# Nilesh Dalvi

> Ph.D. University of Washington 2007

**Wikidata**: [Q102320764](https://www.wikidata.org/wiki/Q102320764)  
**Source**: https://4ort.xyz/entity/nilesh-dalvi

## Summary
Nilesh Dalvi is a computer scientist known for his research in probabilistic databases and data uncertainty management. He earned his Ph.D. from the University of Washington in 2007 under the supervision of Dan Suciu, contributing foundational work to the field of uncertain data management.

## Biography
- Born: [No date/place available]  
- Nationality: [Not specified]  
- Education: Ph.D. in Computer Science, University of Washington (2007)  
- Known for: Research on probabilistic databases and managing data uncertainty  
- Employer(s): [Not specified]  
- Field(s): Computer science, databases, data management  

## Contributions  
Nilesh Dalvi’s primary contribution is his doctoral thesis, *Managing Uncertainty Using Probabilistic Databases* (2007), which addresses the challenge of handling uncertainty in large datasets. His work laid groundwork for systems that reason with incomplete or probabilistic information, a critical need in fields like AI, sensor networks, and scientific research. While specific follow-up publications or products are not detailed in the source material, his thesis directly influenced advancements in probabilistic database design and query processing. By integrating statistical models into database architectures, Dalvi’s research enabled more robust decision-making in data-driven environments. His collaboration with advisor Dan Suciu, a renowned expert in data management, further contextualizes his contributions within a lineage of academic innovation.  

## FAQs  
### Q: Where did Nilesh Dalvi earn his Ph.D.?  
A: He received his Ph.D. in Computer Science from the University of Washington in 2007.  

### Q: What is Nilesh Dalvi best known for?  
A: His work on probabilistic databases, particularly his thesis *Managing Uncertainty Using Probabilistic Databases*, which addresses data uncertainty in large-scale systems.  

### Q: Who supervised Nilesh Dalvi’s doctoral research?  
A: His doctoral advisor was Dan Suciu, a prominent computer scientist specializing in data management.  

## Why They Matter  
Nilesh Dalvi’s research bridges the gap between theoretical computer science and practical data management challenges. By developing frameworks for probabilistic databases, he provided tools to handle uncertainty inherent in real-world data, from sensor readings to financial forecasts. This work underpins modern applications in machine learning, IoT systems, and scientific computing, where data ambiguity is common. Without robust methods for uncertainty management, many contemporary data-driven technologies would lack reliability. Dalvi’s academic lineage, including his collaboration with Dan Suciu, further amplifies his impact, as his ideas have been disseminated through academic networks and integrated into broader database research agendas.  

## Notable For  
- Ph.D. thesis: *Managing Uncertainty Using Probabilistic Databases* (2007)  
- Doctoral advisor: Dan Suciu, a leading figure in data management research  
- Academic affiliation: University of Washington, a top institution for computer science  

## Body  
### Education  
Nilesh Dalvi completed his Ph.D. in Computer Science at the University of Washington in 2007. His dissertation, *Managing Uncertainty Using Probabilistic Databases*, focused on developing systems to handle probabilistic data, a critical challenge in environments with incomplete or ambiguous information.  

### Academic Work  
Dalvi’s research was supervised by Dan Suciu, a Romanian computer scientist recognized for contributions to data management and XML query processing. This mentorship placed Dalvi within a tradition of rigorous academic inquiry into data systems.  

### Research Impact  
While specific post-Ph.D. roles or publications are not detailed in the source material, Dalvi’s thesis directly addressed a growing need for uncertainty management in emerging technologies. Probabilistic databases, as explored in his work, enable applications such as:  
- **Sensor networks**: Managing unreliable or intermittent data streams.  
- **AI/ML systems**: Integrating probabilistic reasoning into data queries.  
- **Scientific research**: Analyzing datasets with inherent measurement uncertainties.  

His approach emphasized scalable solutions, ensuring probabilistic queries could be processed efficiently—a prerequisite for real-world adoption. This work remains relevant as data volumes and complexity continue to grow.  

### Legacy  
Dalvi’s contributions are emblematic of the University of Washington’s strong computer science program, which has produced numerous innovators in data management. By tackling uncertainty—a perennial challenge in computing—he advanced tools critical to modern data infrastructure. Though not a household name, his foundational research supports technologies that underpin contemporary decision-making across industries.

## References

1. Mathematics Genealogy Project
2. WorldCat