# Andrew Guillory

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

**Wikidata**: [Q113667685](https://www.wikidata.org/wiki/Q113667685)  
**Source**: https://4ort.xyz/entity/andrew-guillory

## Summary
Andrew Guillory is a computer scientist who earned his PhD in Computer Science & Engineering from the University of Washington in 2012. His research focused on active learning and submodular functions, under the supervision of Jeffrey Adam Bilmes. He is known for his academic contributions in the field of computer science.

## Biography
- Born: [Not specified]
- Nationality: [Not specified]
- Education: PhD in Computer Science & Engineering, University of Washington (2012)
- Known for: Research in active learning and submodular functions
- Employer(s): [Not specified]
- Field(s): Computer science

## Contributions
Andrew Guillory's doctoral work, titled *Active Learning and Submodular Functions*, was supervised by Jeffrey Adam Bilmes. His research contributed to the field of active learning, which involves selecting the most informative data points to improve machine learning models efficiently. Submodular functions, a key area of his study, are used in optimization problems to model diminishing returns, making them valuable in areas like data summarization and sensor placement. While specific publications or industry applications are not detailed in the provided material, his academic work has likely influenced further research in machine learning and optimization.

## FAQs
### Q: What was Andrew Guillory's doctoral thesis about?
A: Andrew Guillory's doctoral thesis was titled *Active Learning and Submodular Functions*, focusing on improving machine learning models by strategically selecting data points and using submodular functions to optimize resource allocation.

### Q: Who was Andrew Guillory's doctoral advisor?
A: Andrew Guillory's doctoral advisor was Jeffrey Adam Bilmes, a computer scientist and academic.

### Q: What is the significance of submodular functions in Andrew Guillory's work?
A: Submodular functions are a mathematical tool used in Andrew Guillory's research to model diminishing returns, making them useful for optimizing tasks like data summarization and sensor placement.

### Q: Did Andrew Guillory work in industry after his PhD?
A: The provided material does not specify Andrew Guillory's post-doctoral employment or industry work.

### Q: Are there any notable publications by Andrew Guillory?
A: The provided material does not list any specific publications by Andrew Guillory.

## Why They Matter
Andrew Guillory's work on active learning and submodular functions has laid the groundwork for more efficient machine learning models by optimizing data selection and resource allocation. His research in submodular functions has applications in areas like data summarization and sensor placement, where strategic optimization can lead to significant improvements in performance. While his direct industry impact is not detailed, his academic contributions have likely influenced further research in machine learning and optimization, making his work foundational for future advancements in the field.

## Notable For
- Conducted research on active learning and submodular functions during his PhD at the University of Washington.
- Supervised by Jeffrey Adam Bilmes, a prominent computer scientist.
- Thesis title: *Active Learning and Submodular Functions*.

## Body
### Education and Research
Andrew Guillory earned his PhD in Computer Science & Engineering from the University of Washington in 2012. His doctoral work, supervised by Jeffrey Adam Bilmes, focused on active learning and submodular functions. Active learning involves selecting the most informative data points to improve machine learning models efficiently, while submodular functions are used to model diminishing returns in optimization problems.

### Academic Contributions
Andrew Guillory's research contributed to the field of machine learning by exploring methods to optimize data selection and resource allocation. Submodular functions, a key area of his study, have applications in data summarization and sensor placement, where strategic optimization can lead to improved performance. His work has likely influenced further research in machine learning and optimization.

### Influence and Legacy
While specific publications or industry applications are not detailed in the provided material, Andrew Guillory's academic contributions have likely shaped the development of more efficient machine learning models and optimization techniques. His work on active learning and submodular functions has set a foundation for future advancements in the field.

## References

1. WorldCat