Kate Hu joined the Department of Statistics in 2024, following her roles as Head of Data Science at Aclima, Inc. and Senior Data Scientist at Climate LLC, both climate-tech companies based in San Francisco. She earned her PhD from the University of Washington, Seattle, where her research focused on semiparametric inference, empirical processes, two-phase sampling, and the use of auxiliary information. During her postdoctoral research at Harvard University, she explored proximal causal inference with environmental data. 

Her current statistical  research interests include

• on-farm experiment design and analysis
• causal inference with environmental data
• proximal causal inference
• use auxiliary information to improve statistical inference 
• automating asymptotic analysis

Her current applied statistical research interests include 

• precision agriculture
• epidemiology
• climate hazards
• designing environmental monitoring network

For students who are looking for reading courses, RA positions, dissertation topics, and research projects, you are welcome to contact me if you are interested in 

• Inference problems with time series data or spatial data
• selection bias
• smart use of auxiliary information
• automating asymptotic analysis
• any applied statistical research topics listed above

For undergrad students, an additional topic is 

• environmental and agricultural data analysis

My research style is problem-drive, which means we will focus on formulating and solving a  problem. The choice of approaches and tools—whether Bayesian, frequentist, machine learning, or semiparametric models—depends on the specific problem at hand.