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

• causal inference with environmental  data 
• proximal causal inference
• use auxiliary information to improve statistical inference 
• study design
• automating asymptotic analysis

Her current applied statistical research interests include 

• precision agriculture
• epidemiology
• climate hazards
• design 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 

• causal inference with time series data
• causal inference with spatial data
• causal inference with continuous treatment
• selection bias/confounding bias
• use proxies to adjust for bias (proximal causal inference)
• smart use of auxiliary information
• automating asymptotic analysis
• any applied statistical research topics listed above

For master or undergrad students, an additional topic is 

• statistics and decision-making 

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