Alexandros Rekkas

Thessaloniki, Macedonia and Thrace, Greece

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Summary

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Senior

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Top School

Alexandros 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, Master of Science - MS Statistics at KU Leuven

Bachelor'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 (11)

r-package10

shiny-apps8

shiny-server8

predict7

prediction-model6

database6

python3

icu3

machine-learning2

diabetes2

Programming languages (3)

RHTMLPerl

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

mi-erasmusmc/RiskStratifiedEstimation

Jun 2018 - Oct 2020

[Under development] An R package for performing effect estimation in risk strata derived from predictive models.

Contributions:224 pushes, 8 branches, 1 comment in 2 years 4 months

r-packagestatisticsriskpredictive-modelsderived

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