Aina Frau-pascual
Data Scientist
Palma, Balearic Islands, Spain
Summary
👤
Senior🎓
Top SchoolAina Frau-pascual is a data scientist with 12 years of experience applying signal processing, machine learning and statistical modeling to healthcare and brain imaging. With a PhD in applied mathematics and postdoctoral work at Harvard/MGH, she has developed MRI analysis tools and Bayesian models that improved detection of brain activity and structural connectivity. She brings hands-on expertise extracting physiological signals from both contact and contactless phone recordings and building analytics and AI models for perinatal mental health in product settings. Aina contributes to quality and test automation in notable open-source neuroimaging software (nilearn), highlighting her focus on robust, reproducible research software. Based in Palma, she blends academic rigor with product-minded delivery as an adjunct professor and industry data scientist.
12 years of coding experience
1 year of employment as a software developer
MVA Mathématiques / Vision / Apprentissage, Applied Mathematics, MVA Mathématiques / Vision / Apprentissage, Applied Mathematics at ENS Paris-SaclayDoctor of Philosophy (PhD), Applied mathematics, Doctor of Philosophy (PhD), Applied mathematics at Université Grenoble AlpesIngeniería Técnica de Telecomunicación, especialidad en Telemática, Telemática, Ingeniería Técnica de Telecomunicación, especialidad en Telemática, Telemática at Universitat de les Illes BalearsUPC Universitat Politècnica de Catalunya
Spanish, Catalan, English, French
Github Skills (10)
machine-learning10
python10
neuroimaging10
testing10
brain-imaging9
unit-testing8
scikit8
scikit-learn8
connectivity8
pytest7
Programming languages (5)
Github contributions (5)
nilearn/nilearn
Dec 2015 - Dec 2015
Machine learning for NeuroImaging in Python
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:12 commits, 2 PRs, 11 comments in 2 days
Contributions summary:Aina focused on improving the quality and reliability of the nilearn library by implementing and extending testing procedures. Their contributions included adding new tests for image manipulation functions like `copy_img` and `new_img_like`, ensuring these operations have no unintended side effects. They also updated the testing framework by changing the import for `joblib` and addressed several typos in the documentation.
neuroimagingpythondecodingbrain-connectivitymachine-learning
ainafp/FVT4DWI
Aug 2017 - Feb 2019
Quantification of Structural Brain Connectivity via a Conductance Model
Contributions:1 release, 160 commits, 2 PRs in 1 year 6 months
brainconnectivitybrain-connectivitystructuralquantification
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