Tools I Use
Applied AI
I’ve explored LLMs and GenAI interfaces as ways to make complex systems more usable, from multi-agent simulations to natural-language summaries that help users understand what a system is doing and why.
Machine Learning
I’ve worked with models like logistic regression and XGBoost in messy, real-world settings, especially problems with heavy class imbalance, where accuracy alone is misleading. I care a lot about threshold tuning and understanding the tradeoffs behind every prediction.
Evaluation & Decision-Making
I think beyond model scores and focus on what errors actually mean in practice. False positives, confusion matrices, and downstream impact matter a lot to me, especially in systems where humans are the ones acting on the output.
Data Engineering
Most of my work lives in Python and SQL. I enjoy building pipelines that take raw, imperfect data and turn it into something reliable enough to support modeling and decision-making, not just something that runs once and breaks later.
Visualization & Communication
I use tools like Tableau, Power BI, and D3.js to make results easier to understand and explain. A good visualization should help someone answer questions quickly, not make them dig through charts to feel confident.
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