● Utilized various data collection sources to support discovery, network exploitation, and mitigation of foreign intelligence threats. ● Performed research using open source and classified sources to develop a comprehensive baseline profile on designated target sets and determine adversarial actions and intentions.● Conducted advanced analysis on collection to ensure target continuity, monitor targets and their activities, and develop techniques to gain additional target information. ● Analyzed and evaluated information to assess developments, trends, and threat implications.● Identifed intelligence gaps, reevaluated current methods, and tested alternate avenues to close the gaps. ● Derived actionable intelligence from data to aid decision-making. ● Prepared intelligence briefs, presented mission updates, distinguished shortfalls and approaches to move forward. ● Worked closely with other agencies while maintaining optimum levels of communication to effectively and efficiently complete projects.

● Selected from a highly competitive pool with a 3% acceptance rate for the DAF-MIT AI Accelerator Phantom Fellowship. ● Boosted AI research and integration across 15 Lines of Effort (LOEs) totaling $30M in support of National Defense Strategy (NDS) objectives, earning the Innovation Special Experience Identifier (SEI). ● Advised senior leaders on AI/ML integration within a $75M Strategic Air Force (SAF) unit, enhancing a $15M annual program with a team of 160 researchers, aligning efforts with the Chief of Staff of the Air Force (CSAF)'s priority of 'Working with Industry.' ● Collaborated as a DAF/MIT liaison, partnering with Task Force Lima's Cyber and Adversarial Research Strike Group to develop guidance and guardrails for the creation of generative AI policy. ● Contributed to the "Better Networks via AI Enabled Hierarchical Connection Science" team. Focused on the development of privacy-preserving techniques to counter adversarial activities within heavy-tail distributions, spanning low-level network traffic to high-level social media. This aimed to create a comprehensive understanding of network activities and implement appropriate mitigations. ● Developed a tailored educational framework for Large Language Models (LLM), delivering specialized training on 6 critical topics, effectively mitigating vulnerabilities within the Department of Defense (DoD). ● Researched and identified 3 critical security vulnerabilities within the DoD’s LLM implementation, resulting in the publication of an IEEE research paper with recommended mitigations, enhancing national and personal security measures.