# Jonathan Bragg

> Ph.D. University of Washington 2018

**Wikidata**: [Q103311380](https://www.wikidata.org/wiki/Q103311380)  
**Source**: https://4ort.xyz/entity/jonathan-bragg

## Summary
Jonathan Bragg is a computer scientist who earned his Ph.D. from the University of Washington in 2018. He is known for his work in crowdsourcing and adaptive distributed systems, particularly his doctoral thesis on self-improving crowdsourcing.

## Biography
- Born: [Not available in source material]
- Nationality: [Not available in source material]
- Education: Ph.D. in Computer Science, University of Washington (2018)
- Known for: Research in self-improving crowdsourcing and adaptive distributed work
- Employer(s): [Not available in source material]
- Field(s): Computer Science

## Contributions
Jonathan Bragg's primary contribution is his doctoral thesis, *Self-improving Crowdsourcing: Near-effortless Design of Adaptive Distributed Work*, completed in 2018 under the supervision of Daniel S. Weld and Mausam. His research focuses on improving crowdsourcing systems by making them adaptive and self-optimizing, reducing the need for manual intervention. This work advances the field of distributed computing and human-computer collaboration, offering scalable solutions for complex tasks.

## FAQs
### Q: What is Jonathan Bragg known for?
A: Jonathan Bragg is known for his research in self-improving crowdsourcing, particularly his doctoral thesis on adaptive distributed work systems.

### Q: Where did Jonathan Bragg earn his Ph.D.?
A: He earned his Ph.D. in Computer Science from the University of Washington in 2018.

### Q: Who were Jonathan Bragg's doctoral advisors?
A: His advisors were Daniel S. Weld and Mausam, both prominent computer scientists at the University of Washington.

### Q: What is the focus of Jonathan Bragg's research?
A: His research focuses on crowdsourcing systems that can self-improve and adapt, reducing the need for manual design and intervention.

## Why They Matter
Jonathan Bragg's work on self-improving crowdsourcing systems addresses a key challenge in distributed computing: efficiently managing large-scale human and machine collaboration. His research provides frameworks for adaptive systems that can dynamically optimize task allocation, improving efficiency and scalability. This has implications for industries relying on crowdsourcing, from data annotation to complex problem-solving. His contributions build on the work of his advisors, Daniel S. Weld and Mausam, furthering the field of AI-driven distributed systems.

## Notable For
- Author of the doctoral thesis *Self-improving Crowdsourcing: Near-effortless Design of Adaptive Distributed Work* (2018).
- Student of Daniel S. Weld and Mausam, influential figures in computer science.
- Researcher in adaptive and self-optimizing crowdsourcing systems.

## Body
### Education and Academic Background
- **Ph.D. in Computer Science**: University of Washington, 2018.
  - Thesis: *Self-improving Crowdsourcing: Near-effortless Design of Adaptive Distributed Work*.
  - Advisors: Daniel S. Weld and Mausam.

### Research Focus
- **Crowdsourcing Systems**: Bragg's work explores how crowdsourcing platforms can adapt and improve autonomously.
- **Adaptive Distributed Work**: His research aims to minimize manual effort in designing and managing distributed tasks.

### Advisors and Influences
- **Daniel S. Weld**: A leading computer scientist known for work in AI and human-computer interaction.
- **Mausam**: A computer scientist specializing in AI and crowdsourcing, also a Ph.D. graduate of the University of Washington (2007).

## Schema Markup
```json
{
  "@context": "https://schema.org",
  "@type": "Person",
  "name": "Jonathan Bragg",
  "jobTitle": "Computer Scientist",
  "alumniOf": [{"@type": "EducationalOrganization", "name": "University of Washington"}],
  "knowsAbout": ["Computer Science", "Crowdsourcing", "Distributed Systems"],
  "description": "Computer scientist known for research in self-improving crowdsourcing systems."
}

## References

1. Mathematics Genealogy Project
2. WorldCat