Lucas Resende

Robust statistics is about methods that remain reliable when data deviates from ideal assumptions — for instance, when observations have heavy tails or contain outliers.

• Trimmed Sample Means for Robust Uniform Mean Estimation and Regression
  with Roberto Imbuzeiro, AoS, 2025
  In this work we use the trimmed mean to obtain robust estimators for uniform mean and regression, obtaining optimal performance in terms of moments.
  [View on AoS] [View on arXiv] [Poster]
• Robust High-Dimensional Gaussian and Bootstrap Approximations for Trimmed Sample Means
  solo paper, preprint, 2025
  The literature on robust statistics focus on high probability concentration bounds. In this work I explore the high-dimensional Gaussian and bootstrap approximations properties of the trimmed mean. The key takeway is that it yields good approximations under heavy tails, while the empirical mean only support Gaussian approximations under light tails.
  [View on arXiv] [Poster]
• Improved Concentration for Mean Estimators via Shrinkage
  with Antônio Catão and Paulo Orenstein, working paper
  We study a family of adaptive shrinkage-based methods for robust mean estimation that achieve strong statistical guarantees. This project investigates their theoretical properties and practical performance across a range of applications.
  

Models with fixed effects often face the incidental parameter problem — biases that appear when the number of nuisance parameters grows with the sample size. My research develops methods for valid and numerically stable inference in such settings, extending classical econometric approaches.

• Unbiased Estimation of Multi-Way Gravity Models
  with Guillaume Lecué, Lionel Wilner, Philippe Choné, working paper
  This working paper develops a bias-free estimator for weighted networks with high-dimensional fixed effects, generalizing tetrad methods from binary to count data. By exploiting difference-in-differences structures in Poisson models, the approach removes incidental parameters, scales efficiently in sparse networks, and enables identification of both extensive and intensive margins in multi-way settings.
  [View on arXiv]

I collaborate across public health, economics, and industry to apply modern machine learning and statistical methods to real-world problems.

• LLM-Based Health Policy Evaluation
  Graph4Health project, ENSAE, ongoing
  Train transformer models on medical event sequences to create patient embeddings for predicting health outcomes and conducting off-policy evaluation. Currently used to study France’s médecin traitant system.
  [Graph4Health's page]
• Epidemiological Modeling for COVID-19 School Reopening
  with COMORBUSS team, 2021
  Developed models to assess the effects of reopening strategies under varying vaccination and masking scenarios, informing public health decisions.
  [Outreach on Piauí Magazine] [PLOS One paper]
• Deep Hashing via Householder Quantization
  with Lucas Schwengber, Paulo Orenstein and Roberto Imbuzeiro
  Motivated by large-scale similarity search in streaming platforms, this project led to a new deep-hashing quantization method that improves fast retrieval of similar items.
  [View on arXiv]