Austin Bean

Austin Bean is an experienced PhD economist with a specialization in empirical industrial organization, bringing over 6 years of expertise in machine learning techniques. He holds a PhD-level training in structural econometrics, with a focus on demand estimation, auction models, and dynamic oligopoly models. Austin has a strong foundation in causal inference methods and a comprehensive understanding of machine learning methods in big data and high-dimensional data settings.

His skill set includes proficiency in natural language processing encompassing word embeddings and recurrent neural network architectures, as well as computer vision involving convolutional neural network architectures. With a track record of handling large unstructured datasets exceeding 10 Tb, Austin has presented and simplified complex economic concepts effectively to diverse audiences. As a team leader, he has 4 years of experience managing teams to drive scientific research forward. Austin is a seasoned programmer in Julia, Stata, Python, Matlab, SQL, and R.

Austin Bean pursued his Doctor of Philosophy (PhD) in Economics at The University of Texas at Austin after completing his Bachelor's degree in Philosophy, Mathematics, and Economics from the University of Chicago. He also studied at The Anglo-American School of Moscow. Throughout his career, Austin has held notable roles including Senior Economist at Amazon, Assistant Professor at Temple University, Postdoctoral Researcher at Leonard Davis Institute of Health Economics, and has previous experience in admissions at the University of Chicago.