Diego Martinez Taboada

About Me

I am a fourth-year PhD student in the Department of Statistics and Data Science at Carnegie Mellon University, where I am advised by Prof. Aaditya Ramdas. Previously, I completed an MSc in Statistical Science at the University of Oxford and a bachelor's degree in mathematics at the University of Santiago de Compostela.

Google Scholar Profile | LinkedIn Profile | CV

Education

• Carnegie Mellon University, PhD in Statistics and Data Science
  2022 -- present
• University of Oxford, MSc in Statistical Science
  2021 -- 2022
• University of Santiago de Compostela, BSc in Mathematics
  2017 -- 2021

Research

I am broadly interested in the theory and application of methodologies built on high-dimensional (and even infinite-dimensional!) spaces. In particular, I have recently been focusing on (the intersection of) vector and operator-valued concentration inequalities, sequential statistics, causal inference, and RKHS theory (kernel methods), among others.

Preprints

1. Intrinsic dimension concentration inequalities for self-adjoint operators
   Diego Martinez-Taboada, Aaditya Ramdas
   Under review. [arXiv]

Publications

1. Sharp empirical Bernstein bounds for the variance of bounded random variables
   Diego Martinez-Taboada, Aaditya Ramdas
   International Conference on Machine Learning (ICML), 2026. [arXiv]
2. Nonasymptotic heavy-tailed mean estimation in smooth Banach spaces
   Justin Whitehouse, Ben Chugg, Diego Martinez-Taboada, Aaditya Ramdas
   Stochastic Processes and their Applications, 2026. [arXiv] [proc]
3. Vector-valued self-normalized concentration inequalities beyond sub-Gaussianity
   Diego Martinez-Taboada, Tomas Gonzalez-Lara, Aaditya Ramdas
   International Conference on Algorithmic Learning Theory (ALT), 2026. [arXiv]
4. Empirical Bernstein in smooth Banach spaces
   Diego Martinez-Taboada, Aaditya Ramdas
   Annals of Applied Probability, 2026. [arXiv]
5. Sequential Kernelized Stein Discrepancy
   Diego Martinez-Taboada, Aaditya Ramdas
   International Conference on Artificial Intelligence and Statistics (AISTATS), 2025. [arXiv] [proc]
6. Counterfactual Density Estimation using Kernel Stein Discrepancies
   Diego Martinez-Taboada, Edward H. Kennedy
   International Conference on Learning Representations (ICLR), 2024. [arXiv] [proc]
7. An Efficient Doubly-Robust Test for the Kernel Treatment Effect
   Diego Martinez-Taboada, Aaditya Ramdas, Edward H. Kennedy
   Neural Information Processing Systems (NeurIPS), 2023. [arXiv] [proc]

Contact Information

diegomar AT andrew DOT cmu FULLSTOP edu