I specialize in agentic AI systems, LLM pipelines, and scalable analytics — blending behavioral science with applied machine learning to build human-centered AI.
I'm a Data Scientist & AI Engineer completing my Master's in Data Science at NYU, with a background in rigorous quantitative methods and the psychological nuances of human behavior.
My work spans production ETL systems at Amazon, multi-agent AI architectures at the United Nations, and ML research on large language models — always with an emphasis on building systems that are not just powerful, but purposeful.
My psychology background gives me an edge in designing models that account for behavioral patterns, not just data patterns.
My research sits at the intersection of machine learning and behavioral science — using computational methods to uncover patterns in psychological data that traditional analysis often misses.
I've applied ML techniques to psychology datasets, presented findings at academic conferences, and explored how quantitative rigor can deepen our understanding of human behavior.
Open to full-time roles starting May 2026 — Data Science, AI Engineering, and ML.