My PhD is in Operations Management, but I've spent it on the kinds of problems industry faces everywhere:
time-series forecasting, calibrated probabilistic prediction, and stochastic optimization under uncertainty.
These questions reached me through a healthcare partnership — the methods travel far beyond it.
Each of my three research projects goes all the way from theory to a shipped tool. I've worked with over a million
large-scale records, built simulation platforms from scratch, and turned predictive pipelines into human-in-the-loop
systems — one deployed as a large cancer institute's first production ML model on Databricks,
another as a Python-plus-Excel interface that operations staff actually open.
That instinct traces back to my pre-PhD work as a quantitative researcher in finance: backtesting trading strategies,
calibrating Heston stochastic volatility models, and deriving option-implied probability distributions on the S&P 500.
I'm seeking full-time roles (quantitative researcher, data scientist, or consultant),
where I can keep pairing rigorous modeling with messy real-world decisions.