Jiehua Chen

A native of China's Zhejiang province, Jiehua Chen received her B.Sc. degree in Mathematics and Physics from Tsinghua University. In 2008, she received the M.S. in Economics, as well as the PhD in Statistics at Stanford University, under the supervision of Professor Paul Switzer. She is currently a Postdoctoral Researcher and Assistant Professor in the Statistics Department of Columbia University.

My research interests include spatial statistics, random experiment design, causal inference methodology, and Bayesian modelling. I have very strong interests in the proper real-world applications of these statistical ideas to the social, environmental, and political sciences. Please refer to CV for the most recent updates.

United Nations Millenium Village Projects

I was working with Professor Vijay Modi and Professor Macartan Humphreys on a UN Millenium Villages Project, in which we are deploying energy-efficient stoves to needy villages in Uganda. This is a long-term project spanning several years.

My contributions include establishing logistics, negotiating with local peoples on-site, designing statistical experiments to quantitatively demonstrate the effectiveness of aid, and using math to optimize the dissemination of stoves.

Regression Models With Spatially Correlated Residuals; Applications to Chinese Economics

My thesis presents a battery of new spatial statistics theorems pertaining to tests of spatial correlation, estimation, and the consistency of maximum-likelihood estimation in infill asymptotics.

I applied my theoretical results to a large real-world dataset, acquired in conjunction with the Chinese Academy of Sciences, to analyze the effects of economic growth on rapid urbanization in China, focusing on three particular provinces. The major socioeconomic factors underlying urban core growth were determined while properly handling spatial correlation in the regression error terms.

For my CV, including publications and conferences, click here.

I teach a graduate-level statistics course in Time Series Analysis, W4437, at Columbia University. Topics covered include: ARMA and ARIMA models, ACF, forecasting, parameter estimation, unit root tests, and basic spectral analysis.

I have also served as a teaching assistant for numerous statistics courses at Stanford University, including introductory statistics, stochastic processes, markov chains, and the theory of statistics.

address

Department of Statistics, Columbia University
1255 Amsterdam Avenue, Tenth Floor
New York, NY 10027

email

jc3288 AT columbia DOT edu