Miles Lopes

Miles Lopes

Associate Professor, UC Davis

Department of Statistics

Graduate Group in Applied Mathematics (GGAM)

Office: 4105 Mathematical Sciences Building

Email: melopes à ucdavis.edu

Curriculum Vitae

(Ph.D. UC Berkeley, Statistics. Advisor: Peter J. Bickel)

Interests: High-Dimensional Statistics and Machine Learning

Bootstrap methods for high-dimensional data
Bootstrap methods for error analysis of randomized algorithms

Current and former PhD students:

Andrew Blandino
Mingshuo Liu
Siyao Wang
Junwen Yao

Editorial service:

Associate Editor: Electronic Journal of Statistics, Sankhya A

Support:

DOE grant DE-SC0023490, co-PI, "Reliable, Scalable, and Data-efficient Randomized Graph Neural Networks for Neural Combinatorial Optimization with Scientific Applications"
NSF grant DMS-1915786, sole PI, "Bootstrap Methods in Modern Settings: Inference and Computation"
NSF grant DMS-1613218, sole PI, "Resampling Methods for High-Dimensional and Large-Scale Data"

Selected publications:

Robust max statistics for high-dimensional inference
M. Liu, M. E. Lopes
Preprint, 2024 [arxiv]
Testing elliptical models in high dimensions
S. Wang, M. E. Lopes
Preprint, 2024 [arxiv]
Randomized numerical linear algebra: A perspective on the field with an eye to software
with R. Murray, J. Demmel, M. W. Mahoney, et al.
Preprint, 2023 [arxiv]
See also related video on the Mutual Information YouTube channel.
Improved rates of bootstrap approximation for the operator norm: A coordinate-free approach
M. E. Lopes
to appear: Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques, 2024+ [arxiv]
Error estimation for random Fourier features
J. Yao, N. B. Erichson, M. E. Lopes
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023 [link] [arxiv]
(included in oral presentations, top 1.9% of submissions)
Central limit theorem and bootstrap approximation in high dimensions: Near 1/√n rates via implicit smoothing
M. E. Lopes
Annals of Statistics, 2022 [pdf] [link]
Bootstrapping max statistics in high dimensions:
Near-parametric rates under weak variance decay and application to functional and multinomial data
M. E. Lopes, Z. Lin, H.-G. Mueller
Annals of Statistics, 2020 [pdf] [link]
Estimating the algorithmic variance of randomized ensembles via the bootstrap
M. E. Lopes
Annals of Statistics, 2019 [pdf] [link] [arxiv]
Bootstrapping spectral statistics in high dimensions
M. E. Lopes, A. Blandino, A. Aue
Biometrika, 2019 [link] [arxiv]

[extended list]