Nina Miolane
Assistant Professor, Biomedical Imaging, Electrical And Computer Engineering at Atmo
San Francisco Bay Area United States
Summary
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Rockstar🎓
Top SchoolNina Miolane is an assistant professor of Electrical and Computer Engineering at UC Santa Barbara with a decade of experience at the intersection of geometric statistics, biomedical imaging, and computational medicine. Her research and lab leadership focus on modeling and analyzing complex heterogeneous data—bringing rigorous differential-geometry and statistical theory to practical problems like neuroimaging and surgical planning. She has translated theory into open-source tools (notably contributions to the geomstats library, including tutorials, datasets for cell shapes, and geodesic regression code) and previously applied her skills in industry roles at Bay Labs and as an advisor to startups. Trained at École Polytechnique, Imperial College, and Stanford, she pairs deep mathematical foundations with hands-on software engineering and a track record of mentoring interdisciplinary teams.
10 years of coding experience
1 year of employment as a software developer
Master's degree, Theoretical and Mathematical Physics, Master's degree, Theoretical and Mathematical Physics at Imperial College LondonMathematics, Mathematics at Lycée Sainte-GenevièveMaster's degree, Theoretical Physics, Master's degree, Theoretical Physics at Ecole polytechniquePostgraduate Degree, MATHEMATICS AND STATISTICS, Postgraduate Degree, MATHEMATICS AND STATISTICS at Stanford University
French, English, German, Italian, Spanish, Japanese
Github Skills (7)
python10
data-analysis10
jupyter-notebook9
machine-learning9
data-visualization8
data-visualisation8
data-visualizations8
Programming languages (8)
Github contributions (5)
geomstats/geomstats
Oct 2017 - Jan 2023
Computations and statistics on manifolds with geometric structures.
Role in this project:
Backend Developer & Data Scientist
Backend Developer & Data ScientistContributions:19 releases, 784 reviews, 3248 commits in 5 years 3 months
Contributions summary:Nina implemented the first tutorial notebook for the geomstats project. They added the data for cell shapes and updated existing datasets. Their changes involved the implementation of core library functions, and they contributed to examples and usage documentation. Further, they worked on a section of the code for geodesic regression and made significant modifications and improvements.
gpu-programmingstatisticslie-groupsdeep-learningneural-networks
pyt-team/TopoModelX
Apr 2023 - Jun 2024
Topological Deep Learning
Contributions:1 release, 71 reviews, 186 PRs in 1 year 1 month
cell-complex-networkscell-complex-neural-networkscell-neural-networkscellular-message-passingcw-complex
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