# Kayur Dushyant Patel

> PhD, University of Washington, Computer Science & Engineering, 2012

**Wikidata**: [Q113667699](https://www.wikidata.org/wiki/Q113667699)  
**Source**: https://4ort.xyz/entity/kayur-dushyant-patel

## Summary
Kayur Dushyant Patel is a computer scientist who earned a Doctor of Philosophy (PhD) in Computer Science and Engineering from the University of Washington in 2012. He is known for his doctoral research on simplifying the application of machine learning, completed under the supervision of American computer scientist and professor James A. Fogarty.

## Biography
*   **Education:** PhD, Computer Science & Engineering, University of Washington (2012)
*   **Field(s):** Computer Science; Machine Learning
*   **Known for:** Research on lowering the barrier to applying machine learning
*   **Doctoral Advisor:** James A. Fogarty

## Contributions
Kayur Dushyant Patel's primary documented contribution is his doctoral research conducted at the University of Washington. In 2012, he completed his PhD thesis titled **"Lowering the Barrier to Applying Machine Learning."** This work focused on methods to make machine learning techniques more accessible and easier to apply, likely addressing usability challenges within the field. His research was supervised by James A. Fogarty, a noted figure in computer science and academia. While specific product launches or subsequent industrial applications are not detailed in the provided source material, his academic work contributes to the broader discourse on human-computer interaction and data science usability.

## FAQs
### Q: What is Kayur Dushyant Patel's educational background?
A: Kayur Dushyant Patel holds a Doctor of Philosophy (PhD) in Computer Science and Engineering from the University of Washington, which he completed in 2012.

### Q: What was the topic of his doctoral thesis?
A: His doctoral thesis was titled "Lowering the Barrier to Applying Machine Learning."

### Q: Who was Kayur Dushyant Patel's doctoral advisor?
A: His doctoral advisor was James A. Fogarty, an American computer scientist and professor.

## Why They Matter
Kayur Dushyant Patel represents a generation of researchers focused on the democratization of advanced computational tools. His 2012 thesis, "Lowering the Barrier to Applying Machine Learning," addressed a critical bottleneck in the field: the difficulty non-experts face when trying to utilize complex machine learning algorithms. By investigating how to make these powerful tools more accessible, his work contributes to the ongoing effort to integrate intelligent systems into broader software development and data analysis practices. His academic lineage, studying under established academic James A. Fogarty at a major research institution like the University of Washington, places his work within a significant tradition of computer science research aimed at solving practical engineering and usability problems.

## Notable For
*   Completing a PhD in Computer Science & Engineering at the University of Washington in 2012.
*   Authoring the thesis "Lowering the Barrier to Applying Machine Learning."
*   Conducting research under advisor James A. Fogarty.
*   Being recognized as a computer scientist in academic and knowledge base records.

## Body

### Academic Background
Kayur Dushyant Patel is an individual classified as a human and a computer scientist. He pursued his higher education in the United States at the University of Washington. He successfully earned his academic degree of Doctorate (PhD) in 2012. His specific field of study was Computer Science and Engineering.

### Doctoral Research
During his time at the University of Washington, Patel was a student of James A. Fogarty, a prominent American computer scientist and academic. Patel's culminating academic work was his thesis, **"Lowering the Barrier to Applying Machine Learning."** This research was formally recognized and cataloged within academic and Wikidata structures, highlighting its relevance to the field of computer science. The work implies a focus on the "service sector" or "industrial sector" applications of computing, as noted in related knowledge classifications.

## References

1. WorldCat