Marilia Barandas
Senior Scientist at Fraunhofer Portugal AICOS
Lisbon, Lisbon, Portugal
Summary
🤩
Rockstar🎓
Top SchoolMarilia Barandas is a Senior Scientist specializing in indoor positioning and sensor-based solutions, with seven years of research experience at Fraunhofer Portugal AICOS and a strong academic background in biomedical engineering (MSc, PhD candidate). She has developed probabilistic and particle-filtering algorithms for multisensor fusion, fingerprinting methods using magnetic and WiFi data, and practical smartphone inertial-sensor applications for precise indoor localization. Earlier roles combined image processing, single-cell biology tools and teaching, giving her a rare blend of biomedical, signal-processing and software engineering skills. She contributes to open-source time-series tooling—improving feature extraction and HAR examples in the TSFEL library—which underscores her applied data-science focus. A visiting researcher in Milan and a history of building reproducible analysis pipelines reflect her commitment to rigorous, transferable research. Colleagues describe her as a pragmatic researcher who turns complex sensor and imaging problems into deployable, well-tested solutions.
7 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD, Biomedical Engineering, Doctor of Philosophy - PhD, Biomedical Engineering at Faculdade de Ciências e Tecnologia da Universidade NOVA de Lisboa
Github Skills (10)
pandas10
time-series10
feature-engineering10
python10
data-science9
scikit-learn9
feature-extraction9
scikit9
machine-learning9
testing7
Programming languages (1)
Github contributions (5)
fraunhoferportugal/tsfel
Mar 2019 - May 2022
An intuitive library to extract features from time series.
Role in this project:
Data Scientist
Data ScientistContributions:49 releases, 7 reviews, 127 commits in 3 years 2 months
Contributions summary:Marilia appears to be involved in the development of a time series feature extraction library. Their contributions center on enhancing the example notebook for human activity recognition (HAR) by integrating new features and correcting existing features with a focus on improving dataset preparation and the overall flow of the example. They also addressed a bug related to the histogram feature and incorporated improvements to the correlation report method and unit tests for the library.
time-series-analysisfeature-extractiondata-sciencemachine-learningintuitive
mbarandas/nova-aaeb
Sep 2021 - Nov 2021
Contributions:5 pushes, 1 branch in 2 months
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