# Jingjing Wang

> PhD, University of Washington, Computer Science & Engineering, 2018

**Wikidata**: [Q113667767](https://www.wikidata.org/wiki/Q113667767)  
**Source**: https://4ort.xyz/entity/jingjing-wang-q113667767

## Summary
Jingjing Wang is a computer scientist who earned her PhD in Computer Science & Engineering from the University of Washington in 2018. Her research focused on runtime optimizations for large-scale data analytics, and she was advised by Magdalena Bałazińska. She is known for her contributions to the field of computer science, particularly in optimizing data processing systems.

## Biography
- Born: 1989
- Nationality: United States
- Education: PhD in Computer Science & Engineering, University of Washington (2018)
- Known for: Research on runtime optimizations for large-scale data analytics
- Employer(s): University of Washington (as a doctoral student)
- Field(s): Computer science, data analytics

## Contributions
Jingjing Wang's doctoral research, titled *Runtime Optimizations for Large-scale Data Analytics*, focused on improving the efficiency of data processing systems. Her work was supervised by Magdalena Bałazińska and contributed to advancements in optimizing runtime performance for large-scale data analytics. While specific publications or products are not detailed in the provided source material, her thesis represents a key contribution to the field of computer science, particularly in the optimization of data processing frameworks.

## FAQs
### Q: What is Jingjing Wang known for?
A: Jingjing Wang is known for her PhD research in runtime optimizations for large-scale data analytics, completed at the University of Washington in 2018.

### Q: Who was Jingjing Wang's doctoral advisor?
A: Jingjing Wang's doctoral advisor was Magdalena Bałazińska.

### Q: What was the title of Jingjing Wang's doctoral thesis?
A: The title of Jingjing Wang's doctoral thesis was *Runtime Optimizations for Large-scale Data Analytics*.

### Q: Where did Jingjing Wang earn her PhD?
A: Jingjing Wang earned her PhD in Computer Science & Engineering from the University of Washington in 2018.

### Q: What field does Jingjing Wang work in?
A: Jingjing Wang works in the field of computer science, specifically focusing on data analytics and runtime optimizations.

## Why They Matter
Jingjing Wang's research on runtime optimizations for large-scale data analytics has contributed to the development of more efficient data processing systems. Her work, supervised by Magdalena Bałazińska, has likely influenced the design of modern data analytics frameworks, improving their performance and scalability. While her specific impact may not be widely documented, her thesis represents a foundational contribution to the field, potentially paving the way for future advancements in data processing technologies.

## Notable For
- Earned a PhD in Computer Science & Engineering from the University of Washington in 2018.
- Conducted research on runtime optimizations for large-scale data analytics.
- Advised by Magdalena Bałazińska, a notable computer scientist.
- Published a doctoral thesis titled *Runtime Optimizations for Large-scale Data Analytics*.

## Body
### Education
Jingjing Wang completed her PhD in Computer Science & Engineering at the University of Washington in 2018. Her doctoral research focused on runtime optimizations for large-scale data analytics, supervised by Magdalena Bałazińska.

### Research Focus
Jingjing Wang's doctoral thesis, *Runtime Optimizations for Large-scale Data Analytics*, explored methods to improve the efficiency of data processing systems. Her work contributed to the field of computer science by addressing challenges in optimizing runtime performance for large-scale data analytics.

### Advisor
Magdalena Bałazińska served as Jingjing Wang's doctoral advisor. Bałazińska is a computer scientist known for her contributions to the field, as evidenced by her inclusion in academic genealogy records.

### Field of Study
Jingjing Wang's research was primarily in computer science, with a specific focus on data analytics and runtime optimizations. Her work aligns with broader efforts to enhance the performance of data processing systems.

## References

1. WorldCat