Derek Prijatelj is a researcher and machine learning engineer with a Ph.D. from the University of Notre Dame and a decade of experience exploring the interface of information theory and ML. His work focuses on novelty detection, open set/world recognition, and principled Bayesian evaluation of uncertainty in classification models, with practical implementations tested across diverse neural architectures. He has applied these skills in academic and applied settings—from AFRL and NSF-funded NLP projects to designing production-aware NLP architecture for Rotella Qdeck—bridging theory and deployable systems. Unusually, he combines deep theoretical grounding with hands-on system design and model calibration using tools like VAEs, reflecting a pragmatic eye for what methods are actually useful in practice.
10 years of coding experience
1 year of employment as a software developer
Bachelor’s Degree, Bachelor’s Degree at Duquesne University
High School, High School at Mars Area School District
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at University of Notre Dame
Contributions:1 PR, 28 pushes, 2 branches in 1 year 5 months
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