Researcher in Health Data Science at the London School of Hygiene & Tropical Medicine (LSHTM), working on a Wellcome Trust-funded project on predictive modelling for stillbirths and neonatal deaths across Sub-Saharan Africa. That work involved harmonising seven heterogeneous data sources — DHS, EN-INDEPTH, WHOMCS, ALERT, PTBi, PRECISE and NCOPS into a single analytical dataset spanning sub-Saharan Africa, and designing a fully audited, reproducible eligibility cascade from the raw harmonised records to the final analytic set. Visiting Researcher at PROADI-SUS, Hospital Israelita Albert Einstein (São Paulo). Former Technical Consultant (Epidemiologist & Data Scientist) at PAHO/WHO.
Education: PhD in Public Health – Epidemiology (USP) · MPH (UFBA) · MBA in Data Science & Analytics (USP-Esalq) · MBA in AI & Big Data (ICMC-USP) · BSc Nutrition (Lúrio University)
| Region | Based at | Focus |
|---|---|---|
| Africa | Ministry of Health & Lúrio University, Mozambique | District nutrition programmes; national undernutrition studies with UNICEF and the World Food Programme; MSc research on undernutrition using Mozambican survey data |
| South America | PAHO/WHO and the Ministry of Health, São Paulo State Surveillance Centre, USP–LABDAPS, Hospital Israelita Albert Einstein, Brazil | COVID-19 and NCD surveillance, spatial epidemiology, forecasting, real-world evidence, predictive modelling |
| Europe | London School of Hygiene & Tropical Medicine, United Kingdom | Maternal and newborn health data science; multi-source harmonisation and predictive modelling for sub-Saharan Africa |
Experience harmonising and analysing heterogeneous health data — population surveys, facility cohorts, vital registration and notifiable-disease systems — across settings where coding, coverage and data quality differ substantially between sources.
| Multi-country surveys & cohorts | DHS · EN-INDEPTH · WHOMCS · ALERT · PTBi · PRECISE · NCOPS |
| Brazilian national systems | SIM · SIA · SINAN · SINASC · Vigitel · PNS · PNAD-TIC · e-SUS |
| Linked administrative data | CIDACS-Nexus, Fiocruz Bahia |
| Cohort & survey studies | Araraquara maternal–child cohort · Mozambique undernutrition survey (Clinton Foundation), used for my MSc research |
| Collection platforms | REDCap · KoBoToolbox |
-00C853?style=for-the-badge&logoColor=white){kind=icon}
| Project | Description | Stack | |
|---|---|---|---|
| 🔬 | Stillbirths & Neonatal Deaths – SSA | Harmonisation of seven heterogeneous data sources into one analytical dataset; eight-algorithm predictive modelling with external validation in South Asia | Harmonisation XGBoost Optuna |
| 🌍 | Federated Learning – 16 African Countries | Privacy-preserving FL with FedProx + DP-SGD on DHS data | FedProx DP-SGD Flower |
| 🇧🇷 | Federated Learning – Brazil | pFedMe + Flower across 5 Brazilian macroregions | pFedMe Flower |
| 🔄 | Transfer Learning – Africa to Brazil | Cross-continent deep learning fine-tuning | Transfer Learning Fine-tuning |
| 📊 | XGBoost Neonatal Mortality | Avoidable neonatal mortality with explainability | XGBoost SHAP TRIPOD-AI |
| Project | Description | Stack | |
|---|---|---|---|
| 🗺️ | Municipal Perinatal Clustering | Clustering municipalities by perinatal indicators (2012–2023) | k-means Markov RF |
| 📉 | Maternal Mortality Brazil 2000–2024 | Interrupted time-series of maternal mortality trends | ITS Joinpoint |
| 🦟 | Syphilis Trends – Brazil | Temporal trends and spatial patterns of congenital syphilis | Spatial Joinpoint |
| 📋 | Systematic Review – Neonatal ML | SR of ML models for neonatal death & stillbirth prediction | Meta-analysis R |
| 🏥 | Fetal Weight Estimation with ML | ML models for fetal weight prediction | ML Regression |
| Institution | Role |
|---|---|
| London School of Hygiene & Tropical Medicine (LSHTM) | Researcher in Health Data Science |
| PROADI-SUS, Hospital Israelita Albert Einstein | Visiting Researcher |
| MARCH Centre, LSHTM | Research Group Member |
| LABDAPS, University of São Paulo | Research Group Member |
| Rede CoVida, Brazil | Research Group Member |
| UFMS – Epi & Applied Mathematics Group | Research Group Member |
Bridging epidemiology, data science, and global health to generate evidence for policy.