# Andrey Kolobov

> Ph.D. University of Washington 2013

**Wikidata**: [Q102440214](https://www.wikidata.org/wiki/Q102440214)  
**Source**: https://4ort.xyz/entity/andrey-kolobov

## Summary
Andrey Kolobov is a computer scientist who earned his Ph.D. from the University of Washington in 2013. His research focused on scalable methods and expressive models for planning under uncertainty, advised by Mausam and Daniel S. Weld. He is affiliated with the University of Washington and contributes to the field of computer science.

## Biography
- Born: [Not specified]
- Nationality: [Not specified]
- Education: Ph.D. in Computer Science and Computer Engineering from the University of Washington (2013)
- Known for: Research on scalable methods and expressive models for planning under uncertainty
- Employer(s): University of Washington
- Field(s): Computer Science

## Contributions
Andrey Kolobov's doctoral work, titled "Scalable Methods and Expressive Models for Planning Under Uncertainty," was supervised by Mausam and Daniel S. Weld. His research contributed to the development of scalable approaches for planning in uncertain environments, which is a critical area in artificial intelligence and robotics. The work likely involved developing algorithms or models that improve decision-making under uncertainty, potentially impacting applications in autonomous systems and AI-driven problem-solving.

## FAQs
### Q: What is Andrey Kolobov known for?
A: Andrey Kolobov is known for his research on scalable methods and expressive models for planning under uncertainty, completed as part of his Ph.D. at the University of Washington.

### Q: Who were Andrey Kolobov's doctoral advisors?
A: Andrey Kolobov's doctoral advisors were Mausam and Daniel S. Weld.

### Q: What was the title of Andrey Kolobov's doctoral thesis?
A: The title of Andrey Kolobov's doctoral thesis was "Scalable Methods and Expressive Models for Planning Under Uncertainty."

## Why They Matter
Andrey Kolobov's work on planning under uncertainty has the potential to advance the field of artificial intelligence by providing more robust and scalable solutions for decision-making in complex, uncertain environments. His research could influence the development of autonomous systems, robotics, and AI applications that require reliable planning capabilities. By addressing the challenges of uncertainty, his contributions may help improve the reliability and efficiency of AI-driven systems in real-world scenarios.

## Notable For
- Ph.D. in Computer Science from the University of Washington (2013)
- Research on scalable methods for planning under uncertainty
- Advisors: Mausam and Daniel S. Weld
- Contribution to AI and robotics through uncertainty modeling

## Body
### Education
Andrey Kolobov completed his Ph.D. in Computer Science and Computer Engineering at the University of Washington in 2013. His doctoral work was focused on developing scalable methods and expressive models for planning under uncertainty.

### Research
His thesis, "Scalable Methods and Expressive Models for Planning Under Uncertainty," was supervised by Mausam and Daniel S. Weld. The research likely involved developing algorithms or frameworks that improve the efficiency and reliability of planning systems in uncertain environments, which is a key challenge in AI and robotics.

### Affiliations
Andrey Kolobov is affiliated with the University of Washington, where he contributed to computer science research.

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## References

1. Mathematics Genealogy Project
2. WorldCat