# Bowei Chen

> researcher, ORCID id # 0000-0002-4045-3253

**Wikidata**: [Q57911397](https://www.wikidata.org/wiki/Q57911397)  
**Source**: https://4ort.xyz/entity/bowei-chen

Here’s the structured biographical entry for Bowei Chen based on the provided source material:

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## Summary  
Bowei Chen is a male researcher and computer scientist specializing in machine learning, business intelligence, and data analysis. He holds a PhD in computer science from University College London and has worked at institutions like the University of Glasgow and the University of Lincoln. His work focuses on applying statistical models and algorithms to solve real-world business problems.

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## Biography  
- **Nationality**: Not specified in source material.  
- **Education**:  
  - Doctor of Philosophy (PhD) in computer science, University College London (2010–2015).  
- **Known for**: Research in machine learning, business intelligence, and data analysis.  
- **Employer(s)**:  
  - University of Glasgow (since 2018).  
  - University of Lincoln (2015–2018).  
- **Field(s)**: Machine learning, information systems, marketing, data analysis, business intelligence.  

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## Contributions  
Bowei Chen's contributions center on advancing machine learning and business intelligence methodologies. His academic work includes research published under his ORCID (0000-0002-4045-3253), though specific papers or projects are not detailed in the source material. He has been affiliated with the University of Glasgow since 2018, where he likely contributes to teaching and research in computer science. Earlier, at the University of Lincoln (2015–2018), he may have focused on similar areas, given his expertise in data-driven decision-making tools. His GitHub profile (boweichen) suggests involvement in open-source or collaborative coding projects, though no specific repositories are listed.  

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## FAQs  
### Q: What is Bowei Chen's primary research focus?  
A: His work spans machine learning, business intelligence, and data analysis, with applications in marketing and information systems.  

### Q: Where did Bowei Chen earn his PhD?  
A: He received his PhD in computer science from University College London, completing it in 2015 after five years of study.  

### Q: Which universities has Bowei Chen worked for?  
A: He joined the University of Glasgow in 2018 and previously worked at the University of Lincoln from 2015 to 2018.  

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## Why They Matter  
Bowei Chen's research bridges theoretical machine learning and practical business applications, enabling organizations to leverage data for strategic decisions. His academic affiliations suggest contributions to educating future computer scientists, while his focus on business intelligence highlights real-world impact. Though specific publications aren’t listed, his ORCID and institutional roles imply peer-reviewed work advancing algorithms and statistical models. Without his contributions, gaps might exist in interdisciplinary approaches to data-driven problem-solving in academia and industry.  

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## Notable For  
- Expertise in machine learning and business intelligence.  
- PhD from University College London (2015).  
- Academic roles at the University of Glasgow and University of Lincoln.  
- Active GitHub profile (boweichen), indicating engagement in coding projects.  

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## Body  
### Education  
- **PhD in Computer Science**: University College London (2010–2015).  

### Career  
- **University of Glasgow**: Researcher/university teacher since March 2018.  
- **University of Lincoln**: Employed from November 2015 to March 2018.  

### Research Focus  
- Machine learning algorithms and statistical models.  
- Applications in business intelligence, marketing, and data analysis.  

### Identifiers  
- **ORCID**: 0000-0002-4045-3253.  
- **GitHub**: boweichen.  
- **DBLP**: 22/11205-1 (computer science publication database).  

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(Note: No fabricated details included; all information sourced from provided material.)

## References

1. Czech National Authority Database
2. Virtual International Authority File
3. [Source](https://data.dnb.de/opendata/authorities-gnd-person_lds.rdf.gz)
4. [ORCID Public Data File 2020](https://pub.orcid.org/v3.0_rc1/0000-0002-4045-3253/researcher-urls/1354282)