Edderic Ugaddan is a Senior Machine Learning Engineer with 12 years of experience building production ML systems, data pipelines, and full-stack tooling across geospatial and public-health domains. He combines deep probabilistic modeling (Bayesian priors, custom Bayesian regression) and sequence methods (HMMs, PyTorch NER) with pragmatic engineering—deploying models and pipelines via Python, Ruby, and AWS. At dataplor he productionized location-hierarchy inference, and at Breathesafe he created a mask-recommender, a Wells–Riley venue-risk tool, and an open-source 3D-printable air cleaner backed by independent testing and philanthropic funding. Previously he scaled analytics and data lakes at Panorama, processing billions of K–12 records to inform product and research decisions. Comfortable spanning research, ML engineering, and full-stack delivery, he often pairs statistical rigor with product-focused optimization. He’s based in East Providence, RI, and brings a hands-on mix of experimentation, causal thinking, and deployment experience that surfaces in both code and physical prototypes.
12 years of coding experience
11 years of employment as a software developer
SEEDS - Access Changes Everything
Bachelor of Arts (B.A.) Computer Science, Bachelor of Arts (B.A.) Computer Science at University of Richmond
Machine Learning Engineer Nanodegree, Machine Learning Engineer Nanodegree at Udacity
Contributions:115 commits, 57 pushes, 2 branches in 6 years
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