# Urvesh Bhowan

> computer scientist at Amazon Web Services

**Wikidata**: [Q125385451](https://www.wikidata.org/wiki/Q125385451)  
**Source**: https://4ort.xyz/entity/urvesh-bhowan

## Summary
Urvesh Bhowan is a computer scientist working at Amazon Web Services, known for his research on genetic programming for classification with unbalanced data, which he completed as his doctoral thesis at Victoria University of Wellington.

## Biography
- Born: [date and place not provided]
- Nationality: [country not provided]
- Education: Doctor of Philosophy from Victoria University of Wellington (2012)
- Known for: Genetic Programming for Classification with Unbalanced Data
- Employer(s): Amazon Web Services, Victoria University of Wellington, Trinity College, Dublin
- Field(s): Computer Science

## Contributions
Urvesh Bhowan's primary contribution is his doctoral thesis titled "Genetic Programming for Classification with Unbalanced Data," completed in 2012 at Victoria University of Wellington under the supervision of Mengjie Zhang and Mark D. Johnston. The thesis focused on developing genetic programming approaches to address classification problems with imbalanced datasets, a common challenge in machine learning where some classes are significantly underrepresented.

## FAQs
### Q: What is Urvesh Bhowan's current position?
A: He works as a computer scientist at Amazon Web Services.

### Q: Where did he complete his doctoral studies?
A: He earned his Doctor of Philosophy from Victoria University of Wellington.

### Q: What was the title of his doctoral thesis?
A: His doctoral thesis was titled "Genetic Programming for Classification with Unbalanced Data."

## Why They Matter
Urvesh Bhowan's work on genetic programming for classification with unbalanced data addresses a critical challenge in machine learning where datasets often contain significantly more samples from certain classes than others. His research contributes to more robust and accurate classification models, particularly in domains where data imbalance is common, such as medical diagnosis, fraud detection, and anomaly detection. His work helps improve the reliability of machine learning systems in real-world applications where class imbalance is prevalent.

## Notable For
- Completed doctoral thesis on "Genetic Programming for Classification with Unbalanced Data" at Victoria University of Wellington in 2012
- Worked as a computer scientist at Amazon Web Services
- Received academic training at both Victoria University of Wellington and Trinity College, Dublin
- Published research in IEEE Xplore with author ID 37680741600
- Listed on the NZThesisProject focus list

## Body
### Academic Background
Urvesh Bhowan earned his Doctor of Philosophy degree from Victoria University of Wellington in 2012. His doctoral thesis, titled "Genetic Programming for Classification with Unbalanced Data," was supervised by Mengjie Zhang and Mark D. Johnston. The research focused on developing genetic programming approaches to address classification problems where datasets exhibit significant class imbalance.

### Career Progression
Bhowan has held academic positions at both Victoria University of Wellington and Trinity College, Dublin before joining Amazon Web Services as a computer scientist. His professional affiliations include IEEE Xplore with author ID 37680741600 and Google Scholar with ID snqVr-EAAAAJ.

### Research Focus
The core of Bhowan's research centers on genetic programming applications in machine learning, particularly for classification tasks with unbalanced datasets. This area of study addresses the challenge where some classes in a dataset are significantly underrepresented compared to others, which can lead to biased or inaccurate classification models.

### Professional Contributions
As a computer scientist at Amazon Web Services, Bhowan contributes to the development of machine learning systems and algorithms. His academic background in genetic programming and classification with unbalanced data provides a foundation for developing more robust and effective machine learning solutions in industry applications.

## References

1. [Source](https://ieeexplore.ieee.org/author/37680741600)
2. [Source](https://doi.org/10.26686/wgtn.16999045)
3. [Source](https://scholar.google.com/citations?user=snqVr-EAAAAJ)