PNNL

Maria Glenski is a data scientist and leader of the Foundational Data Science group in the National Security Directorate at Pacific Northwest National Laboratory. Her research focuses on computational social science approaches to model, characterize, and explain complex behavior in diverse online social environments and the impacts of biases in machine learning/artificial intelligence models, particularly for deception detection.

Glenski’s research has been published in top-tier venues and interdisciplinary venues, such as the Comparative Approaches to Disinformation workshop at Harvard University in 2019. She has co-organized a tutorial on measuring information spread within and across social platforms at the Association for the Advancement of Artificial International Conference on Weblogs and Social Media (ICWSM) and contributed a chapter to the 2020 Springer book on “Disinformation, Misinformation, and Fake News in Social Media – Emerging Research Challenges and Opportunities.” She regularly serves on the program committee of several international conferences, including as an area chair for the Women in Machine Learning workshop, and has been recognized as an outstanding reviewer (ICWSM 2019).

Glenski received her PhD in computer science from the University of Notre Dame, where she was an Arthur J. Schmitt Leadership in Science and Engineering Fellow and a member of the Interdisciplinary Center for Network Science and Applications. Her dissertation focused on understanding and explaining how social media users engage with and propagate information from social news sources of varied credibility or deception.

• Artificial Intelligence
• Causal Analysis
• Data Science
• Deception Detection
• Deep Learning
• Machine Learning
• Network Science
• NLP
• Social Media

• PhD in computer science, University of Notre Dame
• MS in computer science and engineering, University of Notre Dame
• BA in mathematics, Saint Mary's University of Minnesota