I am currently actively working on projects related to AI strategy within cybersecurity functions, adversarial machine learning for the defense sector, and development of practical generative AI solutions aimed at advancing medical research.
I am actively engaged in developing new coursework surrounding applied generative AI, while enhancing practical components of existing predictive analytics and data warehousing coursework to engage with new methods of data analysis and machine learning..
AI integration with organizational strategy
Internal research activity focused on the impact of AI advancements on organizational strategy. Current outputs include a co-authored research paper investigating the role of cybersecurity professionals in shaping AI strategy, which is currently published as part of the 2024 AMCIS proceedings (link).
Adversarial machine learning in the defense sector
A federal grant funded activity in collaboration with a private organization, Hidden Layer. Public artifacts currently include the white paper "Invisible Threats: Accessing the Heart of Machine Learning" (link)
Developing and deploying innovative AI tools
University grant funded initiative to develop a platform for generative AI application development. This activity culminated in the launch of multiple micro-applications serving the UB student, faculty, and staff community during the Spring 2024 semester. These applications included career tools, classroom support resources, and student assistant apps, all driven by locally run Large Language Models (LLMs).
AI for scientific synthesis
Multiple ongoing projects to explore the efficacy of LLM usage to synthesize scientific literature and enhance the predictive capabilities of classical machine learning models, particularly relating to biological sciences. Current activities include multiple independent projects exploring this problem space.
