Welcome!

I am a Tenure-track Assistant Professor in the Department of Statistics and Data Science at Washington University in St. Louis. I earned a Ph.D. in Mathematics at the Department of Mathematics at the University of Notre Dame under the supervision of Dr. Daren Wang. During the first two years of my Ph.D. I received a Master's degree in Mathematics under the supervision of Dr. Alex Himonas.

My undergraduate degree was a B.S. in Mathematics completed at CIMAT (Mexico) in May 2019, advised by Dr. Víctor M. Pérez Abreu C. and Dr. Mario Díaz.

A copy of my CV can be found here.

I was born and raised in Honduras.

Research

My research interests include:

• High-dimensional statistics
• Nonparametric statistics
• Change point detection
• Causal inference
• Bayes methodology

Published / Accepted Papers

Carlos-Misael Madrid-Padilla, Oscar Hernan Madrid Padilla, Sabyasachi Chatterjee. Risk Bounds For Distributional Regression. PDF NeurIPS 2025. Code.

Carlos-Misael Madrid-Padilla, H. Xu, D. Wang, O.H. Madrid-Padilla, Y. Yu. Change point detection and inference in multivariable nonparametric models under mixing conditions. PDF. NeurIPS 2023. Code.

Carlos-Misael Madrid-Padilla, Daren Wang, Zifeng Zhao, Yi Yu. Change-point detection for sparse and dense functional data in general dimensions. PDF. NeurlPs 2022 Code.

Alexandrou Himonas, Carlos-Misael Madrid-Padilla, Fangchi Yan (alphabetical order). The Neumann and Robin problems for the Korteweg-de Vries equation on the half-line. PDF
Journal of Mathematical Physics, 62, 111503. 2021. (selected as Editors’ Pick)

Preprints

Carlos-Misael Madrid-Padilla, Oscar Hernan Madrid-Padilla, Daren Wang. Temporal-spatial model via Trend Filtering. PDF Under Review. 2024. Code.

Carlos-Misael Madrid-Padilla, Zhi Zhang, Oscar Hernan Madrid-Padilla, Xiaokai Luo, Daren Wang. Dense ReLU Neural Networks for Temporal-spatial Model. PDF Under Review. 2025.

H. Xu, Carlos-Misael Madrid-Padilla, O.H. Madrid-Padilla, D. Wang. Multivariate Poisson intensity estimation via low-rank tensor decomposition. PDF. Under Review. 2025. Code.

Carlos-Misael Madrid-Padilla, Oscar Hernan Madrid Padilla, Yik Lun Kei, Zhi Zhang, Yanzhen Chen. Confidence Interval Construction and Conditional Variance Estimation with Dense ReLU Networks. PDF Under Review. 2025. Code.

Carlos-Misael Madrid-Padilla, Shitao Fan, Lizhen Lin. Robust and Scalable Variational Bayes. PDF Under review. 2025. Code.

Oscar Hernan Madrid Padilla, Yanzhen Chen, Carlos-Misael Madrid-Padilla, Gabriel Ruiz. A causal fused lasso for interpretable heterogeneous treatment effects estimation. PDF Under review. 2025.

Shitao Fan, Carlos-Misael Madrid-Padilla, Yun Yang, Lizhen Lin. Amortized Structural Variational Inference. PDF Under review. 2025. Code.

Shivam Kumar, Haotian Xu, Carlos-Misael Madrid-Padilla, Yuehaw Khoo, Oscar Hernan Madrid Padilla, Daren Wang. Bias-variance Tradeoff in Tensor Estimation. PDF Under review. 2025.

Xiaokai Luo, Haotian Xu, Carlos-Misael Madrid-Padilla, Oscar Hernan Madrid Padilla. Online Change Point Detection for Multivariate Inhomogeneous Poisson Processes Time Series. PDF Under review. 2026.

Carlos-Misael Madrid-Padilla. A Unified Framework for Online Change Point Detection in Nonparametric Regression. Under Review. 2025.

Working Papers

[1] Carlos-Misael Madrid-Padilla, Shitao Fan, Yun Yang, and Lizhen Lin.
ELBO Empirical Bayes.
In Progress. 2025+.

[2] Zhi Zhang, Kyle Richter, Carlos-Misael Madrid-Padilla, and Oscar Hernan Madrid Padilla.
Risk Bounds for Quantile Temporal-Spatial Analysis.
In Progress. 2025+.

[3] Fan Wang, Yik Lun Kei, Carlos-Misael Madrid-Padilla, Xin Ma, and Oscar Hernan Madrid Padilla.
Multilayer Change Point Detection for Brain Data.
In Progress. 2025+.

[4] Carlos-Misael Madrid-Padilla.
Regularized Estimation under High-Dimensional Covariates and Treatment Effects.
In Progress. 2025+.

[5] Carlos-Misael Madrid-Padilla and David Dunson.
Inferring Latent Structure in Ecological Communities via Neural Networks.
In Progress. 2025+.

[6] Carlos-Misael Madrid-Padilla.
Kernel-Based Offline Change-Point Detection for Conditional Distributions.
In Progress. 2025+.

[7] Shourjo Chakraborty and Carlos-Misael Madrid-Padilla.
Distributional Regression: Change Point Detection and Neural Networks.
In Progress. 2025+.

[8] Carlos-Misael Madrid-Padilla and Sangwon Hyun.
Change Point Detection in the Temporal Dynamics of Ocean Plankton Species.
In Progress. 2025+.

[9] Weichen Kang and Carlos-Misael Madrid-Padilla.
A Unified Framework for Offline Change Point Detection in Distributional Regression.
In Progress. 2025+.

[10] Davis Berlind, Junpeng Ren, Oscar Hernan Madrid Padilla, Carlos-Misael Madrid-Padilla.
Heterogeneous Distributional Treatment Effect Estimation with Noncrossing ReLU Neural Networks.
In Progress. 2025+.

[11] Junpeng Ren, Oscar Hernan Madrid Padilla, Carlos-Misael Madrid-Padilla.
Transfer Learning in Nonparametric Regression with Deep ReLU Networks.
In Progress. 2025+.