I'm an MS student in Business Analytics and Artificial Intelligence at Johns Hopkins Carey Business School (expected August 2027). I graduated cum laude from Ohio State, with a background in accounting and logistics management.
My projects examine the reliability of AI in business analytics: how data definitions shape results, whether model evaluations are meaningful, and whether business claims are supported by evidence.
Washington, DC · Email · Hugging Face
A correct amount can still support an incorrect claim. In one retail example, an LLM copies the right product and amount but assigns it third place; the evidence places it seventh.
The workflow connects retail transactions, 100 reproducible business questions, computed reference answers, and local model responses. Interactive cases make the errors inspectable. The evaluation uses provisional, AI-assisted span labels without independent human annotation; the featured case is a curated evidence check.
Explore a case · Methods and results · Code
A published Llama fine-tune, a reproducible evaluation workflow, and controlled tests of model behavior. The project connects model development with a practical question: what does a strong score actually establish?
The audit explains the score through a two-field label rule, then tests how predictions respond to changed inputs—including an irrelevant note that flips all 84 negative predictions, while the antonym Early still returns 1 on every row. What the model actually responds to is the project's open question. The same repository carries a real-order study: classical models trained on checkout-time information and tested on 37,702 later Olist purchases.
Explore the project · Read the case study · Olist study · Model card
Experience includes social intelligence analytics at Ipsos in Shanghai, supply chain analysis at SF Express, and work as a Peer Advisor at Ohio State's Office of International Affairs, supporting international students.
Project stack: Python, pandas, scikit-learn, PyTorch, Hugging Face Transformers, pytest, and GitHub Actions.
These personal projects use AI-assisted implementation and review. Each repository documents its methods, evidence, and limitations.