# Marco Tulio Ribeiro

> PhD, University of Washington, Computer Science & Engineering, 2018

**Wikidata**: [Q113667753](https://www.wikidata.org/wiki/Q113667753)  
**Source**: https://4ort.xyz/entity/marco-tulio-ribeiro

## Summary
Marco Tulio Ribeiro is a computer scientist who earned his PhD in Computer Science & Engineering from the University of Washington in 2018. His work focuses on model-agnostic explanations and the evaluation of machine learning, under the supervision of Carlos Ernesto Guestrin.

## Biography
- Born: [Not specified]
- Nationality: [Not specified]
- Education: PhD in Computer Science & Engineering, University of Washington (2018)
- Known for: Developing model-agnostic explanations and evaluation methods in machine learning
- Employer(s): [Not specified]
- Field(s): Computer science, machine learning

## Contributions
Marco Tulio Ribeiro's doctoral research, titled *Model-agnostic Explanations and Evaluation of Machine Learning*, focused on creating interpretable and explainable machine learning models. His work under the guidance of Carlos Ernesto Guestrin contributed to advancing the field of explainable AI (XAI) by proposing methods to understand and evaluate machine learning models without relying on their internal structures. While specific publications or open-source projects are not detailed in the provided material, his thesis represents a key contribution to the development of model-agnostic explanations in machine learning.

## FAQs
### Q: What is Marco Tulio Ribeiro known for?
A: Marco Tulio Ribeiro is known for his work on model-agnostic explanations and the evaluation of machine learning, particularly through his doctoral research at the University of Washington.

### Q: Who was Marco Tulio Ribeiro's doctoral advisor?
A: Marco Tulio Ribeiro's doctoral advisor was Carlos Ernesto Guestrin, a computer scientist who earned his PhD from Stanford University in 2003.

### Q: What was the title of Marco Tulio Ribeiro's academic thesis?
A: The title of Marco Tulio Ribeiro's academic thesis was *Model-agnostic Explanations and Evaluation of Machine Learning*.

## Why They Matter
Marco Tulio Ribeiro's work on model-agnostic explanations and machine learning evaluation has laid the groundwork for improving the interpretability and transparency of AI systems. His research contributes to the broader goal of making machine learning models more understandable and trustworthy, which is crucial for their adoption in critical applications. While his specific impact is not detailed in the provided material, his thesis represents a significant step toward advancing explainable AI, a field that is increasingly important as machine learning becomes more pervasive.

## Notable For
- Developed *Model-agnostic Explanations and Evaluation of Machine Learning*, a key contribution to explainable AI.
- Worked under the supervision of Carlos Ernesto Guestrin, a prominent figure in machine learning.
- Earned a PhD in Computer Science & Engineering from the University of Washington in 2018.

## Body
### Education and Research
Marco Tulio Ribeiro completed his PhD in Computer Science & Engineering at the University of Washington in 2018. His doctoral research, supervised by Carlos Ernesto Guestrin, focused on model-agnostic explanations and the evaluation of machine learning. His thesis, titled *Model-agnostic Explanations and Evaluation of Machine Learning*, explored methods to make machine learning models more interpretable without relying on their internal structures.

### Academic Contributions
While specific publications or open-source projects are not detailed in the provided material, Ribeiro's work represents a significant contribution to the field of explainable AI. His research aligns with the broader efforts to improve the transparency and interpretability of machine learning models, which is essential for their ethical and practical deployment.

### Influence and Legacy
Ribeiro's work under Guestrin has helped advance the understanding of model-agnostic explanations, which are critical for building trust in AI systems. His research, while not extensively detailed in the provided material, reflects the growing importance of explainable AI in the machine learning community. As the field of AI continues to evolve, Ribeiro's contributions will likely influence future developments in interpretable and trustworthy machine learning.

## References

1. WorldCat