Miriam Santos
Assistant Professor at Faculdade de Ciências da Universidade do Porto | FCUP
Coimbra, Coimbra, Portugal
Summary
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Rockstar🎓
Top SchoolMiriam Santos is an Assistant Professor and data scientist who specializes in turning messy, biased, and underrepresented datasets into high-quality inputs for trustworthy machine learning across fintech, healthcare, telecom and retail. With a PhD focused on missingness, class imbalance and overlap, she has published 20+ papers with hundreds of citations and a noted article in Information Fusion that reframes class overlap and imbalance. She blends rigorous academic research with product-facing roles—previously at YData and multiple clinical projects—bringing both conceptual solutions and hands-on preprocessing, imputation and evaluation expertise. A self-described "data skeptic," Miriam prioritizes causality, explainability and fairness over blind performance gains, and is actively engaged in Responsible AI. Outside academia she founded and scaled As Raparigas do Código into a 400+ member non-profit running 60+ workshops, demonstrating her commitment to inclusive tech leadership and community impact.
5 years of coding experience
10 years of employment as a software developer
PhD in Informatics Engineering (Intelligent Systems) Artificial Intelligence, PhD in Informatics Engineering (Intelligent Systems) Artificial Intelligence at Universidade de Coimbra
Github Skills (56)
data-quality10
data-exploration10
python10
jupyter10
data-science10
ria10
pandas10
statistics10
machine-learning10
pandas-profiling10
synthetic10
exploratory-data-analysis10
pandas-dataframe10
generative-adversarial-network10
dataframe10
Programming languages (6)
Github contributions (5)
miriamspsantos/open-source-imbalance-overlap
Jul 2021 - Dec 2021
Contributions:7 commits in 5 months
miriamspsantos/pycol
Dec 2021 - Feb 2022
The Python Class Overlap Libray (pycol) assembles a comprehensive set of complexity measures associated with the characterization of the Class Overlap problem.
Contributions:9 commits, 10 pushes, 1 branch in 1 month
characterizationmachinelearning-pythonpythonmeta-learningcomplexity
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