Applied Scientist specializing in AI systems, large language
models, and evaluation frameworks. My work focuses on building the
infrastructure, methodologies, and quality systems that enable enterprise AI
agents to be measured, optimized, and deployed with confidence. I lead the
design of evaluation, benchmarking, and grading platforms used to assess model
quality, reliability, and alignment across real-world applications.
With 10+ years of experience and a Ph.D. in Applied
Sciences, I've worked across academia and industry, translating
research into production-scale AI systems spanning enterprise AI, natural
language processing, geospatial intelligence, environmental modeling, and
machine learning platforms. I'm an inventor on 14+ patent
applications and author of multiple peer-reviewed publications in AI,
machine learning, and large language models.
Beyond technical contributions, I've mentored 300+ students
and professionals in data science and machine learning, and regularly
contribute to discussions on AI evaluation, benchmarking, and trustworthy AI.
My interests lie in building reliable AI systems that bridge scientific rigor,
product impact, and real-world adoption.
