# Christine Allen

> master of Computer Science & Engineering, University of Washington, 2020

**Wikidata**: [Q113667853](https://www.wikidata.org/wiki/Q113667853)  
**Source**: https://4ort.xyz/entity/christine-allen-q113667853

## Summary
Christine Allen is a computer scientist who earned a Master of Science in Computer Science & Engineering from the University of Washington in 2020. She is known for her academic research focused on interpretable data phenotyping for healthcare utilizing unsupervised learning techniques.

## Biography
*   **Born:** [Information not provided in source]
*   **Nationality:** [Information not provided in source]
*   **Education:** Master of Science & Engineering, University of Washington (2020)
*   **Known for:** Research in interpretable data phenotyping for healthcare
*   **Employer(s):** [Information not provided in source]
*   **Field(s):** Computer Science, Computer Engineering

## Contributions
Christine Allen's primary documented contribution is in the field of academic research. In 2020, she authored a master's thesis titled **"Interpretable Data Phenotyping for Healthcare Via Unsupervised Learning."** This work contributes to the intersection of data science and healthcare by exploring how unsupervised learning algorithms can be used to categorize patient data in a way that is interpretable by humans.

## FAQs
**What degree did Christine Allen earn?**
Christine Allen earned a master's degree in Computer Science & Engineering from the University of Washington in 2020.

**What was the topic of Christine Allen's thesis?**
Her thesis was titled "Interpretable Data Phenotyping for Healthcare Via Unsupervised Learning," focusing on applying computer science principles to healthcare data analysis.

**Who supervised Christine Allen's research?**
She conducted her research as a student of Ankur Teredesai at the University of Washington.

## Why They Matter
Christine Allen represents the contribution of computer scientists to the healthcare sector through academic research. Her work on "interpretable" data phenotyping addresses a critical need in artificial intelligence: ensuring that complex machine learning models (specifically unsupervised learning) produce results that clinicians and researchers can understand and trust. By bridging the gap between raw computational power and practical healthcare application, her work exemplifies the role of a computer scientist in solving complex problems within the service sector.

## Notable For
*   **Academic Achievement:** Successfully obtaining a master's degree in Computer Science & Engineering from a major research university in 2020.
*   **Specific Research Focus:** Specializing in "interpretable" phenotyping, a significant sub-field of medical informatics.
*   **Collaboration:** Working under the supervision of Ankur Teredesai, a noted figure in the field.
*   **Wikidata Recognition:** Being included in the WikiProject PCC Wikidata Pilot for the University of Washington, indicating her data is structured and recognized in academic knowledge bases.

## Body
### Education and Academic Career
Christine Allen is classified as a computer scientist and is an alumna of the **University of Washington**. She completed her higher education in 2020, graduating with a **master's degree**. Her specific field of study encompassed both **computer science and computer engineering**. During her academic tenure, she was listed on the **WikiProject PCC Wikidata Pilot** for the University of Washington, signifying her inclusion in structured academic data initiatives.

### Research and Thesis Work
Allen's academic culmination was the production of a master's thesis titled **"Interpretable Data Phenotyping for Healthcare Via Unsupervised Learning."** This research was conducted under the mentorship and supervision of **Ankur Teredesai**, whom she is listed as a student of.

Her work falls under the broader occupational definition of a **computer scientist**—a professional who focuses on the theoretical foundations of information and computation. Specifically, her research contributes to the "service sector" aspect of the profession, applying computational systems to healthcare. The focus on "unsupervised learning" highlights an engagement with advanced algorithmic design, while the emphasis on "interpretable" phenotyping suggests a focus on the usability and transparency of data models in medical settings.

## References

1. WorldCat