Amazon, Seattle, WA, USA
Applied Scientist II Intern, Buyer's Risk Prevention (BRP) · May 2026 – Aug. 2026

• Working with the Amazon Buyer's Risk Prevention team on LLM agents and buyer fraud.
• Paper accepted at the REALM workshop @ EMNLP 2026 — a session-level study of whether LLM shopping agents are behaviorally distinguishable from human shoppers.

Intuit, Mountain View, CA, USA
AI Science Intern, QuickBooks AI Science Team · May 2025 – Aug. 2025

• Optimized the latency of an agentic workflow (LangGraph) through high-fidelity automated prompt optimization.
• Built an automated evaluation suite to assess LLM response fidelity for insights on large-scale consumer data.

Rochester Institute of Technology, Rochester, NY
Graduate Research Assistant · Aug. 2023 – Present

• PhD student advised by Prof. Ashiqur R. KhudaBukhsh; research focus on responsible and equitable AI systems.
• Published in A* venues (ACL, EMNLP, AAAI, IJCAI); research covered by ABC News, CNN, and WIRED.
• Mentored three undergraduate researchers through the NSF REU 2026 program — Abhidipto Chakraborty (UT Austin) , Stephanie Birkelbach (Texas A&M) , and Jollen Dai (UCLA) — guiding them from problem formulation to experiments and writing; the work led to two research projects currently under review at top venues.

Trade-System · Personal Project

• A local-first quantitative equity research & paper-trading framework in Python — blending my love of maths, optimization, and machine learning. It ships walk-forward & combinatorial-purged cross-validation with mandatory leakage tests, long-horizon forecasters with conformalized price bands, a nonlinear-dynamics layer (Hurst/fractal, entropy, Lyapunov, RMT, LPPLS bubble detection), and Kelly + Hierarchical Risk Parity position sizing.
• Curious about quant finance? Check it out on GitHub — feedback and stars welcome! 🚀