Alexandros Rekkas
Research Associate
Thessaloniki, Macedonia and Thrace, Greece
Summary
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Senior🎓
Top SchoolAlexandros Rekkas is a Research Associate with eight years of experience bridging statistics, mathematics and medical informatics to study treatment effect heterogeneity from observational data. He holds a PhD from Erasmus University, an MSc in Statistics (cum laude) from KU Leuven, and a BSc in Mathematics from Aristotle University of Thessaloniki, combining rigorous theoretical training with applied research. His work focuses on risk-based approaches, external validation and transportability of causal inference methods, translating complex methodological advances into tools for real-world biosciences. Based in Thessaloniki, he contributes to interdisciplinary teams at INAB and brings a rare blend of statistical rigor and practical insight into observational study challenges. An understated strength is his focus on method interoperability—ensuring techniques generalize across datasets and settings rather than remaining academic proofs of concept.
8 years of coding experience
Master of Science - MS, Statistics, Cum laude, Master of Science - MS, Statistics, Cum laude at KU LeuvenBachelor's degree, Mathematics, Bachelor's degree, Mathematics at Aristotle University of Thessaloniki (AUTH)Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at Erasmus University Rotterdam
Github Skills (20)
r-package10
correlation10
mixed-models10
meta-analysis10
prediction9
shiny-apps8
r-language8
sampling8
shiny-server8
statistical-learning7
database7
shiny6
machine-learning6
data-model6
prediction-model6
Programming languages (3)
Github contributions (5)
mi-erasmusmc/COPE
Sep 2020 - Jun 2023
A simple tool based on 6 routinely measured predictors in the Emergency Department (ED) that is well able to predict mortality and ICU admission for patients who present to the ED with suspected COVID-19.
Contributions:20 PRs, 100 pushes, 19 branches in 2 years 9 months
pythonemergencypatientsmachine-learningpredict
rekkasa/SimulateHte
Jan 2021 - Feb 2025
An R-package that simulates data for the assessment of treatment effect heterogeneity.
Contributions:15 PRs, 30 pushes, 11 branches in 4 years 1 month
r-packageassessmenttreatmentheterogeneity
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