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
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
2021
Conference Publication
Bayesian system inference on shallow pools
Benham, Rodger, Moffat, Alistair and Culpepper, J. Shane (2021). Bayesian system inference on shallow pools. 43rd European Conference on IR Research, ECIR 2021, Virtual, 28 March - 1 April 2021. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-72240-1_17
2021
Conference Publication
Generalizing discriminative retrieval models using generative tasks
Liu, Binsheng, Zamani, Hamed, Lu, Xiaolu and Culpepper, J. Shane (2021). Generalizing discriminative retrieval models using generative tasks. 30th World Wide Web Conference (WWW), Virtual, 12-23 April 2021. New York, NY, United States: ACM. doi: 10.1145/3442381.3449863
2021
Conference Publication
Is query performance prediction with multiple query variations harder than topic performance prediction?
Zendel, Oleg, Culpepper, J. Shane and Scholer, Falk (2021). Is query performance prediction with multiple query variations harder than topic performance prediction?. 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual, 11-15 July 2021. New York, NY, United States: ACM. doi: 10.1145/3404835.3463039
2021
Conference Publication
An enhanced evaluation framework for query performance prediction
Faggioli, Guglielmo, Zendel, Oleg, Culpepper, J. Shane, Ferro, Nicola and Scholer, Falk (2021). An enhanced evaluation framework for query performance prediction. 43rd European Conference on IR Research, ECIR 2021, Virtual, 28 March - 1 April 2021. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-72113-8_8
2021
Conference Publication
Do hard topics exist? A statistical analysis
Culpepper, J. Shane, Faggioli, Guglielmo, Ferro, Nicola and Kurland, Oren (2021). Do hard topics exist? A statistical analysis. IIR 2021: 11th Italian Information Retrieval Workshop 2021, Bari, Italy, 13-15 September 2021. Aachen, Germany: Rheinisch-Westfaelische Technische Hochschule Aachen.
2020
Conference Publication
CC-News-En : A large English news corpus
Mackenzie, Joel, Benham, Rodger, Petri, Matthias, Trippas, Johanne R., Culpepper, J. Shane and Moffat, Alistair (2020). CC-News-En : A large English news corpus. CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Online, 19 - 23 October 2020. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3340531.3412762
2020
Conference Publication
Feature extraction for large-scale text collections
Gallagher, Luke, Mallia, Antonio, Culpepper, J. Shane, Suel, Torsten and Cambazoglu, B. Barla (2020). Feature extraction for large-scale text collections. 29th ACM International Conference on Information and Knowledge Management (CIKM), Virtual, 19-23 October 2020. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3340531.3412773
2020
Conference Publication
Cluster-based document retrieval with multiple queries
Bernstein, Kfir, Raiber, Fiana, Kurland, Oren and Shane Culpepper, J. (2020). Cluster-based document retrieval with multiple queries. ICTIR '20: The 2020 ACM SIGIR International Conference on the Theory of Information Retrieval, Virtual, 14-17 September 2020. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3409256.3409825
2020
Journal Article
Fewer topics? A million topics? Both?! On topics subsets in test collections
Roitero, Kevin, Culpepper, J. Shane, Sanderson, Mark, Scholer, Falk and Mizzaro, Stefano (2020). Fewer topics? A million topics? Both?! On topics subsets in test collections. Information Retrieval, 23 (1), 49-85. doi: 10.1007/s10791-019-09357-w
2020
Conference Publication
Spatial object recommendation with hints: when spatial granularity matters
Luo, Hui, Zhou, Jingbo, Bao, Zhifeng, Li, Shuangli, Culpepper, J. Shane, Ying, Haochao, Liu, Hao and Xiong, Hui (2020). Spatial object recommendation with hints: when spatial granularity matters. 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Virtual, 25-30 July 2020. New York, NY, United States: ACM. doi: 10.1145/3397271.3401090
2020
Conference Publication
Bayesian inferential risk evaluation on multiple IR systems
Benham, Rodger, Carterette, Ben, Culpepper, J. Shane and Moffat, Alistair (2020). Bayesian inferential risk evaluation on multiple IR systems. 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Virtual, 25-30 July 2020. New York, NY, United States: ACM. doi: 10.1145/3397271.3401033
2020
Conference Publication
Temporal network representation learning via historical neighborhoods aggregation
Huang, Shixun, Bao, Zhifeng, Li, Guoliang, Zhou, Yanghao and Culpepper, J. Shane (2020). Temporal network representation learning via historical neighborhoods aggregation. IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, United States, 20-24 April 2020. Los Alamitos, CA, United States: IEEE Computer Society. doi: 10.1109/icde48307.2020.00101
2020
Book Chapter
On the Pluses and Minuses of Risk
Benham, Rodger, Moffat, Alistair and Culpepper, J. Shane (2020). On the Pluses and Minuses of Risk. Information Retrieval Technology. (pp. 81-93) Cham: Springer International Publishing. doi: 10.1007/978-3-030-42835-8_8
2019
Journal Article
Boosting search performance using query variations
Benham, Rodger, Mackenzie, Joel, Moffat, Alistair and Culpepper, J. Shane (2019). Boosting search performance using query variations. ACM Transactions On Information Systems, 37 (4) 41, 1-25. doi: 10.1145/3345001
2019
Conference Publication
A comparative analysis of human and automatic query variants
Liu, Binsheng, Craswell, Nick, Lu, Xiaolu, Kurland, Oren and Culpepper, J. Shane (2019). A comparative analysis of human and automatic query variants. ACM SIGIR 9th International Conference on the Theory of Information Retrieval (ICTIR), Santa Clara, CA USA, 2-5 October 2019. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3341981.3344223
2019
Conference Publication
Relevance modeling with multiple query variations
Lu, Xiaolu, Kurland, Oren, Culpepper, J. Shane, Craswell, Nick and Rom, Ofri (2019). Relevance modeling with multiple query variations. ACM SIGIR 9th International Conference on the Theory of Information Retrieval (ICTIR), Santa Clara, CA USA, 2-5 October 2019. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3341981.3344224
2019
Journal Article
Fast Large-Scale Trajectory Clustering
Wang, Sheng, Bao, Zhifeng, Culpepper, J. Shane, Sellis, Timos and Qin, Xiaolin (2019). Fast Large-Scale Trajectory Clustering. Proceedings of the Vldb Endowment, 13 (1), 29-42. doi: 10.14778/3357377.3357380
2019
Conference Publication
Information needs, queries, and query performance prediction
Zendel, Oleg, Shtok, Anna, Raiber, Fiana, Kurland, Oren and Culpepper, J. Shane (2019). Information needs, queries, and query performance prediction. 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France, 21-25 July 2019. New York, NY, United States: ACM. doi: 10.1145/3331184.3331253
2019
Conference Publication
Accelerated query processing via similarity score prediction
Petri, Matthias, Moffat, Alistair, Mackenzie, Joel, Culpepper, J. Shane and Beck, Daniel (2019). Accelerated query processing via similarity score prediction. 42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Paris, France, 21-25 July 2019. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3331184.3331207
Funding
Current funding
Supervision
Availability
- Professor Shane Culpepper is:
- Available for supervision
Looking for a supervisor? Read our advice on how to choose a supervisor.
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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