
Overview
Background
Shane Culpepper is a Professor of Artificial Intelligence at the University of Queensland in St. Lucia, Australia. Before joining the University of Queensland in 2023, Professor Culpepper held a continuing academic position at RMIT University in Melbourne, Australia. He received his PhD in Computer Science from the University of Melbourne in 2008. His research focuses primarily on building better Search and Recommendation Systems and is primarily interested how to responsibly integrate efficient and scalable generative AI models for search, recommendation, and question answering. Professor Culpepper’s work has applications in a number of downstream applications for Legal, Health, real estate speculation. He has been instrumental in founding the AI Research Network and the Research Center for Enterprise AI at the University of Queensland.
Over his 17 year career, Professor Culpepper has supervised 19 PhD students and co-authored more than 140 peer reviewed papers with 132 different research collaborators on problems that range from core basic research, such as algorithm efficiency and scalability, to practical real world problems on building and deploying new machine learning algorithms for search and recommendation systems. While often technical, his work is always user-driven as humans are the main consumers of this technology. This user-centric research focus has led to several papers on controlled user studies which guide the development of better evaluation techniques which model human behaviour. In the last 5 years, Professor Culpepper has been a program co-chair for international conferences such as SIGIR and CIKM, and co-organized conferences such as WSDM and SWIRL. Professor Culpepper previously held an ARC DECRA fellowship in 2013 as well as an RMIT Vice-Chancellor's Principal Researcher fellowship in 2017. Before joining the University of Queensland. Professor Culpepper was the founding director of the Centre for Information Discovery and Data Analytics at RMIT University. In total, he has been a chief investigator on 11 research grants totalling ~$3.8 Million AUD.
Availability
- Professor Shane Culpepper is:
- Available for supervision
- Media expert
Fields of research
Qualifications
- Doctor of Philosophy of Computer Science, University of Melbourne
- Member, Association for Computing Machinery, Association for Computing Machinery
- Honorary Fellow, RMIT University, RMIT University
Works
Search Professor Shane Culpepper’s works on UQ eSpace
2025
Conference Publication
The effects of demographic instructions on LLM personas
Magnossão de Paula, Angel Felipe, Culpepper, J. Shane, Moffat, Alistair, Pathiyan Cherumanal, Sachin, Scholer, Falk and Trippas, Johanne (2025). The effects of demographic instructions on LLM personas. 48th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR 2025), Padua, Italy, 13-18 July 2025. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3726302.3730255
2025
Conference Publication
IR for AAC Users: A Hyperdimensional Computing (Vector Symbolic Architectures) approach
Briegel, Hunter, Pagal, Maya and Culpepper, J. Shane (2025). IR for AAC Users: A Hyperdimensional Computing (Vector Symbolic Architectures) approach. 48th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR 2025), Padua, Italy, 13-18 July 2025. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3726302.3730273
2025
Conference Publication
Dataset Discovery via Line Charts
Ji, Daomin, Luo, Hui, Bao, Zhifeng and Culpepper, J. Shane (2025). Dataset Discovery via Line Charts. IEEE. doi: 10.1109/icde65448.2025.00046
2025
Conference Publication
Examining the Impact of Transcript Variation on Podcast Search and Re-ranking
Mansour, Watheq, Culpepper, J. Shane and Mackenzie, Joel (2025). Examining the Impact of Transcript Variation on Podcast Search and Re-ranking. 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, 6–10 April 2025. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-88714-7_9
2024
Conference Publication
A fully on-disk updatable learned index
Lan, Hai, Bao, Zhifeng, Culpepper, J. Shane, Borovica-Gajic, Renata and Dong, Yu (2024). A fully on-disk updatable learned index. 2024 IEEE 40th International Conference on Data Engineering (ICDE), Utrecht, Netherlands, 13-16 May 2024. Washington, DC, United States: IEEE Computer Society. doi: 10.1109/icde60146.2024.00369
2024
Conference Publication
Enhancing human annotation: leveraging large language models and efficient batch processing
Zendel, Oleg, Culpepper, J. Shane, Scholer, Falk and Thomas, Paul (2024). Enhancing human annotation: leveraging large language models and efficient batch processing. CHIIR ’24: 2024 Conference on Human Information Interaction and Retrieval, Sheffield, United Kingdom, 10-14 March 2024. New York, NY, United States: ACM. doi: 10.1145/3627508.3638322
2024
Conference Publication
Navigating data repositories: utilizing line charts to discover relevant datasets
Ji, Daomin, Luo, Hui, Bao, Zhifeng and Culpepper, Shane (2024). Navigating data repositories: utilizing line charts to discover relevant datasets. 50th International Conference on Very Large Databases, Guangzhou, China, 26-30 August 2024. New York, NY, United States: Association for Computing Machinery. doi: 10.14778/3685800.3685857
2024
Conference Publication
Optimizing data acquisition to enhance machine learning performance
Wang, Tingting, Huang, Shixun, Bao, Zhifeng, Culpepper, J. Shane, Dedeoglu, Volkan and Arablouei, Reza (2024). Optimizing data acquisition to enhance machine learning performance. 50th International Conference on Very Large Data Bases, Guangzhou, China, 26-30 August 2024. New York, NY, United States: Association for Computing Machinery. doi: 10.14778/3648160.3648172
2023
Journal Article
Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices
Lan, Hai, Bao, Zhifeng, Culpepper, J. Shane and Borovica-Gajic, Renata (2023). Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices. Proceedings of the ACM on Management of Data, 1 (2), 1-22. doi: 10.1145/3589284
2023
Conference Publication
Facility relocation search for good: when facility exposure meets user convenience
Luo, Hui, Bao, Zhifeng, Culpepper, J. Shane, Li, Mingzhao and Zhao, Yanchang (2023). Facility relocation search for good: when facility exposure meets user convenience. ACM Web Conference 2023, Austin, TX, United States, 30 April - 4 May 2023. New York, NY, United States: ACM. doi: 10.1145/3543507.3583859
2023
Conference Publication
Entropy-based query performance prediction for neural information retrieval systems
Zendel, Oleg, Liu, Binsheng, Culpepper, J. Shane and Scholer, Falk (2023). Entropy-based query performance prediction for neural information retrieval systems. QPP++ 2023: Query Performance Prediction and Its Evaluation in New Tasks, co-located with The 45th European Conference on Information Retrieval (ECIR), Dublin, Ireland, 2 - 6 April 2023. CEUR-WS.
