Funded Research Projects since 2010

Faculty Sponsor on Zhuofan Li‘s Doctoral Dissertation Research Improvement Grant: Closed Corporations? Opening Science? Relational Work on the Knowledge Infrastructure of Artificial Intelligence Research. Awarded by the American Sociological Association (ASA) with financial support from the National Science Foundation (NSF), 2023-2024.

As Dr. Zhuofan Li writes, “What is most puzzling about the rapid commercialization of artificial intelligence today is that it appears to be coupled with an unprecedented rise in open science. The post-WWII paradigm of science and scientific funding maintained porous yet definite boundaries between proprietary and public science. But today, corporate laboratories, especially in the U.S. and China, have become leading contributors to the infrastructure of open science. This project draws on the prototypical case of computer vision research to ask: What are the main forms of corporate participation in open science? How have U.S. and Chinese companies participated, and to what extent have they occupied brokerage positions in the open science of artificial intelligence? How does corporate open science shape the interorganizational, transnational flows of labor, knowledge, and research instruments? How does corporate open science shape the careers, practices, and identities of researchers? I combine longitudinal network analysis, computational text analysis, a unique dataset of digital traces left by global computer vision research from 2013 to 2021, and participant observations and in-depth interviews with corporate and academic researchers in Beijing and Silicon Valley, to study how market actors reengineer human knowledge and knowledge infrastructures in the age of open science and platform capitalism.”

Predicting Rare Events by Reducing Unlikely Cases and Decomposing Statistical Models. Awarded by the Intelligence Advanced Research Projects Activity (IARPA), 2018-2019.

Eric Schoon, Principal Investigator. Co-Principal Investigators: Ronald Breiger and David Melamed. We formulate a new multi-method approach for the analysis of rare events. We develop methods to reduce the pool of possible candidate cases for likely incidents of rare events, develop methods to decompose the results of statistical models aimed at estimating effects associated with the prediction of rare events, and use Monte Carlo simulation methods to establish the statistical generalizability of our findings. This work contributes basic research insights leading to increased accuracy for rare events monitoring.

New Analytic Methods for the Exploitation of Open-Source Structured Databases on the Pursuit of WMD Terrorism. Basic Research Grant awarded by the Defense Threat Reduction Agency (DTRA), 2010-2016. ==> A brief 2016 article overviews several of the main research lines pursued. Click to read.

Ronald L. Breiger, Principal Investigator. Co-Principal Investigators: Gary Ackerman, Victor Asal, H. Brinton Milward, R. Karl Rethemeyer. This is a project of the University of Arizona in partnership with the University at Albany-SUNY and the National Consortium for the Study of Terrorism and Responses to Terrorism (START) at the University of Maryland. The research seeks to enhance and leverage existing open-source datasets on violent non-state actors in order to develop new analytical tools to model human networks engaged in the pursuit of chemical, biological, radiological, and nuclear (CBRN) weapons.

Unifying Approaches to Adversarial Modeling. Department of Homeland Security (DHS), Science and Technology Directorate, Office of University Programs Award Number 2012-ST-061-CS0001, Project 3.4, made to the National Consortium for the Study of Terrorism and Responses to Terrorism (START). 2015-2017.

Ronald L. Breiger, H. Brinton Milward, and Charles Ragin, Principal Investigators. The objective of this project is to bridge and unify important aspects of quantitative and qualitative approaches so as to predict relevant phenomena such as the impact of specific ideological components on propensity for attacks of specific types (defined both by target and method), as well as, simultaneously, to identify specific groups most likely to engage in particular types of adversarial activities.

Securing Cyber Space: Understanding the Cyber Attackers and Attacks via Social Media Analytics. Research grant awarded  by the National Science Foundation (NSF), Program on Secure and Trustworthy Cyberspace (SaTC), 2013-2018. Click here for grant website.

Hsinchun Chen, Principal Investigator. Co-Principal Investigators: Ronald L. Breiger, Salim Hariri, Thomas Holt. Web mining and machine learning technologies are used in tandem with social science methodologies to study hacker community structure, content, and behaviors; markets; artifacts; and cultural differences.

Human-Centric Predictive Analytics of Cyber-Threats: A Temporal Dynamics Approach. Early-concept Grant for Exploratory Research (EAGER) awarded by the National Science Foundation (NSF), Program on Secure and Trustworthy Cyberspace (SaTC), 2013-2016. Click here for grant website.

H. Brinton Milward, Principal Investigator.  Co-Principal Investigators: Ronald L. Breiger, Loukas Lazos, Jerzy W. Rozenblit. The three major activities of this project are as follows: (a) comprehensive models of cyber-attack characteristics are developed using feature extraction techniques on diverse data sources, (b) adversarial groups are classified according to their feature similarities, and (c) social network models and tools, as well as case studies, are applied to infer adversarial group typology.

Inferring Structure and Forecasting Dynamics on Evolving Networks. Multidisciplinary University Research Initiative (MURI) Grant awarded by the Air Force Office of Scientific Research (AFOSR), 2010-2015. Click here for the project’s website.

Participating institutions are the University of California, Los Angeles; the University of Arizona; the University of Southern California; the University of California, Santa Barbara; the University of California, Irvine; and Claremont Graduate University.

Principal Investigator: P. Jeffrey Brantingham, UCLA. Co-Principal Investigators at the University of Arizona are Paul Cohen, Ronald Breiger, and H. Brinton Milward. The principal tasks of this project are development of stable metrics for inferring latent properties of social networks; forecasting of dynamical processes operating on evolving networks; and planning and predicting outcomes of interventions on network structure and function.