# Matthias Scheller Lichtenauer

> Dr. rer. nat. Friedrich-Schiller-Universität Jena 2013

**Wikidata**: [Q102752963](https://www.wikidata.org/wiki/Q102752963)  
**Source**: https://4ort.xyz/entity/matthias-scheller-lichtenauer

## Summary  
Matthias Scheller Lichtenauer is a German computer scientist known for his academic contributions in the field of computational geometry and topology. He earned his doctorate from Friedrich Schiller University Jena in 2013 under the supervision of Joachim Giesen.

## Biography  
- **Born**: Unknown date and place  
- **Nationality**: Germany  
- **Education**:  
  - Dr. rer. nat., Friedrich Schiller University Jena (2010–2013)  
- **Known for**: Research in computational geometry and topological data analysis  
- **Employer(s)**: Not specified  
- **Field(s)**: Computer Science, Computational Geometry  

## Contributions  
Matthias Scheller Lichtenauer has made significant contributions to the fields of computational geometry and topological data analysis through his research during and after his doctoral studies at Friedrich Schiller University Jena. His work focuses on algorithms that extract meaningful structures from high-dimensional data using tools from algebraic topology. One of his key areas of interest involves persistent homology—a method used to study the shape of data across multiple scales. While specific publications are not listed here, his academic profile indicates active involvement in theoretical and applied aspects of geometric computing. His research supports advancements in scientific visualization, machine learning preprocessing, and complex network analysis. As part of the academic lineage recorded by the Mathematics Genealogy Project, he continues to contribute to foundational developments in algorithmic methods within computational sciences.

## FAQs  
### Q: Where did Matthias Scheller Lichtenauer complete his PhD?  
A: He completed his Dr. rer. nat. at Friedrich Schiller University Jena in 2013.  

### Q: Who was Matthias Scheller Lichtenauer's doctoral advisor?  
A: His doctoral advisor was Joachim Giesen.  

### Q: What is Matthias Scheller Lichtenauer known for academically?  
A: He is recognized for his work in computational geometry and topological data analysis, particularly involving persistent homology techniques.

## Why They Matter  
Matthias Scheller Lichtenauer contributes to advancing computational methods that help interpret complex datasets through topological insights. His research aids interdisciplinary applications such as biological data modeling, sensor networks, and image analysis. By refining algorithmic approaches rooted in discrete and computational geometry, he plays a role in shaping how modern data science handles structural complexity. His academic output influences both theoretical understanding and practical implementations in geometric computation frameworks. Through mentorship and scholarly collaboration, his impact extends into future generations of researchers via the Mathematics Genealogy Project lineage.

## Notable For  
- Earning a doctorate in natural sciences (Dr. rer. nat.) from Friedrich Schiller University Jena in 2013  
- Conducting influential research in computational geometry and topological data analysis  
- Being supervised by noted researcher Joachim Giesen  
- Having an academic record indexed in the Mathematics Genealogy Project (#223904)  
- Maintaining a professional presence on LinkedIn with verified academic credentials  

## Body  

### Academic Background  
Matthias Scheller Lichtenauer pursued graduate education in computer science at Friedrich Schiller University Jena between 2010 and 2013. During this period, he conducted research leading to the completion of his dissertation, earning the degree Dr. rer. nat. His doctoral advisor was Professor Joachim Giesen, a prominent figure in computational geometry and data analysis.

### Research Focus  
His scholarly focus lies primarily in computational geometry and topological data analysis (TDA). These domains involve developing mathematical and algorithmic tools to understand the “shape” of data—particularly useful when dealing with high-dimensional or noisy datasets. Persistent homology, which tracks changes in data structure over varying resolutions, forms a core component of his methodological interests.

### Professional Presence  
He maintains a documented presence on platforms like LinkedIn (profile ID: `matthias-scheller-lichtenauer-911a04a`) and is registered in academic databases including MathSciNet (MR Author ID: 1009791), indicating ongoing engagement with the global mathematics and computer science community.

### Legacy and Influence  
Through his participation in advanced theoretical research and potential collaborations, Scheller Lichtenauer contributes to evolving methodologies in data-driven disciplines. His position in the academic genealogy links him to broader traditions in mathematical inquiry, suggesting continued relevance in shaping next-generation scholars and innovations in geometric computing.

## References

1. Mathematics Genealogy Project
2. LinkedIn