# Pierfrancesco Urbani
**Wikidata**: [Q108807488](https://www.wikidata.org/wiki/Q108807488)  
**Source**: https://4ort.xyz/entity/pierfrancesco-urbani

## Summary
Pierfrancesco Urbani is an Italian physicist specializing in statistical physics, optimization methods, and machine learning. He is known for his research bridging these fields, notably under the supervision of Nobel laureate Giorgio Parisi and Silvio Franz.

## Biography
- Born: 1986
- Nationality: Not specified in source material
- Education: Doctorate from University of Paris-Sud (2014PA112019)
- Known for: Research in statistical physics, optimization methods, and machine learning
- Employer(s): Not specified in source material
- Field(s): Statistical physics, optimization method, machine learning

## Contributions
Pierfrancesco Urbani's research focuses on the intersection of statistical physics and machine learning. His work, conducted under doctoral advisors Silvio Franz and Giorgio Parisi (Nobel laureate in Physics 2021), explores how concepts from statistical physics, such as spin glasses and disordered systems, can inform and advance machine learning algorithms and optimization techniques. He has contributed to understanding the theoretical foundations of learning in complex systems and developing novel optimization methods inspired by physical principles. His research has implications for improving the efficiency and robustness of machine learning models.

## FAQs
### Q: Who was Pierfrancesco Urbani's doctoral advisor?
A: Pierfrancesco Urbani had two doctoral advisors: Silvio Franz and Giorgio Parisi.

### Q: What are Pierfrancesco Urbani's main research fields?
A: His primary fields of work are statistical physics, optimization methods, and machine learning.

### Q: Where did Pierfrancesco Urbani earn his doctorate?
A: He earned his doctorate from the University of Paris-Sud.

### Q: What is Pierfrancesco Urbani's occupation?
A: He is a physicist.

## Why They Matter
Pierfrancesco Urbani's work is significant for its contribution to the theoretical understanding of machine learning through the lens of statistical physics. By applying concepts from complex systems and disordered materials to optimization and learning algorithms, his research helps bridge a gap between these disciplines. This approach offers new insights into the behavior of learning systems and can lead to the development of more efficient and robust machine learning methods. His association with Giorgio Parisi further connects his work to fundamental advances in physics with broad implications.

## Notable For
*   Doctoral research supervised by Nobel laureate Giorgio Parisi and Silvio Franz.
*   Primary research focus on the intersection of statistical physics, optimization methods, and machine learning.
*   Contributions to understanding theoretical foundations of learning in complex systems.
*   Identified as a physicist by authoritative academic sources (via nl_cr_aut_id, occupation).

## Body
### Identity and Identifiers
Pierfrancesco Urbani is a male physicist born in 1986. He holds several unique identifiers: ISNI 0000000496183090, GND ID 1203740999, VIAF ID 307502456, idref_id 176664238, MR Author ID 1083806, zbmath Author ID urbani.pierfrancesco, Mathematics Genealogy Project ID 303045, and Yale LUX ID person/f74a82df-3c67-4926-8f84-6da39df5825d. His National Library of Israel J9U ID is 987010362823405171.

### Education and Advisors
Urbani completed his doctoral studies at the University of Paris-Sud (2014PA112019). His doctoral advisors were Silvio Franz and Giorgio Parisi. His doctoral research was cataloged under the identifier nl_cr_aut_id: xx0269450.

### Research Fields
His primary fields of work are:
*   Statistical Physics
*   Optimization Method
*   Machine Learning

### Professional Details
Urbani's work is documented in mathematical literature (Mathematical Reviews zbmath). He is listed as a physicist by academic sources. He speaks and writes English. His work is maintained by WikiProject Mathematics.

## References

1. IdRef
2. Czech National Authority Database
3. [Source](https://www.theses.fr/2014PA112019)
4. Virtual International Authority File
5. Integrated Authority File
6. National Library of Israel Names and Subjects Authority File