Kirsten Landsiedel is currently a PhD candidate in Biostatistics at the University of California, Berkeley, mentored by Mark van der Laan and Laura Balzer. She previously earned her MA in Biostatistics at UC Berkeley, as well as a BS in Statistics and a BA in Economics from UCLA. Her research interests primarily lie in targeted machine learning, causal inference, survival analysis, semi-parametric efficiency theory, and methods for handling missing and censored data. She has previously contributed to applied research examining the relationship between alcohol consumption and incident TB infection, as well as understanding the prevalence and predictors of TB among children and adolescents in rural Uganda. Currently, she is developing more efficient estimators for survival in resampling designs and collaborating on projects involving multi-task learning and profile likelihood estimation.
Targeted machine learning, causal inference, survival analysis, semi-parametric efficiency theory, missing and censored data
