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zjh235[at]nyu[dot]edu
I'm a PhD candidate in biostatistics at New York University's school of Global Public Health, where I do research in applied biostatistics for environmental exposure measurement. I’m driven by my commitment to finding statistical solutions to real world environmental data problems - whether by adapting methodology across disciplines or developing new models for complex and heterogeneous data. My research draws on methods from algorithmic fairness, causal inference, and spatial and climate statistics. I am supervised by Dr. Rebecca Betensky, Dr. Yajun Mei, Dr. Priyanka de Souza and Dr. Kevin Josey.
I spent the summer of 2024 as a Summer Incubator Program Fellow at the Max Planck Institute for Demographic Research (MPIDR) working on a project identifying spatial disparities in vulnerability to extreme temperatures in Europe. Previously, I was an NYU Urban Doctoral Fellow (2022-2023) and a member of the Ferguson RISE fellowship program interning at the Centers for Disease Control (CDC) (2022). I completed a B.Sc. in physics and a B.A. in social and political history from Carnegie Mellon University (2020). During my undergrad I was mentored by wonderful faculty including Dr. Earn, Dr. Phillips, and many others.
Feel free to contact me for research collaborations!
My current research projects include:
developing a statistical method to integrate survey weights into peaks-over-threshold modeling of extreme values
investigating the association between PM2.5 and menstrual cycle irregularity, using a Bayesian approach to distributed lag models (tree-DLM) and a case time-series design
Some previous research projects:
quantifying and assessing equity in the distribution of regulatory PM2.5 monitors across the U.S., using hierarchical and spatial error models to understand associations between race/ethnicity and SES and monitor proximity - published in ES&T
applying an extreme value analysis approach to characterize the prevalence of high blood pressure (winning project at the ENAR 2024 Student DataFest competition)
understanding how data patterns (concept shifts, covariate shifts, missingness patterns, and unmeasured confounders) affect explanation disparities in explainable AI algorithms like LIME
analyzed how some quantitative measures of health disparities behave under data patterns common to infectious diseases
worked with Dr. Goodman on developing a comprehensive and validated scale to quantify the extent of community stakeholder involvement with research projects
helped develop an R package for working with historical mortality data from the London Bills of Mortality
News
News
Our team won the Student DataFest competition at ENAR 2024 with our project on using extreme value analysis to characterize the prevalence of high blood pressure (March 2024)
Our paper examining disparities in explanation methods (XAI) is up on the arXiv! (Jan. 2024)
Excited to be attending ISEE 2023 in Taiwan and presenting my work on "Air Pollution Monitor Network Design for Fair Exposure Estimation" (Sept. 2023)
I presented a poster titled "Moving Beyond Risk Ratios: An Analysis of Quantitative Measures of Health Disparities" at SER in Portland for which I was awarded the Kathy Rose Travel Scholarship (June 2023)
Attended NYU Urban Day and presented on air pollution monitor network design for fair exposure estimation (March 2023)
I gave the keynote speech at the inaugural AI for Public Health Practice retreat held in London, Ontario (October 2022)
Thrilled to be selected as a member of the NYU Urban Doctoral Fellowship Program for 2022-2023
I'm honored to be joining the Centers for Disease Control (CDC) as part of the Ferguson RISE fellowship program (Summer 2022)
Our paper on Construct validation of the Research Engagement Survey Tool (REST) is published! (June 2022)
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