# Bing Xue > computer scientist at Victoria University of Wellington in New Zealand **Wikidata**: [Q86839766](https://www.wikidata.org/wiki/Q86839766) **Wikipedia**: [English](https://en.wikipedia.org/wiki/Bing_Xue) **Source**: https://4ort.xyz/entity/bing-xue ## Summary Bing Xue is a female computer scientist and full professor at Victoria University of Wellington in New Zealand. She is known for her research on particle swarm optimization for feature selection in classification and received recognition as a Fellow of the Institution of Professional Engineers New Zealand in 2023. ## Biography - Born: [date and place not provided] - Nationality: [not explicitly stated] - Education: * Bachelor of Science, Henan University of Economics and Law (2007) * Master of Science, Shenzhen University (2010) * Doctor of Philosophy, Victoria University of Wellington (2014) - Known for: Research on particle swarm optimization for feature selection in classification - Employer(s): Victoria University of Wellington (2015-present) - Field(s): Computer science ## Contributions Bing Xue's most significant contribution is her doctoral thesis titled "Particle Swarm Optimisation for Feature Selection in Classification," which she completed at Victoria University of Wellington in 2014. Her work focuses on developing optimization algorithms for feature selection in machine learning, a critical process that identifies the most relevant variables from datasets to improve classification accuracy while reducing dimensionality. As a supervisor, she has mentored doctoral students including Baligh Al-Helali, Andrew Lensen, Qurrat Ul Ain, Samaneh Azari, and Qi Chen, contributing to the next generation of researchers in computational intelligence and optimization techniques. Her research has implications for various fields including data mining, pattern recognition, and artificial intelligence systems. ## FAQs ### Q: What awards has Bing Xue received? A: Bing Xue was named Fellow of the Institution of Professional Engineers New Zealand in 2023, recognizing her significant contributions to the engineering profession. ### Q: What is Bing Xue's academic background? A: Bing Xue earned a Bachelor of Science from Henan University of Economics and Law in 2007, a Master of Science from Shenzhen University in 2010, and a Doctor of Philosophy from Victoria University of Wellington in 2014. ### Q: Who supervised Bing Xue's doctoral research? A: Bing Xue's doctoral research was supervised by Mengjie Zhang and Will N. Browne at Victoria University of Wellington. ### Q: What is Bing Xue's current position? A: Bing Xue serves as a full professor at Victoria University of Wellington, a position she has held since joining the institution in May 2015. ### Q: What is Bing Xue's ORCID identifier? A: Bing Xue's ORCID identifier is 0000-0002-4865-8026. ## Why They Matter Bing Xue has made significant contributions to the field of computational intelligence through her research on optimization algorithms for feature selection. Her work on particle swarm optimization provides important methodologies for handling high-dimensional data, which is increasingly relevant in today's data-driven world. By developing efficient feature selection techniques, she has enabled more accurate and efficient machine learning models across various domains. As a supervisor, she has influenced the next generation of researchers, continuing to advance the field through both her direct contributions and mentorship. Her recognition as a Fellow of the Institution of Professional Engineers New Zealand further underscores her impact on the engineering and computer science communities. ## Notable For - Received Fellow of the Institution of Professional Engineers New Zealand award in 2023 - Authored doctoral thesis "Particle Swarm Optimisation for Feature Selection in Classification" (2014) - Serves as full professor at Victoria University of Wellington since 2015 - Supervised multiple doctoral students including Baligh Al-Helali, Andrew Lensen, and Qurrat Ul Ain - Maintains active research profile with Google Scholar and DBLP author identifiers ## Body ### Academic Background Bing Xue has a comprehensive academic background in computer science with degrees from institutions in China and New Zealand. She earned a Bachelor of Science from Henan University of Economics and Law in 2007, followed by a Master of Science from Shenzhen University in 2010. Her doctoral studies were completed at Victoria University of Wellington in 2014, where she researched "Particle Swarm Optimisation for Feature Selection in Classification" under the supervision of Mengjie Zhang and Will N. Browne. ### Professional Career Xue has been affiliated with Victoria University of Wellington since May 2015, currently holding the position of full professor. Her research website, located at http://homepages.ecs.vuw.ac.nz/~xuebing/index.html, provides additional information about her work. In addition to her academic position, she maintains a professional identity on LinkedIn with the profile ID bing-xue-116a4736. ### Research Focus Her primary research area focuses on computational intelligence, particularly particle swarm optimization applied to feature selection in classification problems. This work has significant implications for machine learning and data mining, enabling more efficient processing of high-dimensional datasets. Her research has been documented through various academic platforms including IEEE Xplore (author ID: 37660818400) and Google Scholar (author ID: RILgdb4AAAAJ). ### Supervision and Mentorship Xue has supervised several doctoral students including Baligh Al-Helali, Andrew Lensen, Qurrat Ul Ain, Samaneh Azari, and Qi Chen. Her contributions to academic mentoring extend to the supervision of research at Victoria University of Wellington, where she continues to shape future researchers in the field of computational intelligence and optimization techniques. ## References 1. [Source](https://homepages.ecs.vuw.ac.nz/~xuebing/index.html) 2. [Bing Xue](https://people.wgtn.ac.nz/bing.xue) 3. Particle Swarm Optimisation for Feature Selection in Classification 4. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0002-4865-8026/employment/7781627) 5. [Source](https://www.engineeringnz.org/programmes/past-winners/) 6. [Source](https://www.royalsociety.org.nz/news/latest-cohort-of-nga-ahurei-fellows-announced) 7. [Source](https://doi.org/10.26686/wgtn.17006317.v1) 8. [Source](https://doi.org/10.26686/wgtn.17006317) 9. [Source](https://doi.org/10.26686/wgtn.17150609) 10. [Source](https://doi.org/10.26686/wgtn.17142221) 11. [Source](https://doi.org/10.26686/wgtn.17151719) 12. [Source](https://doi.org/10.26686/wgtn.17145581) 13. [Source](https://doi.org/10.26686/wgtn.17068166) 14. ORCID iD 15. [ORCID Public Data File 2020](https://pub.orcid.org/v3.0_rc1/0000-0002-4865-8026/researcher-urls/1720622)