Alex Chin

email: ajchin AT stanford DOT edu

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I completed my PhD in Statistics at Stanford in March 2019, working with Johan Ugander. My thesis was on causal inference under interference.

Methodology-wise, I am interested in the inferential challenges posed by complex data generating processes. My expertise in this domain includes causal inference, machine learning, and experimental design. Engineering-wise, I am interested in building scalable, end-to-end, production-quality data science software. I like to focus on combining these methodology and engineering skillsets to tackle challenging problems and build impactful data science product solutions.


A. Chin, D. Eckles, and J. Ugander, Evaluating stochastic seeding strategies in networks, September 2018. [arXiv]

A. Chin, Regression adjustments for estimating the global treatment effect in experiments with interference, August 2018. [arXiv]

A. Chin, Central limit theorems via Stein’s method for randomized experiments under interference, April 2018. [arXiv]

work experience

Data Science R&D Intern, Civis Analytics, Chicago, IL, Summer 2018

I worked on a platform for measuring the effectiveness of political ads.

Core Data Science Intern, Facebook, Menlo Park, CA, Summer 2017

I worked with the Experimental Design and Causal Inference team to optimize design and analysis for dyad-level experiments.

Modeling Scientist Intern, Quantcast, San Francisco, CA, Summer 2016

I built a MapReduce expectation-maximization system for training large mixture classification models.


Before coming to Stanford, I was a Park Scholar at NC State. I also spent a semester studying math in Budapest, Hungary.



I TA’d the following courses at Stanford: