# Sara Hooker

> deep learning researcher

**Wikidata**: [Q107742361](https://www.wikidata.org/wiki/Q107742361)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Sara_Hooker)  
**Source**: https://4ort.xyz/entity/sara-hooker

## Summary
Sara Hooker is a computer scientist and deep learning researcher known for work on interpretability methods and the interaction between machine learning and hardware. She has been a researcher at Google Brain and is affiliated with Cohere, and is the author of the paper "A Benchmark for Interpretability Methods in Deep Neural Networks."

## Biography
- Education: Bachelor of Arts, international relations, Carleton College (completed 2013); studied at Waterford Kamhlaba; Doctor of Philosophy, Université de Montréal.
- Known for: Research on interpretability in deep neural networks and analysis of hardware effects on machine learning ("The Hardware Lottery").
- Employer(s): Google Brain; Cohere.
- Field(s): Computer science; deep learning; computer hardware.
- Occupation: Computer scientist.
- Student of: Alfred Montero.
- Website: https://www.sarahooker.me/
- GitHub: sarahooker
- Google Scholar author ID: J9YCc2sAAAAJ
- Google Knowledge Graph ID: /g/11f0xr1mqh

## Contributions
Sara Hooker has authored and coauthored research addressing interpretability in deep learning and the relationship between algorithms and hardware. She is the author of "A Benchmark for Interpretability Methods in Deep Neural Networks" (arXiv:1806.10758), a public benchmark designed to evaluate interpretability methods for neural networks. She also authored the article commonly referenced as "The Hardware Lottery" in Communications of the ACM, which examines how hardware choices shape which algorithms succeed in practice. Hooker has worked as a researcher at Google Brain and is affiliated with Cohere, contributing to industry research environments where deep learning methods are developed and evaluated. Her academic background includes a Bachelor of Arts in international relations from Carleton College (2013) and doctoral study at the Université de Montréal. She is listed as a student of Alfred Montero. Hooker maintains an online presence through her personal website and a GitHub account (sarahooker), and her publications and citations are indexed under Google Scholar author ID J9YCc2sAAAAJ.

## FAQs
### Q: What does Sara Hooker research?
A: She researches deep learning, with a focus on interpretability methods for neural networks and the interactions between machine learning research and computer hardware.

### Q: Where does Sara Hooker work?
A: She has been a researcher at Google Brain and is affiliated with Cohere.

### Q: What are Sara Hooker's notable publications?
A: Notable works include "A Benchmark for Interpretability Methods in Deep Neural Networks" (arXiv:1806.10758) and the article "The Hardware Lottery" published in Communications of the ACM.

## Why They Matter
Sara Hooker's work matters because it targets two persistent challenges in machine learning: understanding model behavior and recognizing how hardware influences research trajectories. By producing a benchmark for interpretability methods, she provided a concrete tool to compare and evaluate techniques that aim to explain neural networks. This helps researchers and practitioners move beyond anecdotal claims to reproducible evaluations. Her analysis in "The Hardware Lottery" highlights the systemic role of hardware in determining which ideas are feasible and which fail to gain traction, prompting discussion about research priorities and the infrastructure that shapes them. Working within major AI research settings such as Google Brain and with industry affiliation at Cohere, her publications and public writing contribute to conversations across academic and industrial communities. Without these contributions, the field would have fewer organized evaluation resources for interpretability and less critical attention to how hardware constraints shape machine learning progress.

## Notable For
- Author of "A Benchmark for Interpretability Methods in Deep Neural Networks" (arXiv:1806.10758).
- Author of the article "The Hardware Lottery" in Communications of the ACM.
- Researcher at Google Brain.
- Affiliated with Cohere.
- Education: BA in international relations from Carleton College (2013); doctoral study at Université de Montréal.

## Body

### Education
- Carleton College — Bachelor of Arts in international relations. Completion year listed as 2013.
- Waterford Kamhlaba — attended (details provided in structured data).
- Université de Montréal — Doctor of Philosophy (degree listed in structured data).

### Academic Mentorship
- Listed student of Alfred Montero (reference provided in structured data).

### Employment and Affiliations
- Google Brain — listed employer with a research profile at research.google/people/SaraHooker/.
- Cohere — listed as an employer/affiliation in structured data.

### Selected Publications and Writings
- "A Benchmark for Interpretability Methods in Deep Neural Networks" — arXiv:1806.10758. Presented as a public benchmark for evaluating interpretability techniques for neural networks.
- "The Hardware Lottery" — published in Communications of the ACM (linked in structured data). Examines how hardware choices influence which algorithms succeed.

### Online Presence and Identifiers
- Personal website: https://www.sarahooker.me/
- GitHub username: sarahooker
- Google Scholar author ID: J9YCc2sAAAAJ
- Google Knowledge Graph ID: /g/11f0xr1mqh

### Other Structured Data
- Given name: Sara.
- Family name: Hooker.
- Sex/gender: female.
- Occupation: computer scientist.
- Instance of: human.
- Wikipedia title and language: "Sara Hooker" (English).

## References

1. [Source](https://www.carleton.edu/news/stories/carleton-college-student-earns-davis-projects-for-peace-grant/)
2. [Source](https://www.davisuwcscholars.org/scholars/2013/h/node/3058)
3. [Source](https://research.google/people/SaraHooker/)
4. GitHub
5. [Source](https://arxiv.org/abs/1806.10758)
6. [Source](https://cacm.acm.org/research/the-hardware-lottery/)
7. [Source](https://people.carleton.edu/~amontero/cpstudentresearch.htm)