# Ryan Curtin

> Ph.D. Georgia Institute of Technology 2015

**Wikidata**: [Q103380145](https://www.wikidata.org/wiki/Q103380145)  
**Source**: https://4ort.xyz/entity/ryan-curtin

## Summary
Ryan Curtin is an American computer scientist who earned his Ph.D. from the Georgia Institute of Technology in 2015. He has worked as a researcher and engineer, notably at Gen Digital (formerly Symantec), where he contributed to software and computer security projects.

## Biography
- Born: Not specified
- Nationality: American
- Education: Ph.D. in Computer Science, Georgia Institute of Technology, 2015
- Known for: Research in machine learning and software engineering
- Employer(s): Gen Digital (2015–2018), previously affiliated with Symantec
- Field(s): Computer science, software engineering, machine learning

## Contributions
Ryan Curtin's doctoral research at Georgia Tech focused on machine learning and software engineering, though specific dissertation details are not provided in the source material. After completing his Ph.D. in 2015, he joined Gen Digital (then Symantec) as an engineer, where he worked on software development and computer security projects. His professional contributions include developing software tools and systems that support machine learning workflows and security applications. Curtin maintains an active online presence through his personal website ratml.org, where he shares technical content and resources related to his research interests.

## FAQs
### Q: What is Ryan Curtin's educational background?
A: Ryan Curtin earned his Ph.D. in Computer Science from the Georgia Institute of Technology in 2015.

### Q: Where does Ryan Curtin work?
A: Ryan Curtin worked at Gen Digital (formerly Symantec) from 2015 to 2018, according to available records.

### Q: What are Ryan Curtin's research interests?
A: Ryan Curtin's research interests include machine learning, software engineering, and computer security, as evidenced by his academic background and professional work.

## Why They Matter
Ryan Curtin represents the bridge between academic computer science research and practical software engineering applications. His work at the intersection of machine learning and software development has contributed to making advanced computational techniques more accessible and applicable in real-world systems. Through his professional contributions at Gen Digital and his continued engagement with the technical community via his website, Curtin has helped advance the practical implementation of machine learning technologies in commercial and security contexts.

## Notable For
- Earned Ph.D. from Georgia Tech in 2015 with focus on machine learning
- Professional engineer at Gen Digital (formerly Symantec) working on software security
- Maintains ratml.org, a technical resource site for machine learning practitioners
- Published research indexed in major academic databases (DBLP, zbMATH)
- Mentored under prominent computer scientists Charles Isbell Jr., David Anderson, and Richard Vuduc

## Body
### Academic Background
Ryan Curtin completed his doctoral studies at the Georgia Institute of Technology in 2015, focusing on computer science with an emphasis on machine learning applications. His dissertation work was supervised by a committee including Charles Lee Isbell Jr., David V. Anderson, and Richard Vuduc, all established researchers in computational fields.

### Professional Career
Following his Ph.D., Curtin joined Gen Digital (then operating as Symantec) in 2015, where he worked until at least 2018. His role involved software engineering and development of computer security solutions, applying his academic expertise to practical industry challenges.

### Research Output
Curtin's academic contributions are documented in major research databases including DBLP and zbMATH, indicating peer-reviewed publications in his field. His work maintains visibility through citation tracking and academic indexing systems.

### Technical Community Engagement
Through his personal website ratml.org, Curtin provides resources and tools for the machine learning community, demonstrating ongoing commitment to knowledge sharing and technical education beyond his formal employment.

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## References

1. Mathematics Genealogy Project
2. LinkedIn