Research and Projects

🎓 Overview

My research integrates advanced statistical modeling and machine learning to address complex problems in precision medicine, epidemiology, ecology, and environmental science.
I develop interpretable Bayesian and probabilistic frameworks for heterogeneous, high-dimensional, and bounded data — always seeking a balance between methodological innovation and real-world impact.

🔬 Predicting Individual Treatment Effects (PITE) — Precision Medicine and Clinical Trials

Current research focuses on Bayesian learning and hybrid AI to predict individualized treatment responses, advancing personalized healthcare and clinical decision-making.

(2025) A Bayesian Additive Regression Trees Model for Zero and One Inflated Data
Development of a flexible Bayesian framework for Predicting Individual Treatment Effects (PITE) in alcohol use disorder trials.
Preprint →
(2025) Hybrid AI Methods for Feature Selection in Personalized Treatment Effect Prediction
Integration of meta-heuristic optimization and explainable AI for PITE modeling.
WiDS 2025 →
(2025) Bayesian Approaches to Predicting Individual Treatment Effects in Precision Medicine
Evaluation of Bayesian paradigms in individualized inference.
BAYSM 2025 →
(2024) Regression-Based Trees for Outcomes with Closed Boundary Domains
Machine learning for constrained outcomes in biomedical research.
WiDS 2024 →
(2024) Comparing Modern Machine Learning Methods for Predicting Individual Treatment Effects
Benchmark study evaluating Bayesian and frequentist approaches in PITE context.
ISCB 2024 →

🩺 Statistical Consulting in Health and Psychoanalysis

(2024) Challenges and Paradoxes in Mental Health Care for Transgender People in Brazil
Methodological design in psychoanalytic health research.
Paper →

📊 Advanced Statistical Modeling and Machine Learning

Applied research spanning ecology, environmental monitoring, and risk modeling, with emphasis on Bayesian inference, hierarchical models, and multivariate probabilistic analysis.

(2023) Probabilistic Models and Machine Learning for Biomass and LAI Prediction
Integration of multispectral–LiDAR UAV data for environmental monitoring.
Abstract →
(2022) Mapping Leaf Area Index of Chestnut Trees Using UAV-Based Multispectral Data
Remote sensing and predictive modeling of forest structure.
Journal Paper →
(2021) Community Surveillance of COVID-19 Using Wastewater Data
Bayesian hierarchical modeling for epidemic monitoring.
Collaboration with T. Marques and M. Vieira.
(2021) Coral Growth Bands and Trace Elements after the Fundão Dam Collapse
Environmental reconstruction via hierarchical Bayesian inference.
Science of the Total Environment →
(2021) Decadal Dynamics of Southwestern Atlantic Turbid Reefs
Multivariate modeling of long-term ecological data.
PLOS ONE →
(2020) Tropical Rhodolith Beds as Major Reef Fish Habitats
Multilevel ecological modeling of biodiversity.
Scientific Reports →

🌍 Epidemiology and Public Health — Science Communication and Social Impact

During the COVID-19 pandemic, I contributed to Ação Covid19, a volunteer scientific network aimed at translating complex data into accessible language, ensuring reliable, transparent information for the community.

(2020) COVID-19 Waves: Accumulative Mortality and Epidemic Dynamics
Time-series analysis of mortality data to identify temporal trends and epidemic dynamics.
Tohoku Journal of Experimental Medicine →
(2023) COVID-19 Protection Index (CPI)
Geospatial approach to identify regional vulnerabilities in Brazil.
Springer Chapter →
(2020) COVID-19 Vulnerability Mapping
Geostatistical modeling and public dissemination of epidemiological data.
Project →
(2021) Vaccination Process and Epidemiological Surveillance in Brazil
Predictive analysis of vaccination rollout and transmission dynamics.
Project Report →
(2020) Confirmed COVID-19 Cases in Peru by District
Early-stage outbreak modeling and data visualization.
Publication →

📐 Statistical Theory and Methodological Development

(2018) Joint Modeling of Longitudinal Markers and Survival Data
Dynamic hierarchical Bayesian modeling for clinical trials.
(Ph.D. Thesis, Federal University of Rio de Janeiro)
Read Thesis →
(2022) Robust Clustering Under Compositional Constraints
Statistical inference for constrained multivariate data.
Abstract →
(2021) Heavy-Tailed and Overdispersed Collective Risk Models
Advanced actuarial modeling with heavy-tailed distributions.
North American Actuarial Journal →
(2022) Variability in Compositional Regression for Marine Community Dynamics
Hierarchical Bayesian inference for ecological systems.
ISEC 2022 →
(2020) Time-Varying Effects for Compositional Data
Dynamic modeling in ecological time series.
UFRJ Ecology Meeting →

🧭 Early and Interdisciplinary Work

(2018) Symbolic Violence in Urban Music: Analyzing the Artist Maluma
Text mining and sentiment analysis in cultural data.
ECI 2018 →
Slides →
(2015) Visibilidad de la Revista Médica Herediana
Bibliometric analysis of academic impact and visibility in medical research.
Journal Article →

💡 Key Research Strengths

Precision Medicine and Clinical Trials – Bayesian learning, treatment heterogeneity, causal inference.
Machine Learning and Explainable AI – Hybrid optimization, model interpretability, and fairness.
Complex Data Modeling – Longitudinal, survival, compositional, and bounded outcomes.
Bayesian Hierarchical Modeling – Multi-level integration and uncertainty quantification.
Applied and Social Impact – Translating complex science into accessible knowledge in Latin America.