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Jana Diesner
Assistant Professor

Jana Diesner
Jana Diesner is an Assistant Professor at the iSchool at the University of Illinois at Urbana-Champaign. She earned her PhD from Carnegie Mellon University, School of Computer Science, in the Computation, Organizations and Society (COS) Program.

Jana conducts research at the nexus of network science, natural language processing and machine learning. Her research mission is to contribute to the computational analysis and better understanding of the interplay and co-evolution of information and the structure and functioning of socio-technical networks. She develops and investigates methods and technologies for extracting information about networks from text corpora and considering the content of information for network analysis. In her empirical work, she studies networks from the business, science and geopolitical domain. She is particularly interested in covert information and covert networks.

Homepage
iSchool page

Contact

Phone: 217-244-3576
Email: jdiesner@illinois.edu

CIRSS Publications

Evans, C., Diesner, J. and Blake, C. (2015), Email Data Analysis as an Alternate Lens into Historical Events, HASTAC Conference,May 27-30, 2015, Lansing, MI,. Read more

Diesner, J., Evans, C., & Kim, J. (2015, April). Who Are They, and If So, How Many? Propagation of Entity Disambiguation Errors to Network Analysis Results. Presentation at the 2015 GSLIS Research Showcase. Read more

Diesner, J., Pak, S., Kim, J., Soltani, K., & Aleyasen, A. (2014, March). Computational Assessment of the Impact of Social Justice Documentaries. Paper presented at iConference 2014, Berlin, Germany. Read more

Past CIRSS Events

October 14, 2015
Large scale data analysis: Simpson's paradox, data integrity, and reproducibility.
Abstract:We consider factors that can give rise to unreliable or unstable outcomes in empirical research. For example, seemingly small differences in data preparation, consideration of outliers and mi…

November 19, 2014
Who are they? And if so, how many? Impact of social entity disambiguation on network analysis results and robustness of metrics
We identify the impact of errors in node disambiguation on properties of social networks, the robustness of network metrics towards these techniques and likely caused biases in the interpretation of a…

April 30, 2014
Discussion session -- Obtaining funding from foundations, industry and new sponsors
As government sources of grant funding shrink, researchers may look to expand their pool of sponsors. GSLIS faculty who have obtained research funding from foundations, industry and other sponsors le…