Charles Greenberg
Machine Learning Engineer at Meta
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolCharles Greenberg is a Machine Learning Engineer with 12 years of experience applying deep learning and probabilistic methods to health sensing, biomedical imaging, and large-scale sensor data. After a PhD in Biophysics at UCSF and early physics research, he built clinical-risk and explainable-deep-learning systems at Lumiata, advanced health sensing and bias-mitigation methods at Apple, and now works on next-generation wearables at Meta. He specializes in self-supervised learning, model validation that extrapolates study results to populations, and interpretable approaches that reduce demographic bias in deployed models. Equally comfortable with Bayesian uncertainty modeling from his structural-biology work and production ML pipelines, he blends rigorous research instincts with product-focused delivery in regulated health contexts. An understated strength is his track record of translating complex academic methods (Gaussian processes, Monte Carlo sampling) into robust, explainable systems used by cross-functional teams and executives.
12 years of coding experience
9 years of employment as a software developer
PhD Biophysics, PhD Biophysics at University of California, San FranciscoBachelor’s Degree Physics Mathematics, Bachelor’s Degree Physics Mathematics at University of Chicago
Github Skills (63)
structural-biology10
python-bindings10
protein-structure10
modeling10
python9
orientdb8
sampling7
graph-database7
brain-computer-interface7
relational-databases7
factorization7
database7
sparse7
microscopy6
document-database6
Programming languages (5)
Github contributions (5)
integrativemodeling/gamma-tusc
Aug 2015 - Jan 2016
Contributions:38 pushes, 1 branch in 4 months
salilab/pmi
Nov 2013 - Mar 2018
Python Modeling Interface
Contributions:609 commits, 189 pushes, 143 comments in 4 years 5 months
modelingpythonintegrative-modelingprotein-structurestructural-biology
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