Summary
Matthew Amodio is a postdoctoral researcher and machine learning specialist based in Cambridge, MA, with 11 years of experience applying predictive models to massive, messy datasets across computational biology, NLP, social networks, geospatial routing, cybersecurity, and advertising. He earned a PhD focused on AI and combines rigorous academic research with hands-on engineering—building end-to-end pipelines, custom parallelized algorithms, and scalable data-processing solutions. Past roles at Broad Institute, Yale, Booz Allen, IBM, and UW–Madison show a pattern of translating research into production-ready models for real-time bidding, network graph analysis, and large-scale text mining. He is equally comfortable diving into feature engineering and low-level optimization as he is communicating findings to non-technical stakeholders. Outside work he applies the same data-driven curiosity to baseball analytics, reflecting a practical appetite for exploratory modeling and reproducible code. This blend of deep research training and applied systems experience makes him adept at tackling complex, cross-domain ML problems that require both theory and engineering.
11 years of coding experience
8 years of employment as a software developer
Master of Science - MS, Artificial Intelligence, Master of Science - MS, Artificial Intelligence at University of Wisconsin-Madison
Doctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at Yale University
Master of Science (MS), Applied Statistics, Master of Science (MS), Applied Statistics at The Ohio State University