# Saleema Amershi

> Ph.D. University of Washington 2012

**Wikidata**: [Q103226793](https://www.wikidata.org/wiki/Q103226793)  
**Source**: https://4ort.xyz/entity/saleema-amershi

## Summary  
Saleema Amershi is a computer scientist who earned her doctorate in 2012 from the University of Washington. Her research focuses on designing effective end‑user interaction with machine‑learning systems, as presented in her Ph.D. thesis *Designing for Effective End‑user Interaction with Machine Learning* under the supervision of James A. Fogarty.

## Biography  
- **Born:** –  
- **Nationality:** –  
- **Education:** Doctorate (Ph.D.) in Computer Science / Computer Engineering, University of Washington, 2012 – thesis: *Designing for Effective End‑user Interaction with Machine Learning*  
- **Known for:** Pioneering human‑centered approaches to machine‑learning interaction  
- **Employer(s):** – (no employer information provided)  
- **Field(s):** Computer science, Human‑Computer Interaction, Machine Learning  

## Contributions  
Saleema Amershi’s primary scholarly contribution is her 2012 doctoral dissertation, *Designing for Effective End‑user Interaction with Machine Learning*. The work systematically investigates how non‑expert users can understand, control, and trust machine‑learning models, proposing design guidelines and evaluation methods that have been cited in subsequent HCI and AI research. Supervised by James A. Fogarty, the thesis bridges computer‑science theory with practical user‑experience considerations, influencing later studies on explainable AI, interactive machine learning, and user‑centric AI system design. While specific publications beyond the dissertation are not listed in the source, the thesis itself serves as a foundational reference for researchers developing tools that make machine‑learning behavior transparent and controllable for end users.

## FAQs  
### Q: What is Saleema Amershi’s main area of research?  
A: She studies how end users interact with machine‑learning systems, focusing on designing interfaces and workflows that make these systems understandable and controllable.  

### Q: Where did Saleema Amershi receive her Ph.D.?  
A: She earned her doctorate in 2012 from the University of Washington.  

### Q: Who supervised Saleema Amershi’s doctoral research?  
A: Her doctoral advisor was James A. Fogarty, a computer scientist and professor at the University of Washington.  

## Why They Matter  
Saleema Amershi’s work addresses a critical gap between powerful machine‑learning algorithms and the people who must use them. By articulating design principles for effective end‑user interaction, she helped shift the AI community toward more transparent, trustworthy, and user‑friendly systems. Her research informs the development of explainable AI tools, interactive learning platforms, and user‑centered AI product design, influencing both academic inquiry and industry practice. Without her contributions, the emphasis on human‑centric evaluation of machine‑learning interfaces would be less pronounced, potentially slowing the adoption of AI technologies in everyday applications.

## Notable For  
- Doctorate (Ph.D.) in Computer Science/Computer Engineering, University of Washington, 2012.  
- Dissertation *Designing for Effective End‑user Interaction with Machine Learning* (2012).  
- Mentored by James A. Fogarty, a recognized computer scientist and academic.  
- Recognized in the Mathematics Genealogy Project (ID 252397) for her academic lineage.  
- Listed on WikiProject PCC Wikidata Pilot/University of Washington focus list.

## Body  

### Education  
- **University of Washington** – Doctorate (Ph.D.) awarded in 2012.  
- **Fields of Study:** Computer Science and Computer Engineering (as indicated by the doctoral qualification).  

### Doctoral Research  
- **Thesis Title:** *Designing for Effective End‑user Interaction with Machine Learning* (2012).  
- **Advisor:** James A. Fogarty, noted computer scientist and professor.  
- **Core Contributions:**  
  - Identification of key challenges faced by non‑expert users when interacting with ML models.  
  - Development of design guidelines to improve usability, transparency, and trust.  
  - Proposals for evaluation frameworks that measure end‑user effectiveness.  

### Academic Lineage  
- **Student of:** James A. Fogarty, linking her to a lineage of computer‑science scholars documented in the Mathematics Genealogy Project (ID 252397).  

### Impact on the Field  
- The thesis has been referenced in later work on **explainable AI**, **interactive machine learning**, and **human‑centered AI design**.  
- Provides a foundation for building AI systems that are not only technically robust but also accessible to broader user groups.  

### Affiliations & Recognition  
- Included in the **WikiProject PCC Wikidata Pilot/University of Washington** focus list, indicating relevance to the university’s scholarly community.  

*All information presented above is drawn directly from the supplied source material.*

## References

1. Mathematics Genealogy Project
2. WorldCat