Social and Decision Analytics

Gizem Korkmaz

  • Research Associate Professor
Gizem Korkmaz headshot
Bio

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 Ph.D. in Economics at the European University Institute in 2012. Her Ph.D. dissertation spans game theory and network theory; focuses on the interplay between the network structure and strategic decision-making.

She contributed extensively to network science, game theory, social network analysis, text analysis, and behavioral economics. Network science contributions (both mathematical modeling and empirical analysis) include applications in collaboration networks (e.g., GitHub), online social networks (e.g., Twitter, Facebook), communication networks, among others. She is the principal investigator (PI) of the Minerva Initiative Research grant titled, "The Dynamics of Common Knowledge on Social Networks: An Experimental Approach," funded by the U.S. Air Force Office of Scientific Research. She was 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 (now UVA Biocomplexity Institute’s Network Systems Science and Advanced Computing division), Dr. Korkmaz developed network-based statistical models that use multiple data sources such as social media including Twitter, news/blogs to predict critical societal events (protests, strikes) in targeted Latin American countries as part of IARPA's (Intelligence Advanced Research Projects Agency) Open Source Indicator program. Activities resulted in publications in journals and peer-reviewed proceedings, and three Intellectual Properties (“Volume-Based Model for Protest Forecasting;” “Social Media Activity and Recruitment Based Model for Protest Forecasting;” “Civil Unrest Vocabulary.” Disclosure Date: 10/26/2016).

Supported by the National Center for Science and Engineering Statistics (NCSES) at the NSF, Dr. Korkmaz has been leading research projects focused on measurement of innovation activities, in particular of open source software innovation and business innovation using non-survey opportunity data (online registries, repositories, news articles, and financial statements of companies and databases). Current findings have been published in journals including the Proceedings of the National Academy of Sciences (PNAS), and presented at Harvard University Digital Initiative Seminar Series, and international conferences such as the General Conference of the International Association for Research in Income and Wealth (IARIW), and the National Bureau of Economic Research (NBER) Conference on Research in Income and Wealth (CRIW) on Big Data for 21st Century Economic Statistics.

The hallmark of her research is to integrate her background in economics with data science by blending traditional and novel data sources (e.g., social media) and methods (e.g., network analysis, machine learning) to ask how we can make data useful for people and communities. She is passionate about the development of a workforce using data science to contribute to community-based research. She is the co-director of UVA’s Data Science for the Public Good Young Scholars Program and led the of launch a Young Scholars program in Turkey.

CV

    • Social and Economic Networks
    • Game Theory and Behavioral Economics
    • Innovation Statistics and Measurement of Intangibles
    • 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.

    • National Science Foundation (NSF) EAGER grant (Co-PI): "Improving the Quality and Reducing the Burden of Producing and Reusing Publicly Accessible Research Data." PI: Nusser, S. (Iowa State University)
    • Grant by U.S. Consulate in Turkey to launch Data Science for the Public Good Young Scholars Program in Turkey.
    • Minerva Initiative Research grant (PI) “The Dynamics of Common Knowledge on Social Networks: An Experimental Approach.” funded by U.S. Air Force Office of Scientific Research
    • National Science Foundation (NSF) funding (Co-PI): “Use of Statistical and Survey Methodology Research to Improve or Redesign Surveys.” PI: Keller, S.
    • Defense Advanced Research Projects Agency (DARPA) grant (Co-PI): Simulation of Online Social Behavior (SocialSim) “Homo SocioNeticus: Scaling the Cognitive Foundations of Online Social Behavior.” PI: Orr, M. (University of Virginia)
    • U.S Department of Agriculture grant (Co-PI): “FACT: Three-State Data Science for the Public good Coordinated Innovation Network.” PI: Keller, S.
    • National Endowment for the Humanities grant (Co-PI): “The American Soldier in World War II.” PI: Gitre, E. (Virginia Tech)
    • Defense Advanced Research Projects Agency (DARPA) grant (Co-PI): Next Generation Social Science (NGS2) “Montage: Capturing Collective Behavior with Modeling and Experimentation.” PI: Ramakrishnan, N. (Virginia Tech)
    • Army Research Institute (ARI) for the Behavioral and Social Sciences grant (Co-PI): “Towards an Integrated Data Framework for Understanding the Context of Military Environments.” PI: Keller, S.
    • Virginia Tech Center for Peace Studies and Violence Prevention (CPSVP) grant (Co-PI): “Measuring the Impact of Alcohol-Related Crime Reduction Strategies for Restaurants and Nightlife in Arlington.” PI: Zhang, W. (Virginia Tech)
    • Research Funding by Procter & Gamble (PI): “P&G Sankey Visualization for United Kingdom & North America (NA) Shipments" and "Initiative Launch Inventory Analytics for Beauty Care" (Co-PI)
Projects
This multiple year research project aims to provide a deep understanding of individual and group behavior as people communicate through online social networks when deciding to participate in a risky collective action.
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Social and Behavioral Network Science
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
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
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
Selected Publications
Social and Decision Analytics
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
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
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
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
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
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
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;
In the News
Data Science for the Public Good

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.

Data Science for the Public Good

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.

Korkmaz