I am an Economist at LinkedIn.
I graduated with a PhD in Economics from Stanford GSB in Jan 2022. In my research, I combined causal inference and machine learning methods to study inefficiencies and riskiness in the US housing market.
Here is my LinkedIn.
I train a recurrent neural network to predict individuals’ race using information contained in their name and location of residence. I introduce a novel data source that contains millions of race/name/zipcode triplets and covers the entire US. I train my baseline LSTM model using only personal name data. The baseline model attains overall accuracy of 0.87, which is a non-trivial improvement over the existing benchmarks in the literature. However, because personal names of Non-Hispanic Whites and Non-Hispanic Blacks follow similar naming conventions, the baseline model struggles at accurately classifying Non-Hispanic Blacks. Using location data in addition to personal name improves classification accuracy of the model for Non-Hispanic Blacks substantially, and allows the best model to achieve 0.89 accuracy on the test set.
Work in Progress
Industrial Coagglomerations and Risk Concentration
Can Market Forces Bridge the Racial Gaps in the US Housing Market? with Cody Cook