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]
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.
- Ph.D. Statistics, M.S. Statistics, Stanford University, March 2019 [thesis]
- B.S. Mathematics, B.S. Economics, Minor in Linguistics, NC State University, May 2014
I TA’d the following courses at Stanford:
- stats 305c: PhD applied statistics (multivariate statistics), Spring 2018
- stats 305a: PhD applied statistics (linear models), Autumn 2017 and Autumn 2018
- stats 231: theory of machine learning, Spring 2017
- stats 216: introduction to statistical learning, Summer 2015 and Winter 2016
- stats 200: introduction to statistical inference, Autumn 2016
- stats 191: introduction to applied statistics, Winter 2015
- stats 60: introduction to statistical methods, Spring 2015 and Autumn 2015
- stats 50: mathematics of sports, Spring 2016