Pamela M. Chiroque-Solano
Statistician | Machine Learning Researcher | Precision Medicine
Ph.D. in Statistics
Faculty of Informatics and Data Science (FIDS), University of
Regensburg, Germany
📍 Regensburg, Germany | ✉️ ORCID | Google Scholar | ResearchGate
Scientific Profile
I am a statistician dedicated to advancing precision
medicine and personalized healthcare through
the integration of statistical learning,
Bayesian modeling, and artificial
intelligence.
My work focuses on developing interpretable models for predicting
individual treatment effects, enabling more informed and individualized
medical decision-making.
My research bridges advanced statistical theory with modern machine learning paradigms to address complex, real-world data challenges in health, ecology, and environmental monitoring.
These challenges include missing data, time-to-event and longitudinal structures, and non-Gaussian response distributions such as categorical, discrete, or inflated outcomes.
With more than 18 years of continuous research and academic experience, my expertise includes:
- Advanced Statistical Modeling – longitudinal data
analysis, survival models, time-varying coefficients
- Machine Learning & AI – predictive modeling,
meta-heuristic feature selection, explainable AI
- Bayesian Inference & Dynamic & Hierarchical
Models – uncertainty quantification and multi-level data
integration
- Applied Domains – precision medicine, environmental monitoring, and epidemiology.
Career Highlights
- Ph.D. Researcher (since 2023) – University of
Regensburg, Faculty of Informatics and Data Science, Germany
- Tenure Professor of Statistics (2020–2022) –
Universidade Federal Rural do Rio de Janeiro (UFRRJ),
Brazil
- Ph.D. Researcher (2021–2022) – CITAB &
UTAD, Portugal
- Postdoctoral Fellow (2018–2020) – University of Lisbon, Portugal and Universidade Federal do Rio de Janeiro (UFRJ), Brazil
- Undergraduate Professor in Statistics
(2018–2019)
Department of Statistical Methods, Institute of Mathematics, UFRJ, Brazil. - Statistician, Kantar Media Research (2007–2012) – Social Media & Market Data, Perú
Research Themes
Keywords: Bayesian Learning • Precision Medicine • Personalized Treatment Effect Prediction • Compositional Data • Environmental Statistics • Hierarchical Models • Statistical Machine Learning • Causal Inference