Gizem Korkmaz is a Research Assistant Professor at the Social and Decision Analytics Lab (SDAL) at Biocomplexity Institute of Virginia Tech. She is also an Adjunct Faculty at the Department of Economics at Virginia Tech. Her research focuses on social and economic networks, involving mathematical and computational modeling, and empirical analysis.
She is the principal investigator (PI) of 2016 Minerva research project titled "The Dynamics of Common Knowledge on Social Networks: An Experimental Approach." She was selected as the 2016 Outstanding New Faculty by Virginia Tech Northern Capital Region Faculty Association.
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.
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. The postdoctoral research position at the Network Dynamics and Simulation Science Laboratory complemented her theoretical background with empirical research. 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.
Selected Peer-Reviewed Publications
Korkmaz, G., C.J. Kuhlman, S.S. Ravi, F. Vega-Redondo. 2017. "Spreading of Social Contagions without Key Players." World Wide Web. pp. 1-35.
Ziemer, K. and G. Korkmaz. 2017. "Using Text to Predict Psychological and Physical Health: A Comparison of Human Raters and Computerized Text Analysis." Computers in Human Behavior. 76: 122-127.
Pires, B., J. Goldstein, D. Higdon. S. Reese, P. Sabin, G. Korkmaz et al. 2017. "A Bayesian Simulation Approach for Supply chain Synchronization." (to appear) Proceedings of Winter Simulation Conference (WSC) 2017.
Molfino, E., G. Korkmaz, S.A. Keller, A. Schroeder, S. Shipp, D.H. Weinberg. 2017. "Can Administrative Housing Data Replace Survey Data?" Cityscape: A Journal of Policy Development and Research. 9:1. pp. 265-292.
Keller, S.A., G. Korkmaz, M. Orr, A. Schroeder, S. Shipp. 2017. "The Evolution of Data Quality: Understanding the Transdisciplinary Origins of Data Quality Concepts and Approaches." Annual Review of Statistics and Its Applications 2017. 4:5.1-5.24.
Korkmaz, G., C.J. Kuhlman, F. Vega-Redondo. 2016. "Can Social Contagions Spread without Key Players?" In Proceedings of 2016 IEEE International Conference on Behavioral, Economic and Socio-cultural Computing (BESC), Durham, NC, USA, 2016, pp. 1-6.
Korkmaz, G., J. Cadena, C.J. Kuhlman, A. Marathe, A. Vullikanti, N. Ramakrishnan. 2016. "Multi-source Models for Civil Unrest Forecasting." Social Network Analysis and Mining. 6:50.
Korkmaz, G., C.J. Kuhlman, S.S. Ravi, and F. Vega-Redondo. 2016. “Approximate Contagion Model of Common Knowledge on Facebook." In Proceedings of the 27th ACM Conference on Hypertext and Social Media (HT' 16). ACM, New York, NY, USA, 231-236.
Ziemer, K. and G. Korkmaz. 2016. “Human vs. Automated Text Analysis: Estimating Positive and Negative Affect." In Proceedings of the 27th ACM Conference on Hypertext and Social Media (HT' 16). ACM, New York, NY, USA, 309-314.
Korkmaz, G., J. Cadena, C.J. Kuhlman, A. Marathe, A. Vullikanti, N. Ramakrishnan. 2015. "Combining Heterogeneous Data Sources for Civil Unrest Forecasting." In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM). ACM, New York, NY, USA, 258-265.
Cadena, J., G. Korkmaz, C. J. Kuhlman, A. Marathe, N. Ramakrishnan, A.Vullikanti. 2015. "Forecasting Social Unrest Using Activity Cascades." PloS ONE 10.6 (2015): e0128879.
Pires, B., G. Korkmaz, K. Ensor et al. 2015. "Towards an in silico Experimental Platform for Air Quality: Houston, TX as a Case Study." Santa Fe. New Mexico; 2015. Computational Social Science Society of America Conference.
Korkmaz, G., C.J. Kuhlman, A. Marathe, M.V. Marathe, and F. Vega-Redondo. 2014. “Collective Action through Common Knowledge Using a Facebook Model.” In Proceedings of the 2014 ACM International Conference on Autonomous Agents and Multi-agent Systems, pp. 253-260.
N. Ramakrishnan, P. Butler, S. Muthiah, N. Self, R. Khandpur, P. Saraf, W. Wang, J. Cadena, A. Vullikanti, G. Korkmaz, et al. 2014. “’Beating the News’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators.” In Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Industrial Track), New York, 2014.
Cadena, J., Y. Keneshloo, G. Korkmaz, and N. Ramakrishnan. 2014. “Detecting and Forecasting Domestic Political Crises: A Graph-based Approach.” In Proceedings of the 2014 ACM Conference on Web Science, Bloomington, 2014.