Kathleen M. Carley is a professor in the School of Computer Science in the department - Institute for Software Research - at Carnegie Mellon University. She also has courtesy appointments at Engineering and Public Policy , Heinz School, Electrical and Computer Engineering and GSIA.

She has an H.D. from the University of Zurich in Business, Economics and Informatics; a Ph.D. from Harvard University in Sociology; and two S.B.’s from Massachusetts Institute of Technology – one in Political Science and one in  Economics.

She is the director of the Center for Computational Analysis of Social and Organizational Systems (CASOS), a university wide interdisciplinary center that brings together network analysis, computer science, and organization science (www.casos.cs.cmu.edu) and the director of the Center for Informed Democracy and Social-cybersecurity (IDeaS). 

Kathleen M. Carley's research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, information and disinformation diffusion, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks, social media analytics tools and various agent-based simulation systems. These tools include: ORA, a graphical and statistical toolkit for analyzing and visualizing social networks, semantic networks and any high dimensional networks. AutoMap, a text-mining system for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks. OrgAhead an agent-based model of organizational performance.  And Construct, an agent-based model of information spread and belief formation. 

She is the founding co-editor of the journal Computational and Mathematical Organization Theory which she now co-edits with Dr. Terrill Frantz. She has co-edited several books in the computational organizations and dynamic network area.  She has also served on numerous panels for the National Academy of Science.

Alex Davis is an Associate Professor in the Department of Engineering and Public Policy at Carnegie Mellon University. His research is at the intersection of behavioral science, technology, and public policy, with diverse applications to health, energy, climate change, the environment, and innovation. Specific recent work focuses on integrating expert judgment with machine learning to solve challenging technical problems, eliciting individual and group preferences, and using risk analysis, communication, and digital health to improve perinatal outcomes for pregnant individuals. He teaches graduate courses in applied data analysis (19-704) and stochastic discrete choice models (19-786). Alex earned his B.S. from Northern Arizona University in Psychology (2007) and his M.S. (2009) and Ph.D. (2012) from Carnegie Mellon University in Behavioral Decision Research.  He worked as a postdoctoral fellow and research scientist at Carnegie Mellon University prior to joining the faculty at Carnegie Mellon.

Conrad Tucker is an Arthur Hamerschlag Career Development Professor of Mechanical Engineering and holds courtesy faculty appointments in machine learning, robotics, and biomedical engineering at Carnegie Mellon University. His research focuses on the design and optimization of systems through the acquisition, integration, and mining of large scale, disparate data.

Tucker has served as PI/Co-PI on federally/non-federally funded grants from the National Science Foundation (NSF), the Air Force Office of Scientific Research (AFOSR), the Defense Advanced Research Projects Agency (DARPA), the Army Research Laboratory (ARL), the Office of Naval Research (ONR) via the NSF Center for eDesign, and the Bill and Melinda Gates Foundation (BMGF). In February 2016, he was invited by National Academy of Engineering (NAE) President Dr. Dan Mote, to serve as a member of the Advisory Committee for the NAE Frontiers of Engineering Education (FOEE) Symposium. He received his Ph.D., M.S. (industrial engineering), and MBA degrees from the University of Illinois at Urbana-Champaign, and his B.S. in mechanical engineering from Rose-Hulman Institute of Technology.

Christine Lepird is a Societal Computing PhD student at Carnegie Mellon University where I’m advised by Dr. Kathleen Carley in CASOS.  Prior to CMU, she worked for the Johns Hopkins University Applied Physics Lab where she earned my masters in Computer Science at night and as a data scientist at Wayfair where she created recommender systems. Christine earned her BS from MIT where she double majored in Math and Management Operations Research. 

In her spare time, she enjoys swimming with her Masters team, doing house repair projects, and spending time with her dog, husband, and two cats. She is originally from Media, Pennsylvania. 

Description of Research:

Christine's primary area of research is in social network analysis - a field of study that investigates social structures through ties of various strengths. I investigate suspicious network behavior on platforms like Reddit and Facebook in order to differentiate between organic communities and malicious networks that post fake news.

Thomas Magelinski is a fourth-year PhD. Student in Societal Computing advised by Professor Kathleen Carley. His research falls at the intersection of Network Science, Dynamical Systems, and Machine Learning. Specifically, his work seeks to leverage network dynamics to better understand social media discourse. He received an ARCS foundation scholarship in 2017 to support this work. He obtained the Honors Baccalaureate Degree in Engineering Science and Mechanics from Virginia Tech in 2017, with minors in Math and Physics. His undergraduate thesis focused on computational models of military air drop systems.

Description of Research:

Tom is currently working on developing and applying dynamic community detection methods for multipartite networks to Twitter discourse. The goal is to find and characterize communities propagating misleading information. Specifically, he is the investigating the spread of untrustworthy news articles in the COVID-19 discussion, and the Reopen America campaign.

Joshua Uyheng is a third-year PhD student in societal computing at Carnegie Mellon University. He is advised by Dr. Kathleen Carley at the Center for Computational Analysis of Social and Organizational Systems (CASOS). His research combines machine learning, network science, and social and cognitive psychology to examine the large-scale dynamics of hate speech and disinformation on social media platforms. He is especially interested in comparative work focused on the Asia-Pacific and the Global South. Previously, he has conducted research on human-in-the-loop modelling and computational social systems at Heidelberg University and the Nara Institute of Science and Technology. He holds an MS in societal computing from Carnegie Mellon University, and undergraduate degrees in mathematics and psychology from the Ateneo de Manila University. He hails from the Philippines.

Description of Research:

Josh’s current research uses social identity theory and constructural theory to conceptualize online hate speech in terms of the narrative and network dynamics of groups. His work develops computational methods which characterize the prevalence and targets of hate speech, the properties of hateful communities, and how these processes are exploited by bots and trolls in the context of information operations. He specifically investigates these issues across diverse online conversations around national elections, natural disasters, social unrest, and the COVID-19 pandemic. Through his research, he aims to expand global and interdisciplinary understandings of the problem of hate speech, and ultimately illuminate new directions for making digital platforms more resilient to violence and conflict.