2022
Conference Publication
Representative routes discovery from massive trajectories
Wang, Tingting, Huang, Shixun, Bao, Zhifeng, Culpepper, J. Shane and Arablouei, Reza (2022). Representative routes discovery from massive trajectories. KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, United States, 14 - 18 August 2022. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3534678.3539079
2022
Conference Publication
Can users predict relative query effectiveness?
Zendel, Oleg, Ebrahim, Melika P., Culpepper, J. Shane, Moffat, Alistair and Scholer, Falk (2022). Can users predict relative query effectiveness?. 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Madrid, Spain, 11-15 July 2022. New York, NY USA: Assocation for Computing Machinery. doi: 10.1145/3477495.3531893
2022
Journal Article
sMARE: a new paradigm to evaluate and understand query performance prediction methods
Faggioli, Guglielmo, Zendel, Oleg, Culpepper, J. Shane, Ferro, Nicola and Scholer, Falk (2022). sMARE: a new paradigm to evaluate and understand query performance prediction methods. Information Retrieval, 25 (2), 94-122. doi: 10.1007/s10791-022-09407-w
2021
Journal Article
Let trajectories speak out the traffic bottlenecks
Luo, Hui, Bao, Zhifeng, Cong, Gao, Culpepper, J. Shane and Khoa, Nguyen Lu Dang (2021). Let trajectories speak out the traffic bottlenecks. ACM Transactions on Intelligent Systems and Technology, 13 (1) 8, 1-21. doi: 10.1145/3465058
2021
Journal Article
Topic difficulty: collection and query formulation effects
Culpepper, J. Shane, Faggioli, Guglielmo, Ferro, Nicola and Kurland, Oren (2021). Topic difficulty: collection and query formulation effects. ACM Transactions on Information Systems, 40 (1) 19, 1-36. doi: 10.1145/3470563
2021
Journal Article
Strong natural language query generation
Liu, Binsheng, Lu, Xiaolu and Culpepper, J. Shane (2021). Strong natural language query generation. Information Retrieval Journal, 24 (4-5), 322-346. doi: 10.1007/s10791-021-09395-3
2021
Journal Article
Dynamic ridesharing in peak travel periods
Luo, Hui, Bao, Zhifeng, Choudhury, Farhana M. and Culpepper, J. Shane (2021). Dynamic ridesharing in peak travel periods. IEEE Transactions on Knowledge and Data Engineering, 33 (7) 8937731, 2888-2902. doi: 10.1109/tkde.2019.2961341
2021
Conference Publication
Different keystrokes for different folks: visualizing crowdworker querying behavior
Benham, Rodger, MacKenzie, Joel, Culpepper, J. Shane and Moffat, Alistair (2021). Different keystrokes for different folks: visualizing crowdworker querying behavior. CHIIR '21: ACM SIGIR Conference on Human Information Interaction and Retrieval, Canberra, ACT Australia, 14 - 19 March 2021. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3406522.3446054
2021
Journal Article
A survey on trajectory data management, analytics, and learning
Wang, Sheng, Bao, Zhifeng, Culpepper, J. Shane and Cong, Gao (2021). A survey on trajectory data management, analytics, and learning. ACM Computing Surveys, 54 (2) 39, 1-36. doi: 10.1145/3440207
Funding
Current funding
Supervision
Availability
- Professor Shane Culpepper is:
- Available for supervision
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Available projects
-
Large Language Models for Search and Recommendation
Large Language Models such as ChatGPT offer enormous promise to users completing everyday tasks. However, these models confidently provide misinformation which can be very convincing. This project aims to explore new techniques to improve the effectiveness of LLMs.
Supervision history
Current supervision
-
Doctor Philosophy
Using Large Language Models and Knowledge Graphs to Improve Search and Recommendation
Principal Advisor
Other advisors: Dr Joel Mackenzie
-
Doctor Philosophy
Efficient Next-Generation Information Retrieval Systems
Associate Advisor
Other advisors: Dr Joel Mackenzie
Media
Enquiries
Contact Professor Shane Culpepper directly for media enquiries about:
- Generative AI
- Large Language Models
- Search and Recommendation
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