Donald Smith
Computer Scientist
Harrison, Arkansas, United States
Summary
👤
Senior🎓
Top SchoolDonald Smith is a seasoned computer scientist and project management professional with nine years in data analytics roles and a 30+ year career leading engineering, quality assurance, and operations in heavy industries and logistics. He combines deep technical skills in SQL, Teradata, Oracle, MS SQL Server, Power BI and predictive analytics with hands-on program leadership at FedEx where he oversaw large cross-functional initiatives, facility construction, and process-improvement portfolios. A PhD-level researcher in computer science with open-source contributions to Deeplearning4j—where he improved a music-generation MelodyModelingExample—he bridges applied machine learning and production engineering. Known for reducing corrective action backlogs and implementing robust QA programs in high-regulation environments, he brings a disciplined, auditable approach to complex systems and contractor oversight. Based in Harrison, Arkansas, he pairs strategic sourcing and preventive/corrective maintenance expertise with a knack for turning messy data into actionable executive insights.
9 years of coding experience
16 years of employment as a software developer
Diploma, Diploma at Marshall High SchoolGraduate School, Engineering Management, Graduate School, Engineering Management at The University of Alabama in HuntsvilleBachelors, Nuclear Technology, Bachelors, Nuclear Technology at Excelsior College
Github Skills (9)
music-generation10
javas10
deeplearning4j10
lstm10
deep-learning10
java10
backpropagation9
artificial-intelligence9
python5
Programming languages (6)
Github contributions (5)
deeplearning4j/deeplearning4j-examples
Mar 2017 - Dec 2022
Deeplearning4j Examples (DL4J, DL4J Spark, DataVec)
Role in this project:
ML Engineer
ML EngineerContributions:83 commits, 15 PRs, 35 comments in 5 years 9 months
Contributions summary:Donald primarily focused on improving the MelodyModelingExample, a deep learning example for generating music. Their contributions included cleaning up code, improving documentation, and refactoring the code to support more flexible configuration, including turning off truncated backpropagation. They also made changes to related classes, such as MelodyStrings and TestMelodyConversion, to enhance clarity and usability. Further, they moved files around, likely to improve code organization.
deeplearning4jpythonzeppelin-notebookdeep-learningmachine-learning
DonaldAlan/dl4j-examples
Mar 2017 - Dec 2022
Deeplearning4j Examples (DL4J, DL4J Spark, DataVec)
Contributions:65 pushes, 9 branches in 5 years 9 months
deeplearning4jdeep-learningspark-mlmachine-learningspark
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