Raymond Essick
Associate Teaching Professor
Silver Spring, Maryland, United States
Summary
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Senior🎓
Top SchoolRaymond Essick is an Associate Teaching Professor and Graduate Program Manager in Computer Science at Georgetown University with a decade of experience bridging engineering research and software development. He holds a Ph.D. in Mechanical Engineering from the University of Illinois and taught a wide range of courses from dynamics to control and signal processing before transitioning into computer science education. Raymond combines academic rigor with hands-on full‑stack development—contributing UI and back-end improvements to the open-source PrairieLearn online problem-driven learning system. He has a track record of developing teaching software and course administration tools, and he previously led coordination of large introductory mechanics sequences. Based in Silver Spring, MD, he brings a blend of control-theoretic research experience and practical software engineering to curriculum design and graduate program operations. A not-obvious strength is his history of developing parallel computing tools for controller synthesis, reflecting comfort with both theoretical and practical high-performance implementations.
10 years of coding experience
9 years of employment as a software developer
University of Illinois Urbana-Champaign
Github Skills (9)
javascript10
ejs10
database9
autograd9
back-end-development9
database-api9
databases9
html8
css6
Programming languages (1)
Github contributions (4)
PrairieLearn/PrairieLearn
Sep 2016 - Aug 2018
Online problem-driven learning system
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:37 commits, 12 PRs, 67 comments in 1 year 10 months
Contributions summary:Raymond primarily focused on enhancing the user interface and functionality of the online learning system. They implemented features for displaying and rendering submissions within the exam interface, working with EJS templates, JavaScript, and database interactions. Furthermore, they refactored code, improved rendering, and cleaned up the user interface by removing unnecessary code and adjusting visual elements. These contributions involved both front-end (EJS) and back-end (database) interactions, demonstrating full-stack capabilities.
machine-learningproblemassessmentdrivingautograders
rbessick5/PrairieLearn
Feb 2016 - Mar 2016
Contributions:5 pushes, 2 branches in 7 days
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