
Connect and collaborate with experts as the Cyber Fusion Innovation Center (CFIC) facilitates virtual events in this series. Webinars take place quarterly and offer presentations to various audience members on various topics, including technology, cybersecurity, future-proofing efforts, and more. The audience can learn together during the hour-long virtual event and ask questions during a Q&A segment. Post-session videos are typically available the day after the event concludes. Please check back if you're unable to attend live!
More About Dr. Shakarian
Paulo Shakarian is the K.G. Tan Endowed Professor of Artificial Intelligence at Syracuse University where he directs the Leibniz Lab. Shakarian has made notable contributions in the areas of logic programming, neurosymbolic AI, security, and data mining. His academic accomplishments include four best-paper awards, over 100 peer-reviewed articles, 12 issued patents, and 8 published books. Shakarian has secured over $7 million in grant funding from a wide range of government and industry sponsors that included highly competitive grants such as the Amazon ARA, AFOSR Young Investigator Award, and multiple awards from DARPA, IARPA, ARPA-H, ARO, and ONR. Paulo also raised $8 million in venture capital to commercialize his work on software exploit prediction, leading to startup (acquired in 2022). Previously, Shakarian held fellowships with DARPA and the New America Foundation, a faculty position at the U.S. Military Academy (West Point), and served as Research Director at the School of Computing and AI at Arizona State University where he was also tenured faculty. Prior to his academic career, he was a field-grade officer in the U.S. Army. He holds a Ph.D. and M.S. in computer science from the University of Maryland and a B.S. (computer science) from the U.S. Military Academy.
Topic: AI Driven Vulnerability Discovery
Key points:
Limitations of LLM agents for vulnerability discovery
The role of domain knowledge
Logic-based AI method to combine LLM agents with domain knowledge for improved vulnerability discovery

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