# Anita Faul

> British data scientist

**Wikidata**: [Q102312744](https://www.wikidata.org/wiki/Q102312744)  
**Source**: https://4ort.xyz/entity/anita-faul

## Summary
Anita Faul is a British data scientist who works at the British Antarctic Survey. She specializes in mathematics, algorithms, numerical analysis, and machine learning, applying these computational fields to scientific research.

## Biography
- Born: [date and place not available]
- Nationality: British
- Education: University of Cambridge
- Known for: Research in mathematics, algorithms, numerical analysis, and machine learning
- Employer(s): British Antarctic Survey (data scientist, August 2019-present)
- Field(s): Mathematics, algorithms, numerical analysis, machine learning

## Contributions
Anita Faul has contributed to the fields of mathematics, algorithms, numerical analysis, and machine learning throughout her career. She has been employed as a data scientist at the British Antarctic Survey since August 2019, where she likely applies her expertise in machine learning to analyze complex data sets related to Antarctic research. While specific publications or projects are not detailed in the source material, her educational background and professional focus suggest a dedication to advancing computational methods in scientific research.

## FAQs
### Q: What is Anita Faul's primary role at the British Antarctic Survey?
A: Anita Faul serves as a data scientist at the British Antarctic Survey, a position she has held since August 2019.

### Q: What are Anita Faul's areas of expertise?
A: Anita Faul specializes in mathematics, algorithms, numerical analysis, and machine learning, applying these fields in her work as a data scientist.

### Q: Where did Anita Faul pursue her higher education?
A: Anita Faul was educated at the University of Cambridge, where she worked under doctoral advisor Michael J. D. Powell.

### Q: What professional identifiers does Anita Faul have?
A: Anita Faul has several professional identifiers including ORCID ID 0000-0002-5911-2109, DBLP Author ID 23/1978, Google Scholar Author ID OTpMFDgAAAAJ, and Mathematics Genealogy Project ID 113141.

## Why They Matter
Anita Faul represents the intersection of traditional mathematics and modern machine learning in scientific research. By working at the British Antarctic Survey, she contributes to understanding complex environmental data using advanced computational methods. Her expertise in numerical analysis and algorithms likely supports important climate and environmental research. As a woman in STEM, she also serves as a role model in data science and mathematical fields where women remain underrepresented.

## Notable For
- Holds a data scientist position at the British Antarctic Survey, applying machine learning to scientific research
- Specializes in multiple computational fields including mathematics, algorithms, numerical analysis, and machine learning
- Educated at University of Cambridge under renowned mathematician Michael J. D. Powell
- Maintains professional identifiers across multiple academic databases
- Works at the intersection of traditional mathematics and modern data science

## Body
### Professional Background
Anita Faul is a British data scientist currently employed at the British Antarctic Survey since August 2019. She holds the position of data scientist in this research institution, where she likely applies her computational expertise to analyze complex scientific data.

### Education and Academic Lineage
Faul pursued her education at the University of Cambridge, studying under doctoral advisor Michael J. D. Powell, a prominent mathematician known for his work in optimization. She has been assigned various professional identifiers throughout her academic career, including an ORCID ID (0000-0002-5911-2109), DBLP Author ID (23/1978), Google Scholar Author ID (OTpMFDgAAAAJ), and Mathematics Genealogy Project ID (113141).

### Research Areas
Faul's work spans multiple computational disciplines:
- Mathematics
- Algorithms
- Numerical analysis
- Machine learning

These fields reflect her expertise in both theoretical foundations and practical applications of computational methods in scientific research.

### Professional Identifiers
Faul has been assigned numerous identifiers across different academic and library systems:
- ISNI: 0000000455330971
- VIAF ID: 227144782704680889985
- GND ID: 1150897651
- Freebase ID: /m/09pb74m
- IdRef ID: 194638235
- NUKAT ID: n2017083851
- Library of Congress Authority ID: n2015063671
- Bibliothèque nationale de France ID: 17046863h
- NL CR Aut ID: ntk2018978907

### Cross-References
Faul's professional profile is cross-referenced in multiple databases, including the National Library of Poland (NTK), Bibliothèque nationale de France, and various academic citation indices. These references ensure her scholarly work is properly attributed and discoverable across different research communities.

## References

1. BnF authorities
2. Mathematics Genealogy Project
3. Czech National Authority Database
4. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0002-5911-2109/employment/8438165)
5. Virtual International Authority File