Pat Verga
Senior Staff Member Of Technical Staff at Cohere
Pittsburgh, Pennsylvania, United States
Summary
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Senior🎓
Top SchoolPat Verga is a Senior Staff Member of Technical Staff at Cohere with 11 years of experience specializing in NLP and machine learning research. He holds advanced degrees from UMass Amherst including a PhD in Computer Science and has held research roles at Google and DeepMind, bridging cutting-edge research with product-forward engineering. Pat contributes to deployable probabilistic modeling tooling—evidenced by backend fixes to the widely used FACTORIE Scala library—showing attention to inference algorithms and robust, production-ready code. Based in Pittsburgh, he focuses on making research systems practical and scalable, combining deep academic grounding with hands-on improvements to open-source NLP infrastructure.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of Massachusetts Amherst
Github Skills (14)
scala10
data-modeling10
probabilistic-programming10
statistical-models10
probabilistic-reasoning10
probabilistic-models10
natural-language-processing9
nlp9
code-library9
lib9
inference8
algorithms8
graph7
factors7
Programming languages (2)
Github contributions (5)
factorie/factorie
Sep 2014 - Mar 2016
FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:37 commits, 8 PRs in 1 year 6 months
Contributions summary:Pat primarily contributed to bug fixes and code improvements within the Factorie toolkit, a Scala-based library for probabilistic modeling. The commits reveal changes related to inference algorithms, specifically fixing a bug in the SparseLDAInferencer and optimizing the NPChunkMentionFinder. These modifications indicate a focus on improving the library's functionality and addressing specific issues within the core probabilistic modeling components. The contributions suggest an understanding of the Factorie framework's internal workings and its application to natural language processing tasks.
library-softwareparametersrelationaldeployableprobabilistic-modeling
patverga/multilingual-universal-schema
Mar 2015 - Apr 2015
Contributions:200 pushes in 1 month
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