Data Scientist · Ph.D. in Physics
I work across causal inference, A/B testing, time-series forecasting, and statistical modeling at scale. Most recently at Meta on ads measurement and attribution strategy, before that at Happy Returns through its acquisitions by PayPal and UPS, and before that doing physics research at NYU's Center for Quantum Phenomena. I also write about statistics, probability, and physics simulations.
About me
I'm a Senior Data Scientist with a background in experimental physics, specializing in causal inference, experimentation, and statistical modeling at scale.
At Meta, I work on ads measurement and help advertisers understand the true return on investment of their ad campaigns. My work focuses on building and strengthening attribution strategy by integrating experimentation with causal inference methods: difference-in-differences, propensity score matching, synthetic controls, and incrementality frameworks, to better reflect how the advertising industry measures impact. I also lead cross-functional efforts to standardize fair-credit rules for external measurement partners across methodologies like MTA, MMM, and GeoLift.
Before Meta, I spent three years at Happy Returns (acquired by PayPal, then UPS during my tenure). I started building predictive models for shipping supply forecasting across thousands of locations and led multi-round A/B tests on customer-facing features. Over time my work shifted toward experimentation and optimization, building time-series forecasting pipelines for shipping logistics across 10,000 centers.
My PhD research at NYU's Center for Quantum Phenomena focused on current-induced spin dynamics in antiferromagnetic materials, supervised by Andy Kent. I worked with hundreds of gigabytes of electron microscopy image data and developed analytical and numerical models to extract signal from high-dimensional voltage data as a function of temperature, current, and magnetic field.
Outside of work, I write about topics I find interesting in the blog, mostly statistics, probability, and the occasional physics simulation.
Career
Technical
Writing
Notes on statistics, probability, and physics. Written for people who like thinking carefully about things.
Two schools of thought dominate statistical inference. Using a simple coin flip example, this post walks through how each framework builds uncertainty intervals, what those intervals actually mean, and when to reach for one over the other.
What does "random" actually look like? Most people have a confident answer: scattered, spread out, no obvious patterns. But true randomness looks nothing like that. It clusters, repeats, and surprises in ways our intuitions don't expect.
When running experiments, understanding uncertainty and statistical significance matters a lot. This post explores how the bootstrapping method helps calculate confidence intervals and p-values in a concrete A/B test scenario.
Interactive
A live simulation of the 2D Ising model running in your browser: a lattice of spins that spontaneously orders below a critical temperature.
Research
Physics research from my time at NYU's Center for Quantum Phenomena. Full list on Google Scholar.