Robert Lunde

Robert Lunde is an Assistant Professor in the Department of Mathematics and Statistics at Washington University in St. Louis. He holds a PhD in Statistics and Data Science from Carnegie Mellon University and completed postdoctoral research at the University of Michigan and University of Texas.

Lunde's research focuses on statistical inference for complex data structures including networks and time series. His expertise encompasses resampling methods, distribution-free inference, and high-dimensional statistics. Specific interests include conformal prediction for network data, validity of jackknife methods for graphs, and bootstrap techniques for streaming algorithms.

His recent publications explore subsampling sparse graphons, bootstrap error analysis for Oja's algorithm, and theoretical foundations for network-assisted regression. Lunde teaches courses in mathematical statistics and probability theory, employing his research expertise in statistical learning and inference methods.