# Rachel Blagojevic

> computer science researcher at University of Auckland in New Zealadn

**Wikidata**: [Q117304225](https://www.wikidata.org/wiki/Q117304225)  
**Source**: https://4ort.xyz/entity/rachel-blagojevic

## Summary
Rachel Blagojevic is a New Zealand–based computer scientist and researcher. She earned a Doctor of Philosophy from the University of Auckland in 2011 for a doctoral thesis titled "Using Data Mining for Digital Ink Recognition" and has held research and academic appointments at the University of Auckland and Massey University.

## Biography
- Born: [not provided]
- Nationality: [not provided]
- Education: Doctor of Philosophy (University of Auckland, 2011); doctoral thesis "Using Data Mining for Digital Ink Recognition"
- Known for: Doctoral research applying data-mining techniques to digital ink recognition (2011)
- Employer(s): University of Auckland (affiliated profile); Massey University (employment record; appointment referenced from 2019)
- Field(s): Computer science; research in data mining and digital ink recognition

## Contributions
Rachel Blagojevic’s documented concrete contribution is her 2011 doctoral thesis, "Using Data Mining for Digital Ink Recognition." The thesis investigated the application of data-mining methods to the problem of recognizing digital ink (handwritten or pen-input strokes captured digitally). Her doctoral work was supervised by Beryl Plimmer, John Grundy, and Yong Wang and is archived at the University of Auckland (hdl.handle.net/2292/7526). The thesis represents a formal research output (doctoral dissertation) that combines data-mining techniques with handwriting/digital-ink recognition tasks. Beyond the thesis, the structured records list Blagojevic as a researcher and computer scientist with professional profiles held at the University of Auckland (cs.auckland.ac.nz/~rpat088/) and an employment record at Massey University beginning in 2019 (ORCID employment record). These institutional affiliations and the doctoral dissertation together form the verifiable published and professional record available in the provided sources.

## FAQs
### Q: Who is Rachel Blagojevic?
A: Rachel Blagojevic is a computer scientist and researcher with a PhD from the University of Auckland. She is associated with the University of Auckland and has an employment record at Massey University.

### Q: What is her main research contribution?
A: Her main documented research contribution is her 2011 doctoral thesis, "Using Data Mining for Digital Ink Recognition," which applied data-mining approaches to problems in digital ink (handwritten input) recognition.

### Q: Where did she receive her doctorate and who supervised it?
A: She received her Doctor of Philosophy from the University of Auckland in 2011. Her doctoral advisors were Beryl Plimmer, John Grundy, and Yong Wang.

### Q: What professional affiliations does she have?
A: Sources list her with a University of Auckland researcher profile and an employment record at Massey University (appointment referenced in 2019).

## Why They Matter
Rachel Blagojevic’s documented work matters in that it formally applied data-mining techniques to digital ink recognition in a doctoral research context. Digital ink recognition is a technical area that underpins handwriting recognition, pen-based user interfaces, and related human–computer interaction tasks. By framing and investigating this problem through data-mining methods in a PhD dissertation, Blagojevic contributed a rigorously reviewed research artifact (the thesis) that records methodology, experimental work, and scholarly findings available to the research community via the University of Auckland archive. Her supervisors—established academics Beryl Plimmer, John Grundy, and Yong Wang—situate the thesis within recognized research groups. Additionally, her professional affiliations with the University of Auckland and a documented employment record at Massey University connect her to New Zealand’s academic computer science community. Without this documented doctoral research and institutional participation, there would be one fewer formal study applying data-mining approaches specifically to digital ink recognition within the New Zealand academic record.

## Notable For
- Author of the doctoral thesis "Using Data Mining for Digital Ink Recognition" (University of Auckland, 2011).
- Doctor of Philosophy awarded by the University of Auckland (2011).
- Doctoral advisors: Beryl Plimmer, John Grundy, and Yong Wang.
- Professional affiliations: research profile at the University of Auckland and employment record at Massey University (appointment referenced 2019).
- Listed aliases and professional names: Rachel Venita Blagojevic; R Blagojevic; Rachel Patel.

## Body

### Names and Identifiers
- Given name: Rachel.
- Aliases: Rachel Venita Blagojevic; R Blagojevic; Rachel Patel.
- Professional profiles referenced at:
  - University of Auckland CS page: https://www.cs.auckland.ac.nz/~rpat088/
  - ORCID employment record: https://pub.orcid.org/v3.0/0000-0003-0737-3291/employment/8058937
- Thesis archive: http://hdl.handle.net/2292/7526

### Education and Degree
- Degree: Doctor of Philosophy.
- Institution: University of Auckland.
- Year: 2011 (point in time recorded for the degree).
- Thesis title: "Using Data Mining for Digital Ink Recognition."
- Thesis type: Doctoral thesis.
- Thesis advisors: Beryl Plimmer; John Grundy; Yong Wang.
- Thesis URL: http://hdl.handle.net/2292/7526

### Research Focus
- Primary documented research topic: application of data-mining techniques to digital ink (handwriting/pen-input) recognition.
- Discipline: computer science; described in sources as "computer science researcher" and "computer scientist."

### Employment and Affiliations
- University of Auckland: listed affiliation/profile (cs.auckland.ac.nz/~rpat088/).
- Massey University: employment record referenced (ORCID employment entry with a recorded employment start date of 2019-10-15 in structured data).
- On-focus listing: included on NZThesisProject focus list with references to New Zealand thesis resources.

### Publications and Outputs
- Verified published output: Doctoral thesis ("Using Data Mining for Digital Ink Recognition," 2011).
- No other specific papers, patents, products, or projects are listed in the provided source material.

### Gender and Instance
- Sex/gender: female (recorded in source data).
- Instance: human (Wikidata instance_of: human).

(End of entry.)

## References

1. [Source](http://hdl.handle.net/2292/7526)
2. [Source](https://orcid.org/0000-0003-0737-3291)
3. [Source](https://www.cs.auckland.ac.nz/~rpat088/)
4. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0003-0737-3291/employment/8058937)
5. [Source](https://www.massey.ac.nz/massey/expertise/phd-student-profiles/phd-student-profiles_home.cfm)