Social and Decision Analytics

Gizem Korkmaz

  • Research Associate Professor
Gizem Korkmaz headshot
Contact
434-243-7334

Bio

Gizem Korkmaz is an Associate Professor at the Social and Decision Analytics Division (SDAD) of the Biocomplexity Institute and Initiative at the University of Virginia. She is also an Adjunct Assistant Professor in the Department of Agricultural and Applied Economics at Virginia Tech. Her research focuses on social and economic networks, involving mathematical and computational modeling, and empirical analysis. Gizem received her PhD in Economics at the European University Institute in 2012. Her PhD dissertation spans game theory and network theory; focuses on the interplay between the network structure and strategic decision-making.

She is the principal investigator (PI) of the 2016 Minerva research project titled "The Dynamics of Common Knowledge on Social Networks: An Experimental Approach." She is also Co-PI of two projects as part of DARPA programs: Computational Simulation of Online Social Behavior (SocialSim) and Next Generation Social Science (NGS2). She was selected as the 2016 Outstanding New Faculty by Virginia Tech Northern Capital Region Faculty Association. During the postdoctoral research position at the Network Dynamics and Simulation Science Laboratory, she contributed to the EMBERS project, as part of IARPA's (The Intelligence Advanced Research Projects Agency) Open Source Indicator program. She developed network-based and statistical models that use multiple data sources such as social media including Twitter, news/blogs in order to predict critical societal events (protests, strikes) and election results in targeted Latin American countries.

The hallmark of her research is to blend her knowledge in traditional economics with big data using tools from social network analysis and machine learning. She works with traditional as well as novel data sources (e.g., social media, 911, Fire/EMS, patient records, census) to ask how we can make data useful for people and communities.

CV

    • Social and Economic Networks
    • Game Theory
    • Behavioral Economics
    • Social and Decision Analytics
    • Industrial Organization
    • Applied Microeconomic Theory
    • R&D Economics
    • Innovation
    • Collective Action
    • Social Contagion
    • Social Media Analytics
    • Political Economy
    • Policy Analytics
    • Outstanding New Faculty Award, Virginia Tech National Capital Region Faculty Association, Dec. 2016.
    • Full Scholarship Award by the National Economists Club (NEC) for the National Association for Business Economics (NABE) Economic Policy Conference, Washington D.C., Mar. 2017
    • Contributing Faculty, “Early Model Based Event Recognition using Surrogates (EMBERS) Software” won Inventors of the Month, The Office of the Vice President for Research and Innovation, Virginia Tech, Nov. 2016.

  • Ph.D., Economics, European University Institute (EUI), Florence - Italy
        Thesis: "Network Structure Matters: Applications to R&D Collaboration, Collusion, and Online Communication
        Networks" Advisor: Prof. Fernando Vega-Redondo

    M.A., Economics, Boğaziçi University, Istanbul - Turkey

    M.Sc., Economics, European University Institute (EUI), Florence - Italy

    B.A. with Honors, Economics, Boğaziçi University, Istanbul - Turkey

  • Co-director of the DSPG Young Scholars Program.

Leveraging Sociological, Economic and Psychological Theory for Simulating At-Scale Online Information Propagation

A simulation of the spread and evolution of online information, if accurate and at-scale, could enable a deeper and more quantitative understanding of use of the global information environment than is currently possible using existing approaches. High-fidelity (i.e., accurate, at-scale) computational simulation of the spread and evolution of online information would support efforts to analyze strategic disinformation campaigns, deliver critical information to local populations during disaster relief operations, and could potentially contribute to other critical missions in the online information domain.
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Resilient Societies and Interdependent Infrastructures,Social and Behavioral Network Science,Social, Cognitive, and Behavioral Science
Busy New York scene

Three-State Data Science for the Public Good Innovation Network

The three-state Innovation Network was a first step to forge data science partnerships with Cooperative Extension professionals and stakeholders in communities across Virginia, Oregon, and Iowa. Research faculty and Cooperative Extension professionals worked closely with DSPG young scholars on issues facing rural communities including food security, health, nutrition, youth growth and development, forestry, agriculture, and natural resource management.
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Community Well-being
Night sky

Attitudes of American Soldiers During WWII

During World War II, the U.S. Army conducted surveys to reveal attitudes toward, and between Black and White Soldiers. These responses hold insights regarding attitudes about race, gender, and family roles of the time. Our research team used computational text analysis and social network analysis of handwritten responses to learn about the dynamics and language of soldiers in the 1940’s.
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National Well-being
Old written document from survey
Social and Decision Analytics
Article

