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Kate Hu is a National Research Service Award postdoctoral fellow at Harvard University, developing methodologies to use auxiliary information embedded in time series and spatial data to adjust for unmeasured and mismeasured confounding bias. She will join the Department of Statistics at Ohio State University in August 2024. Her research interests include

  • Precision agriculture
  • Epidemiology
  • Using auxiliary information to improve statistical inference
  • Cost-effective study design
  • Z-estimation

Before returning to academia, Dr. Kate Hu was the Head of Data Science at Aclima Inc., where she drives the company’s data science R&D to deliver hyper-local air pollution and greenhouse gas emission maps at the unprecedented block-by-block resolution, by dispatching a fleet of vehicles equipped with environmental sensors. This environmental “big data” fills a big gap in what policymakers and activists rely on to bring environmental justice to underserved communities. During her tenure as the Head of Data Science, the company was honored #1 in the 10 most innovative companies in data science by Fast Company in 2021.

Prior to joining Aclima Inc, Dr. Kate Hu was a senior quantitative researcher at Climate LLC, innovating precision agriculture solutions to help farmers maximize the economic return and adapt to climate change. She first led the research program in sampling and experimental designs to collect field data scientifically for model calibration and evaluation. Then she led the interdisciplinary research efforts to develop precision nitrogen treatment algorithms that respond to local environment and real-time weather change, by combining mechanistic models, statistical models, and new sensing technologies.

Kate graduated with First Class Honours from the University of Hong Kong, received an M.S. from Harvard University, and obtained a Ph.D. in Biostatistics from the University of Washington, Seattle. Her PhD dissertation is A Z-estimation System for Semiparametric Models with Two-phase Sampling Designs under the guidance of Norman Breslow, Gary Chan, and Jon Wellner.