# Sameer Agarwal

> Ph.D. University of California, San Diego 2006

**Wikidata**: [Q102303055](https://www.wikidata.org/wiki/Q102303055)  
**Source**: https://4ort.xyz/entity/sameer-agarwal

## Summary
Sameer Agarwal is a computer scientist who earned his Ph.D. from the University of California, San Diego (UCSD) in 2006. He is recognized for his academic contributions to computer science, particularly under the guidance of his doctoral advisor, Serge Belongie, a notable researcher in computer vision and machine learning.

## Biography
- **Born**: [Date and place not specified]  
- **Nationality**: [Not specified]  
- **Education**: Ph.D., University of California, San Diego (2006)  
- **Known for**: Academic research in computer science, guided by Serge Belongie.  
- **Employer(s)**: [Not specified]  
- **Field(s)**: Computer science, computer vision, machine learning.  

## Contributions  
Sameer Agarwal’s work is rooted in his doctoral research at UCSD, where he specialized in computer science under the supervision of Serge Belongie, a prominent figure in computer vision and machine learning. While specific publications or projects are not detailed in the provided source material, his academic foundation and association with Belongie position him within a lineage of researchers advancing computational techniques. His contributions likely align with the broader goals of his field, such as improving visual recognition systems or developing machine learning frameworks, though explicit outcomes (e.g., papers, patents) are not enumerated in the available data.  

## FAQs  
### Q: Where did Sameer Agarwal complete his Ph.D.?  
A: He earned his Ph.D. from the University of California, San Diego in 2006.  

### Q: Who was his doctoral advisor?  
A: His doctoral advisor was Serge Belongie, a renowned computer scientist in computer vision and machine learning.  

### Q: What field is Sameer Agarwal associated with?  
A: He works in computer science, with a focus aligned with his advisor’s expertise in computer vision and machine learning.  

## Why They Matter  
Sameer Agarwal’s significance stems from his academic credentials and training under Serge Belongie, a key contributor to computer vision and machine learning. By engaging with these fields at a high level, Agarwal contributes to the advancement of technologies that underpin modern AI systems, such as image recognition and data analysis tools. His work, while not detailed in the source material, reflects the broader impact of researchers trained in rigorous computer science programs, driving innovation in both academic and industrial contexts.  

## Notable For  
- Earned a Ph.D. in computer science from UCSD (2006).  
- Trained under Serge Belongie, a leading researcher in computer vision and machine learning.  
- Affiliated with institutions advancing computational research.  

## Body  
### Academic Background  
Sameer Agarwal completed his Ph.D. in computer science at the University of California, San Diego in 2006. His doctoral advisor, **Serge Belongie**, is a distinguished researcher known for contributions to computer vision and machine learning, particularly in object recognition and image analysis.  

### Research Focus  
While specific projects or publications are not detailed in the source material, Agarwal’s association with Belongie suggests engagement with foundational challenges in computer science, such as:  
- Developing algorithms for visual data interpretation.  
- Improving machine learning models for pattern recognition.  

### Institutional Affiliations  
Agarwal’s education and training connect him to UCSD’s computer science program, recognized for its research excellence. His advisor’s dual affiliation with Cornell University and the University of Copenhagen further contextualizes his academic lineage within global research networks.  

### Legacy  
As a computer scientist educated under a leading figure in the field, Agarwal’s work contributes to the evolution of technologies critical to artificial intelligence and data science. His role underscores the importance of academic rigor in driving technological progress, even as specific contributions remain undocumented in the provided sources.

## References

1. Mathematics Genealogy Project