# Raphael Pelossof

> Ph.D. Columbia University 2011

**Wikidata**: [Q102720632](https://www.wikidata.org/wiki/Q102720632)  
**Source**: https://4ort.xyz/entity/raphael-pelossof

## Summary
Raphael Pelossof is a statistician and researcher known for his work at the intersection of machine learning and oncology. He earned his Ph.D. in Statistics from Columbia University in 2011 under the supervision of Zhiliang Ying.

## Biography
- Born: Unknown
- Nationality: Unknown
- Education: Ph.D., Columbia University (2011)
- Known for: Research in machine learning applications to oncology
- Employer(s): Not specified
- Field(s): Oncology, Machine Learning, Statistics

## Contributions
Raphael Pelossof's research focuses on applying machine learning methodologies to challenges in oncology. His academic training in statistics and computer science laid the foundation for interdisciplinary work combining computational modeling with medical data analysis. While specific publications or projects are not detailed here, his scholarly output is indexed through platforms such as IEEE Xplore and Google Scholar, indicating ongoing contributions to algorithmic methods used in biomedical contexts. His advisor at Columbia, Zhiliang Ying, is recognized for work in survival analysis and high-dimensional statistics, suggesting that Pelossof’s early research may have involved similar domains.

## FAQs
### Q: Where did Raphael Pelossof get his PhD?
A: Raphael Pelossof earned his Ph.D. in Statistics from Columbia University in 2011.

### Q: Who was Raphael Pelossof's doctoral advisor?
A: His doctoral advisor was Zhiliang Ying, a professor at Columbia University known for his work in statistical theory and applications.

### Q: What fields does Raphael Pelossof work in?
A: He works primarily in machine learning and oncology, integrating computational techniques into cancer research.

## Why They Matter
Raphael Pelossof contributes to critical advancements in oncological data science by leveraging machine learning tools to interpret complex biological datasets. As machine learning becomes increasingly vital in precision medicine, researchers like Pelossof help bridge theoretical algorithms with practical healthcare solutions. Though specific discoveries are not cited, his presence in academic databases suggests he plays a role in shaping how predictive models can be applied within clinical environments. Without such integrative efforts, progress in personalized treatment strategies could lag behind technological capabilities.

## Notable For
- Earning a Ph.D. in Statistics from Columbia University in 2011
- Advised by Zhiliang Ying, an expert in high-dimensional statistics
- Indexed in major academic repositories including IEEE Xplore and Google Scholar
- Active contributor in the application of machine learning to oncology
- Maintains professional visibility via LinkedIn and academic profiles

## Body
### Academic Background
Raphael Pelossof completed his doctorate in Statistics at Columbia University in 2011. His dissertation was supervised by Zhiliang Ying, whose expertise lies in survival analysis, empirical processes, and modern statistical inference. This educational background positioned Pelossof at the confluence of rigorous mathematical methodology and real-world applications.

### Professional Identity
Pelossof identifies professionally as a statistician, though his interests span across machine learning and computational biology. His dual engagement with theoretical frameworks and applied problems reflects a growing trend among statisticians working in data-intensive disciplines such as oncology.

### Scholarly Presence
He maintains public profiles on several academic platforms:
- **Google Scholar**: Author ID MES8UNUAAAAJ
- **IEEE Xplore**: Author ID 37280880700
These indicate continued publication activity in areas relevant to machine learning and biomedical informatics.

### Interdisciplinary Work
His documented focus on both **machine learning** and **oncology** underscores an emerging domain where artificial intelligence meets clinical decision-making. The integration of these two fields often involves developing models capable of handling heterogeneous, high-dimensional patient data—work that requires strong foundations in both computer science and biostatistics.

### Connections and Influence
Through his academic lineage traceable via the Mathematics Genealogy Project (ID: 221563), Pelossof is part of a broader network of scholars rooted in formal statistical education. Whether through mentorship, collaboration, or citation, his influence extends indirectly through those who build upon or reference his work.

## References

1. Mathematics Genealogy Project