Timothy Emerick
Director Of Innovation
Charlottesville, Virginia, United States
Summary
👤
Senior🎓
Top SchoolTimothy Emerick is a Director of Innovation and data scientist with 12 years of experience blending advanced mathematics and applied machine learning to drive research-to-product impact at General Atomics Integrated Intelligence. He holds a PhD in Mathematics from the University of Virginia and is skilled in deep learning, computer vision, NLP, and statistical modeling, pairing theoretical rigor with production engineering in Python. Timothy has contributed performance-critical improvements to the widely used gensim library—optimizing doc2vec implementations with Cython—demonstrating hands-on open-source impact in topic modeling. He also brings teaching and research experience from academia, having instructed statistics and ODEs while conducting graduate research, which helps him communicate complex ideas clearly to multidisciplinary teams. Based in Charlottesville, he combines mathematical depth with practical system-level optimizations to move novel algorithms into operational use.
12 years of coding experience
6 years of employment as a software developer
California Polytechnic State University, San Luis ObispoDoctor of Philosophy (PhD) Mathematics, Doctor of Philosophy (PhD) Mathematics at University of Virginia
Github Skills (9)
machine-learning10
gensim10
word-embeddings10
topic-modeling10
nlp10
python10
natural-language-processing10
cython10
data-science9
Programming languages (4)
Github contributions (5)
piskvorky/gensim
Jul 2014 - Jan 2015
Topic Modelling for Humans
Role in this project:
ML Engineer & Backend Developer
ML Engineer & Backend DeveloperContributions:23 commits, 1 PR, 1 comment in 5 months
Contributions summary:Timothy primarily contributed to the development of doc2vec models, adding, fixing, and optimizing their implementation within the gensim library. Their work included the integration of Cython for performance improvements, specifically for the distributed bag-of-words (DBOW) and distributed memory (DM) models. The commits demonstrate a focus on improving the efficiency and functionality of the library's topic modeling capabilities, including both the skip-gram (SG) and continuous bag-of-words (CBOW) models. They also addressed bugs in the CBOW implementation.
pythonword-similarityword-embeddingsdata-miningfor-humans
temerick/calcdb
Sep 2013 - May 2017
Contributions:4 commits, 2 PRs, 1 push in 3 years 8 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.