Cher Bass
Machine Learning Scientist at Panakeia
Stony Stratford, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolCher Bass is a Machine Learning Scientist with nine years’ experience applying interpretable deep learning and generative models to biomedical imaging and neuroscience problems. She led the development of an interpretable 3D MRI classification model—published at NeurIPS 2020—working with ADNI, UK Biobank and HCP datasets and now adapting those methods for neonatal age and cognitive prediction. Her background spans a PhD in Neurotechnology, hands-on experimental neuroscience, and software development for VAEs, GANs and CNNs, enabling a rare combination of wet-lab insight and ML engineering. At King’s College London she combined research, teaching and group organisation, and currently advances ML research at Panakeia. She is comfortable supervising students, producing teaching materials, and translating complex models into clinically relevant tools. Beyond publications, her profile reflects a practical focus on interpretable, deployable models for early detection of neurocognitive impairment.
9 years of coding experience
3 years of employment as a software developer
MRes, Neurotechnology, Merit, MRes, Neurotechnology, Merit at Imperial College LondonHaydon SchoolBachelor's Degree, Neuroscience, 2:1, Bachelor's Degree, Neuroscience, 2:1 at University of Bristol
English, Hebrew, Japanese
Github Skills (6)
representations10
adversarial-learning10
classification9
xai9
pytorch7
deep-learning7
Programming languages (2)
Github contributions (5)
CherBass/ICAM
May 2020 - Aug 2022
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping
Contributions:1 review, 63 commits, 1 PR in 2 years 3 months
pytorchadversarial-learningrepresentationsxaideep-learning
CherBass/TimeseriesCalciumAnalysis
Feb 2017 - Sep 2017
Contributions:53 pushes, 6 branches in 7 months
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