. This allows us to frame the processes and infrastructure to create tools and techniques as we go. We call this The Research Pull.
Our cities are composed of many diverse neighborhoods. Through the lens of local data flows, CommunityScapes contextualize where we live, learn, work and play. They lay a foundation for a quantitative understanding of human health, habitat and well-being.
An evidence-based understanding is the cornerstone of effective policymaking. Working with local governments, we develop processes and platforms to support data-driven governance.
Social and Economic Networks
Networks play a central role in our social and economic lives, and affect our welfare by determining how we make economic, social and political decisions. They help understand information transmission, the exchange of goods and services, collaborations, conflicts and coordination among agents. We study how these interactions shape individual behavior and social systems.
Soldier Performance and Behaviors
The U.S. Army possesses vast amounts of administrative data on Soldiers but has yet to integrate these data to create a holistic operating picture. Through data science techniques, we are working to uncover the social characteristics of Soldier performance that drive Soldiers to successfully meet the challenges of rapid technological change for our country’s future needs.
We partner with federal statistical agencies to develop data science methods for the enhancement, improvement, or redesign of their current survey techniques. This includes discovering new data sources and repurposing those data to measure concepts such as innovation, entrepreneurship and competitiveness.
Data Science for the Public Good: Workforce Development
With a goal to build a data science workforce pipeline that is interested in civic engagement, this program focuses on “public good” projects. We engage young scholars in finding solutions to some of the most pressing social issues of our time.
(l-r)Kim Lyman, Josh Goldstein, Teja Pristavec, Lori Conerly, Devika Nair, Sallie Keller, Joy Tobin, Aaron Schroeder, Gizem Korkmaz, Stephanie Shipp, Vickie Lancaster (not pictured: Nathaniel Ratcliff)
Leveraging data to solve real-world problems requires a broad range of scientific expertise. The SDAD team includes thought leaders in a wide variety of fields: statistics, economics, information architecture, computational social science, and public policy.
Team Members include:
- Sallie Keller, Director and Professor of Public Health Sciences Research Interests: social and decision informatics, the statistical underpinnings of data science, and data access and confidentiality
- Stephanie Shipp, Deputy Director and Research Professor Research Interests: economic and demographic analyses of social issues, identifying novel approaches to measuring innovation and competitiveness, and evaluating public programs
- Gizem Korkmaz, Research Associate Professor, Economics Research Interests: conducting theoretical and empirical analysis of social and economic networks and combining traditional economics with big data using tools from social network analysis and machine learning
- Aaron Schroeder, Research Associate Professor, Data Science, Public Policy Research Interests: developing approaches, methods and platform related to the secure integration, storage, retrieval, sharing, and optimal use of public-sector administrative data
- Vicki Lancaster, Principal Scientist and Statistician Research Interests: applying statistical logic and methodologies to high-profile interdisciplinary investigations and presenting results using novel visualization approaches
- Joshua Goldstein, Research Assistant Professor, Statistician Research Interests: spatial modeling of infectious disease, Markov chain Monte Carlo methods, and generating synthetic populations
- Joy Tobin, Principal Scientist Research Interests: using information technology to improve the delivery of healthcare; applying technologies from other industries to healthcare; building partnerships to accomplish shared vision
- Devika Nair, Research Scientist
- Teja Pristavec, Research Associate Research Interests: quantitative methods, health, and inequality
- Kim Lyman, Project Associate
- Lori Conerly, Administrative Assistant