# Dan Feldman

> Ph.D. Tel Aviv University 2010

**Wikidata**: [Q102403446](https://www.wikidata.org/wiki/Q102403446)  
**Source**: https://4ort.xyz/entity/dan-feldman

## Summary  
Dan Feldman is an Israeli computer scientist known for his work in computational geometry and algorithms. He earned his Ph.D. from Tel Aviv University in 2010 under the supervision of Micha Sharir and Amos Fiat, and currently serves as a faculty member at the University of Haifa.

## Biography  
- **Born**: Unknown date and place  
- **Nationality**: Israel  
- **Education**: Ph.D., Tel Aviv University (2010)  
- **Known for**: Research in approximation algorithms, clustering, and geometric optimization  
- **Employer(s)**: University of Haifa  
- **Field(s)**: Computer Science  

## Contributions  
Dan Feldman's research focuses on developing efficient algorithms for problems in computational geometry, machine learning, and data analysis. His work includes significant contributions to coresets—compact representations of large datasets used to speed up computations without sacrificing accuracy. One of his notable works is the development of streaming algorithms for shape fitting and clustering, which were foundational in advancing scalable methods for big data analytics. He has authored numerous peer-reviewed papers published in top-tier conferences and journals such as ACM Symposium on Computational Geometry (SoCG), IEEE FOCS, and journals like Discrete & Computational Geometry. Feldman also contributes to open-source algorithmic libraries and collaborates with interdisciplinary teams applying algorithmic solutions in robotics and computer vision.

## FAQs  
### Q: Where did Dan Feldman get his PhD?  
A: Dan Feldman received his Ph.D. in Computer Science from Tel Aviv University in 2010.

### Q: Who advised Dan Feldman during his PhD?  
A: His doctoral advisors were prominent Israeli computer scientists Micha Sharir and Amos Fiat.

### Q: What is Dan Feldman known for in computer science?  
A: He is recognized for his work on approximation algorithms, coresets, and geometric optimization techniques used in data analysis and robotics.

## Why They Matter  
Dan Feldman’s innovations in algorithm design have had a lasting impact on how complex geometric and analytical tasks are handled computationally. By pioneering new approaches to coreset construction and approximation algorithms, he enabled more efficient processing of massive datasets—an essential need in modern computing environments. His theoretical advancements have been adopted across fields including artificial intelligence, robotics, and geographic information systems. As both a researcher and educator, Feldman continues to influence future generations through his academic role at the University of Haifa and extensive publication record that bridges theory and application.

## Notable For  
- Developing novel coreset frameworks for clustering and shape fitting problems  
- Publishing influential works in leading computational geometry venues including SoCG and SODA  
- Serving as a key figure in bridging theoretical computer science with practical applications in robotics and AI  
- Holding affiliations with prestigious institutions such as Tel Aviv University and the University of Haifa  
- Advising students and contributing to international collaborations in algorithmic research  

## Body  
### Academic Background  
Dan Feldman completed his Ph.D. in Computer Science at Tel Aviv University in 2010. His dissertation advisors were renowned Israeli researchers Micha Sharir and Amos Fiat, both leaders in discrete and computational geometry.

### Professional Affiliation  
He currently holds a position at the Department of Computer Science at the University of Haifa, where he teaches and conducts advanced research in algorithms and computational geometry.

### Core Research Areas  
Feldman's scholarly output spans several areas within theoretical and applied computer science:
- Approximation algorithms
- Coresets and sketching
- Clustering and facility location
- Geometric optimization
- Applications in robotics and sensor networks

### Selected Publications  
Some of his most cited works include:
- “Turning Big data into tiny data: Constantsize coresets for k-means, PCA and projective clustering” – introducing compact summaries applicable to unsupervised learning models.
- “Streaming geometric optimization using graphics hardware” – exploring GPU-based acceleration for real-time geometric computation.

### Collaborative Impact  
His collaborative efforts span academia and industry, particularly focusing on translating high-dimensional geometric insights into implementable tools for real-world challenges in autonomous systems and spatial data analysis.

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## References

1. Mathematics Genealogy Project
2. National Library of Israel