Alex Luedtke

Alex Luedtke is an Adjunct Associate Professor in Biostatistics and Associate Professor in Statistics at the University of Washington. His research focuses on causal inference, machine learning, and nonparametric methods with applications in clinical trials and public health. He is affiliated with the UW Clinical Trials Center and Genetic Analysis Center.

Education details are not explicitly stated in the text, though his roles indicate advanced training in statistics. His work emphasizes methodological advancements in causal machine learning, efficient estimation, and robust statistical techniques.

Recent research trends include coreset selection for dataset compression, stabilized inverse probability weighting, and automatic differentiation for influence functions. He has contributed to high-impact venues like the International Conference on Machine Learning (ICML) and the Conference on Learning for Healthcare (CLeaR).

Recipient of the Best Paper Award at CLeaR 2025 for work on automatic debiased machine learning. Active in mentoring doctoral students (15 advised, 10 international) and advocating for academic policies supporting diverse student populations.

Luedtke collaborates with institutions like the Institute of Statistical Mathematics (Tokyo) and engages in public discourse on NIH funding, immigration policies affecting academia, and ethical AI governance. His lab focuses on translational statistical methods for healthcare and complex systems.