Computer systems are increasingly central to national infrastructure in the financial, medical, manufacturing, defense, and other domains. This infrastructure is at risk from sophisticated cyber-adversaries backed by powerful nation-states, whose capabilities rapidly evolve, demanding equally rapid responses. This calls for advances in artificial intelligence and autonomous reasoning that are tightly integrated with advanced security techniques to identify and correct vulnerabilities, detect threats and attribute them to adversaries, and mitigate and recover from attacks. The ACTION Institute will develop novel approaches that leverage artificial intelligence?informed by and working with experts in security operations?to perform security tasks rapidly and at scale, anticipating the moves of an adversary and taking corrective actions to protect the security of computer networks as well as people?s safety. The Institute will function as a nexus for the AI and cybersecurity communities, and its research efforts will be complemented by innovation in education from K-12 to postdoctoral students, the development of new tools for workforce development, and the creation of new opportunities for collaboration among the Institute?s organizations and with external industry partners.

The AI Institute will initiate a revolutionary approach to cybersecurity, in which AI-enabled intelligent security agents cooperate with humans across the cyber-defense life cycle to jointly improve the security posture of complex computer systems over time. Intelligent security agents will follow a new paradigm of continuous, lifelong learning both autonomously and in collaboration with human experts, supported by a shared knowledge bank and an integrated AI stack that provides novel fundamental primitives for (1) reasoning and learning that incorporates domain knowledge, (2) human-agent interaction, (3) multi-agent collaboration, and (4) strategic gaming and tactical planning. Over time, these intelligent security agents will improve their domain knowledge, becoming increasingly robust and effective in the face of changes in the adversaries? modes of operation, composing defense strategies and tactical plans in the presence of uncertainty, collaborating with each other and with humans for mutually complementary teaming, and adapting to unfamiliar and novel attacks.

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A key research challenge in human-AI systems is the current limitation in data, models and theories that explain their dynamic behavior, coordination, and performance. We do not yet fully understand the dominant socio-cognitive processes that determine the dynamic, adaptive, and learning behavior of human-AI teams.  Of especial interest are decision-making problems in intellective tasks with uncertainty and limited resources: what are the rational, efficient, or irrational strategies and heuristics that humans tend to adopt in such circumstances? Useful socio-cognitive models should inform the design of efficient AI agents that improve the overall human-AI team performance. In other words, empirically-validated models and theories are needed to model and build the human-AI teams of the future and to intervene when their performance deteriorates. The project's broad objective is the development and experimental validation of a theory of coordination of human-AI teams in complex intellective tasks.  We plan to combine fundamental insights and models of team behavior from social sciences with state-of-the-art machine learning and dynamical systems methods.  Specifically, our objective include: 1. modeling socio-cognitive structures in human-AI teams, including transactive memory systems, influence systems, and prospect theories; 2. identifying leading cognitive processes, heuristics and biases that underlie the formation of socio-cognitive structures and affect the accuracy of human-AI team decision making; 3. designing supervisory/coordinating AI agents in human-AI teams, based on concepts from applied psychology and machine learning, and testing/validating them in sequential, risky, uncertain decision making tasks; and 4. modeling how human-AI teams cope with limited training data acquired over short sessions, including how they react to various manipulations and intervention schemes.

Funded by: Army Research Office.

Predicting network structure and flows on edges is a precondition to effective planning and disaster response in critical infrastructure networks. Accordingly, we propose to develop algorithms inspired by network science, physical modeling, and machine learning to determine this information from incomplete observations. Using both physical domain-specific and data-driven approaches, these algorithms will address diverse problems including: the reconstruction of network topology and parameters, the estimation of network flows, the optimal selection and scheduling of multiple types of sensors, and the modeling of interdependencies in multilayer networks. Together these methods will develop a rigorous theory for solving the inverse problem of edge and flow determination from observations of critical infrastructure networks.

Funded by: Defense Threat Reduction Agency, Award# HDTRA1-19-1-0017

Due to the societal and technological advances made possible by data-driven science, there is a strong demand for professionals versed in the tools and techniques needed for manipulating and understanding data. This project will develop an undergraduate curriculum in data science that spans and connects the three main public higher education systems in California: the research-driven University of California system, the practical and career-oriented California State University system, and the two-year California Community Colleges. The collaborative program will establish pathways for data science training through coursework and real-world projects. This project will impact students from diverse social, ethnic, cultural, and economic backgrounds and will improve the feeder pipelines from two-year colleges to four-year universities. This multi-campus approach to building a data science training program will foster collaborations for training a diverse workforce in data science. The resulting course materials and project outcomes will be made available so that other institutions can adopt best practices.

The partnership consists of four academic institutions on the West Coast: University of California, Santa Barbara (UCSB), California Polytechnic State University, San Luis Obispo (Cal Poly), Santa Barbara City College (SBCC), and California State University, San Bernardino (CSUSB). The alliance will expand training at UCSB and Cal Poly by building on existing strengths through a sequence of new capstone courses, as well as lay the groundwork for data science curriculum development at SBCC and CSUSB, whose students will participate in a summer internship program at UCSB. Over 100 undergraduate students will be supported by stipends during the course of the project. The developed courses will emphasize programming and data inference within the context of application domains that is critical to training in data science. Students will be taught the underlying principles of data science, including data-generating processes and the role of measurement, ethics and privacy, information-processing tools for harnessing the power of big data, and the oral and written communication skills necessary for pursuing effective professional careers in the field. The program will culminate in a year-long capstone course for seniors, who will synthesize and apply previously learned data science tools and techniques in a large-scale project in a chosen domain area.

Funded by: National Science Foundation, HDR DSC, Award# 1924205.