Felix Rios
Research Fellow at National University of Singapore
Singapore
Summary
👤
Senior🎓
Top SchoolFelix Rios is a research fellow based in Singapore with eight years of experience at the intersection of computational statistics, probabilistic graphical models, and causal inference. He develops Bayesian and computational methods to uncover cause-and-effect structure in complex data, turning learned networks into tools for decision-making, root-cause analysis, and experimental design (e.g., marketing mix modeling and A/B testing). His work spans academia and industry—from building Benchpress, a platform to benchmark ~60 causal discovery algorithms, to leading causal analyses for the GUSTO birth cohort—demonstrating both methodological depth and practical impact. Trained as a PhD in computational and applied mathematics at KTH, he combines rigorous probabilistic modelling with software craftsmanship from earlier full‑stack and Erlang development roles. Notably, he focuses on situations where dependencies change with state, designing algorithms that capture context-dependent causal structures.
8 years of coding experience
6 years of employment as a software developer
Åsö VuxengymnasiumDoctor of Philosophy - PhD, Computational and applied mathematics, mathematical statistics, Doctor of Philosophy - PhD, Computational and applied mathematics, mathematical statistics at KTH Royal Institute of TechnologyScience (elite sports), Science (elite sports) at Katrinelundsgymnasiet
Spanish, Swedish, English
Github Skills (56)
causal10
reproducible-research10
benchmark10
snakemake10
causal-discovery10
incremental-learning10
machine-learning10
graphical-models10
benchmarking10
causality10
causal-inference10
discovery10
predictive-modeling10
probabilistic10
probabilistic-graphical-models10
Programming languages (7)
Github contributions (5)
felixleopoldo/cstrees
Aug 2023 - Aug 2024
A Python library for CStrees
Contributions:5 PRs, 101 pushes, 3 comments in 11 months
bayesian-networkscstreesgraphical-models
felixleopoldo/benchpress
Jan 2020 - Jan 2023
Scalable open-source software to run, develop, and benchmark causal discovery algorithms
Contributions:20 releases, 10 reviews, 227 commits in 3 years
benchmarkingcausalbayesian-networksbenchmarksnakemake-workflow
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