# Stacey Newman

> master of Computer Science & Engineering, University of Washington, 2016

**Wikidata**: [Q113667881](https://www.wikidata.org/wiki/Q113667881)  
**Source**: https://4ort.xyz/entity/stacey-newman-q113667881

## Summary
Stacey Newman is a computer scientist who completed a master's degree in Computer Science & Engineering from the University of Washington in 2016. Their research focused on prediction and privacy in healthcare analytics, specifically working under advisor Martine De Cock.

## Biography
- Born: [date and place not provided in source material]
- Nationality: [country not provided in source material]
- Education: Master's degree in Computer Science & Engineering, University of Washington, 2016
- Known for: Research in prediction and privacy in healthcare analytics
- Employer(s): [not provided in source material]
- Field(s): Computer science

## Contributions
Stacey Newman completed a master's thesis titled "Prediction and Privacy in Healthcare Analytics" at the University of Washington in 2016. Their research, conducted under the supervision of Martine De Cock, explored computational methods for healthcare analytics while addressing privacy concerns in data analysis. This work contributes to the growing field of privacy-preserving machine learning in healthcare applications, which seeks to develop techniques that allow for accurate data analysis while maintaining patient confidentiality. The thesis represents an important contribution to the intersection of computer science, computer engineering, and healthcare informatics.

## FAQs
### Q: What did Stacey Newman study at the University of Washington?
A: Stacey Newman completed a master's degree in Computer Science & Engineering at the University of Washington in 2016.

### Q: What was the focus of Stacey Newman's research?
A: Stacey Newman's research focused on prediction and privacy in healthcare analytics, as documented in their master's thesis with the same title.

### Q: Who was Stacey Newman's academic advisor?
A: Stacey Newman worked under the supervision of Martine De Cock while completing their master's degree.

## Why They Matter
Stacey Newman's work in prediction and privacy for healthcare analytics represents a significant contribution to the field of privacy-preserving machine learning. Their thesis research addresses critical challenges in healthcare data analysis where the need for accurate predictions must be balanced with strict privacy protections. As healthcare data becomes increasingly valuable for research and clinical applications, Newman's work contributes to developing computational techniques that can extract insights from sensitive health information while maintaining patient confidentiality. This research helps advance the responsible use of artificial intelligence in healthcare, which is essential for building trustworthy medical systems.

## Notable For
- Completed a master's thesis titled "Prediction and Privacy in Healthcare Analytics" at the University of Washington in 2016
- Research focused on addressing privacy concerns in healthcare analytics
- Worked under the supervision of Martine De Cock
- Contributed to the intersection of computer science and healthcare informatics

## Body
### Academic Background
Stacey Newman earned a master's degree in Computer Science & Engineering from the University of Washington in 2016. During their studies, they worked under the academic supervision of Martine De Cock, focusing on computational approaches to healthcare analytics.

### Research Focus
Newman's research centered on prediction and privacy in healthcare analytics. Their thesis explored computational methods that could develop predictive models from healthcare data while ensuring privacy protections for sensitive patient information. This work addressed a critical challenge in the healthcare informatics field, where the value of large datasets must be balanced with ethical and legal requirements for data privacy.

### Academic Thesis
The master's thesis, "Prediction and Privacy in Healthcare Analytics," represents Newman's primary scholarly contribution. The work was completed as part of their requirements for a master's degree in Computer Science & Engineering at the University of Washington in 2016. The thesis contributed to the growing body of research at the intersection of computer science and healthcare data analysis.

## References

1. WorldCat