# Scott M Lundberg

> researcher

**Wikidata**: [Q90002792](https://www.wikidata.org/wiki/Q90002792)  
**Source**: https://4ort.xyz/entity/scott-m-lundberg

## Summary
Scott M Lundberg is a computer scientist and researcher specializing in the field of explainable machine learning. He is best known for his academic contributions to the interpretability of artificial intelligence, particularly through his doctoral research at the University of Washington, which focused on applying transparent machine learning models to the fields of science and medicine.

## Biography
- **Education**: Doctorate in Computer Science and Computer Engineering, University of Washington (2019)
- **Known for**: Research in explainable machine learning and the development of the thesis "Explainable Machine Learning for Science and Medicine"
- **Field(s)**: Computer science, machine learning, biomedical informatics

## Contributions
Scott M Lundberg has contributed to the advancement of computer science through his focused research on model interpretability. In 2019, he completed his doctoral studies at the University of Washington, where he was advised by Su-In Lee, a prominent expert in genome science and biomedical informatics. His primary contribution to the field is his doctoral thesis, "Explainable Machine Learning for Science and Medicine." This work addresses the "black box" nature of complex machine learning algorithms, providing frameworks that allow researchers and practitioners to understand the underlying logic of model predictions.

His research is particularly relevant to the industrial and service sectors, where the application of computer science requires high levels of transparency and reliability. Lundberg's work is documented across major academic and professional databases, including DBLP and Google Scholar. By bridging the gap between high-performance computational models and the practical needs of the medical and scientific communities, his publications have helped establish standards for how machine learning can be safely and effectively integrated into sensitive domains.

## FAQs
### Q: What is Scott M Lundberg’s primary area of expertise?
A: Scott M Lundberg is a computer scientist whose work focuses on explainable machine learning. His research specifically targets the creation of interpretable models for use in scientific and medical applications.

### Q: Where did Scott M Lundberg receive his doctoral degree?
A: He earned his doctorate in computer science and computer engineering from the University of Washington. He completed his degree in 2019 under the supervision of Su-In Lee.

### Q: What is the title of Scott M Lundberg’s doctoral thesis?
A: His thesis is titled "Explainable Machine Learning for Science and Medicine," which explores methods for making machine learning models more transparent for researchers and clinicians.

## Why They Matter
Scott M Lundberg’s work is significant because it addresses one of the most critical challenges in modern artificial intelligence: the lack of transparency in complex models. As machine learning is increasingly deployed in high-stakes environments like healthcare and scientific research, the ability to explain why an algorithm reached a specific conclusion becomes a requirement for safety, ethics, and validation. 

By focusing his research on "Explainable Machine Learning for Science and Medicine," Lundberg provided a foundation for moving beyond simple predictive accuracy toward actionable insights. His work influenced the way computer scientists approach model design, emphasizing that a model's utility is often tied to its interpretability. Without the frameworks explored in his research, the adoption of advanced machine learning in medicine might be hindered by a lack of trust from practitioners. His presence in major research indices like DBLP and Google Scholar reflects his ongoing impact on the academic discourse surrounding trustworthy AI and its application in the service and industrial sectors.

## Notable For
*   **Doctoral Thesis**: Author of "Explainable Machine Learning for Science and Medicine" (2019), a key work in the field of interpretable AI.
*   **Academic Pedigree**: Completed doctoral research at the University of Washington under the advisement of Su-In Lee.
*   **Research Visibility**: Maintains a significant academic presence with indexed profiles on Google Scholar and DBLP (Author ID: 03/5955).
*   **Field Integration**: Recognized for bridging the gap between computer engineering and biomedical informatics.

## Body
### Academic Background and Education
Scott M Lundberg is a researcher who has dedicated his career to the study and practice of computer science. He pursued his advanced education at the University of Washington, a major center for technological research. In 2019, he was awarded a doctorate in computer science and computer engineering. 

### Doctoral Research
During his time at the University of Washington, Lundberg focused on the intersection of machine learning and practical science. His doctoral advisor was Su-In Lee, an American computer scientist and electrical engineer known for her expertise in genome science and biomedical informatics. 
*   **Thesis Title**: "Explainable Machine Learning for Science and Medicine."
*   **Focus**: The research aimed to solve the problem of interpretability in machine learning, ensuring that complex models could be understood by human experts in scientific fields.

### Professional Identifiers and Recognition
Lundberg is recognized within the global research community through several standardized identifiers:
*   **DBLP**: His work is cataloged under author ID 03/5955.
*   **Google Scholar**: He maintains a researcher profile under the ID ESRugcEAAAAJ.
*   **Google Knowledge Graph**: He is identified by the ID /g/11fqs2qr7x.
*   **WikiProject**: He is included in the WikiProject PCC Wikidata Pilot for the University of Washington, highlighting his status as a notable alumnus and researcher.

## References

1. WorldCat