# Shulin Yang

> PhD, University of Washington, Computer Science & Engineering, 2013

**Wikidata**: [Q113667708](https://www.wikidata.org/wiki/Q113667708)  
**Source**: https://4ort.xyz/entity/shulin-yang

## Summary
Shulin Yang is a computer scientist who earned her PhD in Computer Science & Engineering from the University of Washington in 2013. She is known for her work in feature engineering for fine-grained image classification, supervised by Linda Shapiro.

## Biography
- Born: 1983
- Nationality: United States
- Education: PhD, University of Washington (Computer Science & Engineering, 2013)
- Known for: Research in feature engineering for fine-grained image classification
- Employer(s): University of Washington (as a student)
- Field(s): Computer science

## Contributions
Shulin Yang completed her doctoral research under the supervision of Linda Shapiro at the University of Washington. Her thesis, titled "Feature Engineering in Fine-grained Image Classification," focused on advancing techniques for fine-grained image classification, a subfield of computer vision. While specific contributions from her thesis are not detailed in the provided material, her work likely contributed to the development of more accurate and efficient methods for distinguishing between visually similar objects, which has applications in fields like medical imaging, autonomous systems, and industrial inspection.

## FAQs
### Q: What was Shulin Yang's academic focus?
A: Shulin Yang focused on feature engineering for fine-grained image classification during her PhD at the University of Washington.

### Q: Who was Shulin Yang's doctoral advisor?
A: Shulin Yang's doctoral advisor was Linda Shapiro.

### Q: What degree did Shulin Yang earn?
A: Shulin Yang earned a PhD in Computer Science & Engineering from the University of Washington in 2013.

### Q: What is Shulin Yang known for?
A: Shulin Yang is known for her research in feature engineering for fine-grained image classification.

### Q: Where did Shulin Yang complete her PhD?
A: Shulin Yang completed her PhD at the University of Washington.

## Why They Matter
Shulin Yang's work in feature engineering for fine-grained image classification contributed to advancements in computer vision, particularly in improving the accuracy and efficiency of distinguishing between visually similar objects. Her research likely influenced subsequent studies in automated image analysis, with potential applications in fields requiring high-precision visual recognition, such as healthcare diagnostics or autonomous vehicle systems. While her specific impact is not detailed in the provided material, her contributions align with broader efforts to enhance machine learning models for real-world challenges.

## Notable For
- PhD in Computer Science & Engineering from the University of Washington (2013)
- Research on feature engineering for fine-grained image classification
- Supervised by Linda Shapiro, a prominent American computer scientist
- Worked in the field of computer vision, focusing on image classification techniques

## Body
### Education
Shulin Yang earned her PhD in Computer Science & Engineering from the University of Washington in 2013. Her doctoral studies were supervised by Linda Shapiro, a renowned computer scientist and university teacher.

### Research Focus
Shulin Yang's doctoral thesis, "Feature Engineering in Fine-grained Image Classification," explored techniques to improve the accuracy of distinguishing between visually similar objects. Fine-grained image classification is a specialized area of computer vision that requires high precision, making her work relevant to applications in medical imaging, autonomous systems, and industrial quality control.

### Academic Affiliation
During her PhD, Shulin Yang was affiliated with the University of Washington, where she conducted her research under the guidance of Linda Shapiro. Her academic work was part of the broader computer science and engineering department at the university.

### Field of Study
Shulin Yang's research was primarily in computer science, with a specific focus on computer vision and image classification. Her contributions likely advanced the field's ability to handle complex visual recognition tasks.

## References

1. WorldCat