About

Hi there! I’m a Postdoctoral Appointee in the Optimization & Uncertainty Quantification department at Sandia National Laboratories. I recently completed my Ph.D. in Statistics at Iowa State University.

My work revolves around designing advanced Bayesian statistical models to tackle complex challenges in scientific computing and engineering. This includes everything from calibrating large-scale complex systems like the Energy Exascale Earth System Model (E3SM) to helping accelerate fusion power plant design. Beyond physics models, I specialize in expressive hidden Markov models for diverse applications, such as inferring advanced cyber threats from noisy, irregular time series data. My broader statistical interests still lie in time series, spatio-tamporal models, Gaussian Processes, and all things Bayesian.

Background

If my background sounds a bit non-traditional, here are a few details to make more sense of it. I got a B.B.A. and landed a job in finance right away as a structurer of Asset-Backed Securities. I used to model future cashflows and default rates for credit portfolios—essentially creating debt and equity pieces traded by investors. While working full-time, I started a M.Sc. in Finance, but after the coursework, I realized I didn’t enjoy it much.

I decided to challenge myself with something more quantitative: a M.Sc. in Applied Statistics. Meanwhile, I was promoted to head of Asset Management, where I led a small team of analysts and managed publicly traded mutual funds. When the opportunity to go deeper into statistics knocked, I took a plane to the Midwest to start my Ph.D. in Statistics. That journey led me to a doctorate and now a postdoctoral position, and I’m still having a great deal of fun doing stats!