Doing Data Science: A Framework and Case Study

Keller SA; Shipp SS; Schroeder AD; Korkmaz G . Harvard Data Science Review. 2020; 2(1)
Social and Decision Analytics
Article

Modeling the Social and Spatial Proximity of Crime: Domestic and Sexual Violence Across Neighborhoods

Kelling C; Graif C; Korkmaz G; Haran M . Journal of Quantitative Criminology. 2020;
Social and Decision Analytics
Research Paper

Modeling the Impact of Python and R Packages Using Dependency and Contributor Networks

Korkmaz G; Kelling C; Robbins C; Keller S . Social Network Analysis and Mining (SNAM). 2019; 10:7
Social and Decision Analytics
Article

Spatial Modeling Response Time to Structure Fires

Arnsbarger M; Goldstein J; Kelling C; Korkmaz G; Keller S . The American Statistician. 2019; :92-100
Network Systems Science and Advanced Computing | Social and Decision Analytics
Conference Proceeding

Coordination and Collective Action on Communication Networks

Korkmaz G; Capra M; Kraig A; Kuhlman CJ; Lakkaraju K; Vega-Redondo F . Proceedings of the 17th ACM International Conference on Autonomous Agents and Multi-agent Systems (AAMAS). 2018; :1062-1070
Social and Decision Analytics
Conference Proceeding

Opportunities to Observe and Measure Intangible Inputs to Innovation: Definitions, Operationalization, and Examples

Keller S; Korkmaz G; Robbins C; Shipp S . Proceedings of the National Academy of Sciences (PNASs). 2018; 115(50):12638-12645
Network Systems Science and Advanced Computing | Social and Decision Analytics
Conference Proceeding

Combining Heterogeneous Online Data for Civil-Unrest Forecasting

Korkmaz G; Cadena J; Kuhlman C; Marathe A; Vullikanti A; Ramakrishnan N . ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015. 2015; :258-265
Network Systems Science and Advanced Computing | Social and Decision Analytics
Article

Forecasting Social Unrest Using Activity Cascades

Cadena J; Korkmaz G; Kuhlman C; Marathe A; Ramakrishnan N; Vullikanti A . PLoS ONE. 2015; 10(6):e0128879
Network Systems Science and Advanced Computing | Social and Decision Analytics
Conference Proceeding

"Beating the News" with EMBERS: Forecasting Civil Unrest using Open Source Indicators

Ramakrishnan N; Butler P; Muthiah S; Self N; Khandpur R; Saraf P; Wang W; Cadena J; Vullikanti A; Korkmaz G; Kuhlman C; Marathe A; Zhao L; Hua T; Chen F; Lu CT; Huang B; Srinivasan A; Trinh K; Getoor L; Katz G; Doyle A; Ackermann C; Zavorin I; Ford J; Summers K; Fayed Y; Arredondo J; Gupta D; Mares D . In Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2014; :1799-1808
Network Systems Science and Advanced Computing | Social and Decision Analytics
Conference Proceeding

Collective Action through Common Knowledge Using a Facebook Model

Korkmaz G; Kuhlman CJ; Marathe A; Marathe MV; Vega-Redondo F . In Proceedings of the 13th International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2014). 2014;
Turkey cityscape
Data Science for the Public Good

The University of Virginia’s Biocomplexity Institute Marks Global Expansion of its Data Science for the Public Good Young Scholars Program with MOU Among Entities in Turkey

The University of Virginia’s Biocomplexity Institute announces that it has entered into a Memorandum of Understanding (“MOU”) with five entities in Turkey, establishing the Data Science for the Public Good (DSPG) Young Scholars program in Istanbul and Ankara, Turkey’s capital city.

Student presenting
Data Science for the Public Good

Biocomplexity Institute Helps Launch First All-Virtual Data Science for Public Good Young Scholars Program

Over the last five summers, students from across the country have come together at the Biocomplexity Institute’s Arlington, Va. location for the Data Science for the Public Good (DSPG) Young Scholars program – an 11-week immersive research program through which students gain world-class, hands-on professional experience combining data science and public service. This year, as the world continues to evolve due to the COVID-19 pandemic, the program has followed suit, adopting an entirely virtual format for the first time.