# Marina Höhne

> computer scientist

**Wikidata**: [Q87679027](https://www.wikidata.org/wiki/Q87679027)  
**Source**: https://4ort.xyz/entity/marina-hohne

## Summary
Marina Höhne is a German computer scientist specializing in machine learning and artificial intelligence. She is a full professor at the University of Potsdam and previously worked as a researcher at Technische Universität Berlin.

## Biography
- Born: Aachen, Germany
- Nationality: German
- Education: Doctoral degree under advisor Klaus-Robert Müller
- Known for: Research in machine learning and artificial intelligence
- Employer(s): University of Potsdam (since 2022), Technische Universität Berlin (2020-2022)
- Field(s): Machine learning, artificial intelligence

## Contributions
Marina Höhne has published extensively in the field of machine learning and artificial intelligence, with her work indexed across multiple academic platforms including Google Scholar, Semantic Scholar, and DBLP. Her research focuses on developing and studying algorithms and statistical models that enable computer systems to perform tasks without explicit instructions. As a doctoral student under Klaus-Robert Müller, she contributed to foundational research in artificial intelligence. Since becoming a full professor at the University of Potsdam in 2022, she has continued advancing the field through both research and academic leadership.

## FAQs
### Q: What is Marina Höhne's current position?
A: Marina Höhne is a full professor at the University of Potsdam, where she has worked since November 2022.

### Q: Who was Marina Höhne's doctoral advisor?
A: Marina Höhne completed her doctoral studies under the supervision of Klaus-Robert Müller, a prominent German computer scientist and artificial intelligence researcher.

### Q: What are Marina Höhne's main research areas?
A: Marina Höhne specializes in machine learning and artificial intelligence, focusing on algorithms and statistical models that enable intelligent behavior in computer systems.

## Why They Matter
Marina Höhne represents the next generation of German computer scientists advancing artificial intelligence research. Her work builds upon the foundations laid by her advisor Klaus-Robert Müller while establishing her own contributions to the field. As a professor, she not only conducts research but also trains future computer scientists, helping to maintain Germany's position in global AI development. Her academic career trajectory from doctoral student to full professor demonstrates the continuity of expertise in German computer science institutions.

## Notable For
- Full professor at University of Potsdam specializing in machine learning
- Extensive publication record across major academic databases
- Doctoral work under renowned AI researcher Klaus-Robert Müller
- Research contributions to artificial intelligence algorithms and models
- Academic leadership in German computer science education

## Body
### Academic Background
Marina Höhne completed her doctoral studies under the supervision of Klaus-Robert Müller at Technische Universität Berlin. Her academic journey reflects the strong tradition of computer science education in Germany, with her work contributing to the field's international reputation.

### Research Focus
Her research centers on machine learning and artificial intelligence, specifically developing algorithms and statistical models that enable computer systems to exhibit intelligent behavior without explicit programming. This work aligns with global trends in AI development while maintaining the rigorous theoretical foundations characteristic of German computer science.

### Professional Trajectory
Höhne's career progressed from doctoral student to researcher at Technische Universität Berlin (2020-2022), then to full professor at the University of Potsdam in 2022. This trajectory demonstrates both her research capabilities and her transition into academic leadership.

### Academic Impact
Her work is documented across multiple academic platforms including Google Scholar, Semantic Scholar, DBLP, and Microsoft Academic, indicating significant scholarly impact. The breadth of her academic identifiers suggests substantial contributions to peer-reviewed literature in her field.

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## References

1. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0003-3090-6279/employment/14320021)
2. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0003-3090-6279/employment/20880682)