Jiehua Chen

Associate Research Scientist at The Earth Institute

Columbia University

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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 an Associate Research Scientist in the Agriculture and Food Security Center of Earth Institute at Columbia University. Physically, I am mostly based in Africa, where I am working on an exciting soil property prediction engine using statistics, machine learning, and parallel cloud computing.


My research interests are spatial-temporal modeling, time series analysis, infill asymptotics, and multilevel modeling. Applications that I am interested in include rainfall forecasting, environment digital mapping, renewable energy planning, and forecasting.

Africa Soil Information Service (AfSIS)
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I am currently focusing on spatial-temporal data analysis, high-dimensional data analysis, image processing for remote sensing applications, and cloud computing software tools for parallelizing these statistical techniques. These tools are currently used in Africa Soil Information Service (AfSIS) project, which requires statistical analyses of spatial-temporal soil and agricultural yield data, hyperspectral soil data, and satellite imagery.

United Nations Millenium Village Projects
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For my postdoc research, I worked with Professor Vijay Modi and Professor Macartan Humphreys on a UN Millenium Villages Project, in which we deployed energy-efficient stoves to needy villages in Uganda. This was a long-term project spanning several years.

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My contributions included 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
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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.

Curriculum Vitae and Publications

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


I taught 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.

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Contact Information
US address
Earth Institute, Columbia University
Agriculture and Food Security Center, Lamont Campus
61 Route 9W, Lamont Hall, 2G
P.O. Box 1000
Palisades, NY 10964
Africa address
Selian Agricultural Research Institute (SARI)
P.O. Box 6024, Arusha
jc3288 AT columbia DOT edu

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Updated 08/03/13, 08:05:25 PM