Kent Gauen is a statistical machine learning and deep learning researcher and engineer with a decade of experience blending academic rigor and applied data science from Purdue University. He leads research on dynamic image reconstruction via contrastive learning and develops MCMC inference tools for Hawkes processes, with a track record of building reproducible Python/PyTorch frameworks that balance predictive power and uncertainty. Kent has led student consulting teams on industry-funded projects, translating research methods into production-ready code and documentation. His background spans computer vision for network camera data, recurrent text classification prototypes at Nielsen, and early chemistry and spectroscopy research—evidence of a multidisciplinary approach to problem solving. Based in Indianapolis, he’s pursuing a PhD in Statistical Machine Learning while continuing to bridge theoretical advances with practical systems that perform well with limited data and compute.
10 years of coding experience
1 year of employment as a software developer
Lawrence North High School
Bachelor of Applied Science (B.A.Sc.), Computer Engineering and Statistical Mathematics, Bachelor of Applied Science (B.A.Sc.), Computer Engineering and Statistical Mathematics at Purdue University
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Kent Gauen - Statistical Machine Learning And Deep Learning