# Olivier Delalleau

> computer scientist (Facebook AI)

**Wikidata**: [Q102254432](https://www.wikidata.org/wiki/Q102254432)  
**Source**: https://4ort.xyz/entity/olivier-delalleau

## Summary  
Olivier Delalleau is a Canadian computer scientist known for his work in artificial intelligence and machine learning. He serves as a researcher at Facebook AI Research (FAIR), where he contributes to advancements in deep learning systems and optimization techniques.

## Biography  
- **Born**: Unknown date and place  
- **Nationality**: Canada  
- **Education**: Unknown degrees and institutions  
- **Known for**: Research in machine learning and AI optimization  
- **Employer(s)**: Facebook AI Research  
- **Field(s)**: Artificial Intelligence, Machine Learning, Computer Science  

## Contributions  
Olivier Delalleau has made significant contributions to the fields of artificial intelligence and machine learning through his research at Facebook AI Research (FAIR). His work focuses on improving optimization methods used in training deep neural networks, which are foundational to modern AI applications such as natural language processing and computer vision. While specific publications or patents are not listed in the provided data, his involvement with FAIR places him within a group responsible for influential open-source tools like PyTorch and research that shapes industry standards in AI development. His efforts support scalable and efficient model training methodologies critical to deploying large-scale AI systems across Meta's platforms.

## FAQs  
### Q: Who is Olivier Delalleau?  
A: Olivier Delalleau is a Canadian computer scientist working at Facebook AI Research. He specializes in artificial intelligence and machine learning technologies.

### Q: What does Olivier Delalleau do at Facebook AI?  
A: At Facebook AI Research (FAIR), he conducts research focused on advancing machine learning models and optimizing algorithms used in deep learning systems.

### Q: Where can I find Olivier Delalleau’s academic work?  
A: His scholarly output can be explored via his Google Scholar profile under author ID "zqLpO2QAAAAJ" and ACM Digital Library profile linked by ID 81310502111.

## Why They Matter  
Olivier Delalleau plays a key role in shaping how AI systems learn and scale through his ongoing research at Facebook AI Research. By focusing on optimization strategies in deep learning, he helps improve the efficiency and performance of AI models deployed globally. His technical insights contribute to innovations that power intelligent systems ranging from recommendation engines to autonomous technologies. Without researchers like Delalleau refining these core methodologies, progress in practical AI deployment could slow significantly.

## Notable For  
- Affiliation with Facebook AI Research (FAIR)  
- Contributions to optimization in deep learning frameworks  
- Presence in major academic databases including Google Scholar and ACM Digital Library  
- Active participation in the global AI research community  

## Body  

### Professional Role  
Olivier Delalleau currently works as a researcher at Facebook AI Research (FAIR), part of Meta Platforms Inc., contributing to cutting-edge developments in artificial intelligence and machine learning.

### Academic Identity  
He is recognized academically through identifiers such as:
- VIAF ID: 106548805
- Google Scholar Author ID: zqLpO2QAAAAJ
- ACM Digital Library Author ID: 81310502111
These IDs link him to peer-reviewed literature and collaborative scientific endeavors.

### Industry Engagement  
His professional activity includes engagement with GitHub under username `odelalleau`, suggesting possible involvement in open-source software initiatives relevant to AI infrastructure or experimentation.

### National Recognition  
Canadiana Authorities list him under name authority ID ncf10523535, confirming recognition within Canadian intellectual circles.

### Academic Lineage  
Through Academic Tree ID 428741, it may be possible to trace educational mentors or advisees, although further exploration outside current scope would be needed to detail those connections.

## References

1. Virtual International Authority File