Daniel Worley
President at Foggy Mountain Data Services
Greater Charlottesville Area United States
Summary
🤩
Rockstar🎓
Top SchoolDaniel Worley is a seasoned software engineer and founder with over a decade of experience building mobile apps, custom web solutions, and data-mining/statistical systems, currently leading Foggy Mountain Data Services. He combines hands-on development in Java, C#, Python, PHP and modern web stacks with system administration across Linux, macOS and Windows to deliver stable, scalable products. At OpenSource Connections and previous roles he architected high-performance search and crawling systems using Lucene and AJAX, and contributed advanced scoring/statistical features to the popular Elasticsearch Learning to Rank plugin. Comfortable leading product-focused teams and working closely with designers, he has a track record of translating complex research requirements into practical, user-centered applications. Based in the Greater Charlottesville area, he brings both entrepreneurial grit and a penchant for graphics and search optimization that surfaces in his open-source work.
12 years of coding experience
14 years of employment as a software developer
BS Computer Science, BS Computer Science at James Madison University
Github Skills (11)
javas10
elasticsearch10
elasticsearchquery10
aws-elasticsearch10
java10
elasticsearch-api10
amazon-elasticsearch10
machine-learning9
lucene9
architecture8
architectures8
Programming languages (16)
Github contributions (5)
o19s/elasticsearch-learning-to-rank
Jun 2017 - Jan 2023
Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch
Role in this project:
Backend Developer
Backend DeveloperContributions:2 releases, 8 reviews, 103 commits in 5 years 7 months
Contributions summary:Daniel implemented new statistical scoring features within the Elasticsearch Learning to Rank plugin. These features involved the creation of a new query builder and sub-phase to calculate and expose term frequency and inverse document frequency statistics. The changes included modifications to existing classes like `ExplorerQueryBuilder`, `ExplorerFetchSubPhase`, and `ExplorerQuery` to incorporate the new scoring logic and data retrieval. Further modifications involved cleanup of various features to optimize code and improve scoring.
elasticsearchmachine-learningrelevant-searchsearch-relevanceelasticsearch-plugin
worleydl/fb-peek
Jan 2017 - Jul 2018
Contributions:11 commits, 1 push in 1 year 6 months
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