# Suzanne Bunton

> Ph.D. University of Washington 1996

**Wikidata**: [Q102251109](https://www.wikidata.org/wiki/Q102251109)  
**Source**: https://4ort.xyz/entity/suzanne-bunton

## Summary
Suzanne Bunton is a computer scientist who earned her Ph.D. in computer science from the University of Washington in 1996. She is recognized for her doctoral research on online stochastic processes in data compression, conducted under the supervision of notable computer scientists Gaetano Borriello and Richard E. Ladner.

## Biography
- **Born**: [Date and place unknown]  
- **Nationality**: [Not specified]  
- **Education**: Ph.D. in computer science, University of Washington (1996)  
- **Known for**: Doctoral thesis on "On-line Stochastic Processes in Data Compression"  
- **Employer(s)**: [Not specified]  
- **Field(s)**: Computer science, data compression  

## Contributions
Suzanne Bunton’s primary contribution is her doctoral thesis, *On-line Stochastic Processes in Data Compression*, completed in 1996 at the University of Washington. This work explores stochastic methods applied to data compression, a foundational area in computer science. Her research was supervised by Gaetano Borriello and Richard E. Ladner, both prominent figures in the field. While specific technical outcomes or publications beyond her dissertation are not detailed in the source material, her work aligns with broader efforts to optimize data representation and transmission efficiency. The thesis reflects interdisciplinary engagement with both computer science and engineering principles, as indicated by her academic qualifiers.

## FAQs
### Q: Where did Suzanne Bunton earn her Ph.D.?
A: She received her Ph.D. from the University of Washington in 1996.  

### Q: What is Suzanne Bunton known for?
A: She is known for her doctoral research on stochastic processes in data compression.  

### Q: Who supervised her graduate work?
A: Her doctoral advisors were Gaetano Borriello and Richard E. Ladner.  

## Why They Matter
Suzanne Bunton’s research contributes to the foundational understanding of data compression, a critical area in computer science with applications in storage, communication, and information processing. Her work, guided by leading experts in the field, reflects the interdisciplinary nature of computer science and engineering. While the direct impact of her thesis is not explicitly documented in the provided sources, her focus on stochastic methods underscores the importance of probabilistic approaches in optimizing data efficiency. Her academic achievement also highlights the University of Washington’s role in advancing computer science research during the 1990s.

## Notable For
- Completed her Ph.D. in computer science at the University of Washington (1996).  
- Authored the thesis *On-line Stochastic Processes in Data Compression*.  
- Advised by renowned computer scientists Gaetano Borriello and Richard E. Ladner.  

## Body
### Academic Career
Suzanne Bunton pursued her graduate studies at the University of Washington, where she earned her doctorate in 1996. Her academic focus spanned computer science and computer engineering, culminating in a thesis that bridged theoretical stochastic processes with practical data compression applications.

### Doctoral Research
Her dissertation, *On-line Stochastic Processes in Data Compression*, investigated the application of stochastic (random process) models to improve data compression techniques. This research addressed challenges in efficiently encoding and decoding data streams, a topic of growing relevance in the 1990s as digital data volumes expanded rapidly.

### Advisors
Bunton’s graduate work was supervised by two distinguished computer scientists:  
- **Gaetano Borriello** (Ph.D., University of California, Berkeley, 1988), a specialist in embedded systems and wireless sensor networks.  
- **Richard E. Ladner**, an expert in accessible computing and algorithms.  
Both advisors were affiliated with the University of Washington, contributing to its reputation for innovation in computer science.

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## References

1. Mathematics Genealogy Project
2. WorldCat