Nathan Eddy Email & Phone Number

Lawrence Township, Nj, Us

Data science leader in oncology, immunology, fibrosis. Leveraging large internal and external datasets spanning clinical data, imaging, time series, NLP, RWD, '-omics', and other modalities to generate meaningful insights driving drug development forward. Machine learning, predictive modeling, statistics, and AI applied to a broad portfolio of initiatives addressing anything from fundamental scientific research questions to enterprise operational challenges.

Boston, Massachusetts, Us

Boston, Massachusetts, Us

Boston, Massachusetts, Us

Building underlying infrastructure and machine learning models to enable the generation of insights at scale from terabytes of ultrahigh-res medical images in conjunction with genomic, clinical, and other streams of data. Implementation of modern computer vision techniques and best practices for varied use cases in digital + computational pathology. Cross-functional engagement between key stakeholders in R&D, product, technology, medical, and other teams to ideate scientific use cases and execute on ML solutions. Evangelize the use of modern statistical and ML methodology to drive value in oncology.

San Francisco, Ca, Us

Developed ACORN, a deep learning platform for codon optimization in synthetic biology. Built framework to enable training deep NLP sequence-to-sequence machine translation models leveraging 100,000+ target/label sequences across different biological expression platforms and to enable inference on unseen sequences. Served model to users via custom Flask application frontend on AWS tech stack.

Built computational models leveraging experimental datasets to learn structure/function relationships of viral pathogens using molecular dynamics/monte carlo methodologies and high performance parallel computing. Unsupervised learning techniques and clustering to learn interpretable and meaningful underlying biological mechanisms from very high-dimensional datasets. Created and analyzed structural models of the chromosome using deep neural networks and statistical inference from genomic datasets.

Houston, Tx, Us

Created models to analyze large-scale functional motions in viral pathogens, resulting in testable predictions about structure/function relationships in influenza and other viral systems. Performed statistical analyses to distill coarse mechanistic information from a large number of atomic degrees of freedom in class I and class II viral fusion proteins. Built upon and customized code-base infrastructure to enable / simplify novel modeling schemes for varied use cases in computational chemistry.

Doctor Of Philosophy (Phd), Physics

Master Of Science - Ms, Physics

Bachelor Of Science (Bs), Engineering Physics