Assistant Professor and PhD Program Director
PhD, Computer Science, Carnegie Mellon
jdiesner [at] illinois.edu
Office HoursThursdays 3:00-5:00 p.m.
Human-centered data science, network analysis, natural language processing, machine learning, data regulations
Other Professional AppointmentsFaculty Affiliate, Department of Computer Science
Faculty Affiliate, Information Trust Institute (ITI)
Faculty Affiliate, Illinois Informatics Institute (I3)
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.
Diesner 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.
Diesner was a 2015-2016 faculty fellow in the National Center for Supercomputing Applications (NCSA) at Illinois and is a 2016 Dori J. Maynard Senior Fellow.
Assistant Professor Jana Diesner a received an Faculty Fellowship and seed funding for her project, “Predictive Modeling for Impact Assessment,” from the National Center for Supercomputing Applications (NCSA). Diesner collaborates closely with NCSA scientists on the project, which builds on her work developing computational solutions to assess the impact of issue-focused information projects such as social justice documentaries and...
Films are produced, screened and perceived as part of a larger and continuously changing ecosystem that involves multiple stakeholders and themes. This project will measure the impact of social justice documentaries by capturing, modeling and analyzing the map of these stakeholders and themes in a systematic, scalable and analytically rigorous fashion.
Diesner’s team is developing a natural-language processing solution for probabilistic entity detection and classification in the domain of healthcare. The core of the solution are prediction models built by using supervised and/or semi-supervised machine learning techniques. The resulting models can be used to annotate natural language text data documents for entity classes. The team will perform fact extraction from medical text data documents as well as map tokens to predefined medical...
How do limitations and intransparencies in data quality and data provenance bias research outcomes, and how can we detect and mitigate these limitations? For example, we have been investigating the impact of entity resolution errors on network analysis results. We found that commonly reported network metrics and derived implications can strongly deviate from the truth—as established based on gold standard data or approximations thereof—depending on the efforts dedicated to entity resolution...
How can we use user-generated content to construct, infer or refine network data? We have been tackling this problem by leveraging communication content produced and disseminated in social networks to enhance graph data. For example, we have used domain-adjusted sentiment analysis to label graphs with valence values in order to enable triadic balance assessment. The resulting method enables fast and systematic sign detection, eliminates the need for surveys or manual link labeling, and...
How can we be rule compliant and still innovate? The collection and analysis of human-centered and/ or data are governed by multiple sets of norms and regulations. Problems can arise when researchers are unaware of applicable rules, uninformed about their practical meaning and compatibility, and insufficiently skilled in implementing them. We are developing and delivering educational modules to address this issue.
How accurate and suitable are current solutions? How can they be improved? We evaluate the coverage and accuracy of various medical terminologies, and test strategies for increasing the precision of mapping medical reports to standardized terminologies.
Completed Research Projects
In the News
Jun. 19, 2017
May. 24, 2017
Mar. 15, 2017
Feb. 22, 2017
Feb. 17, 2017