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Professor Shane Culpepper
Professor

Shane Culpepper

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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

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

121 works between 1995 and 2025

21 - 40 of 121 works

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

A survey on trajectory data management, analytics, and learning

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

Bayesian system inference on shallow pools

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

Generalizing discriminative retrieval models using generative tasks

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

Is query performance prediction with multiple query variations harder than topic performance prediction?

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

An enhanced evaluation framework for query performance prediction

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.

Do hard topics exist? A statistical analysis

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

CC-News-En : A large English news corpus

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

Feature extraction for large-scale text collections

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

Cluster-based document retrieval with multiple queries

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

Fewer topics? A million topics? Both?! On topics subsets in test collections

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

Spatial object recommendation with hints: when spatial granularity matters

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

Bayesian inferential risk evaluation on multiple IR systems

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

Temporal network representation learning via historical neighborhoods aggregation

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

On the Pluses and Minuses of Risk

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

Boosting search performance using query variations

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

A comparative analysis of human and automatic query variants

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

Relevance modeling with multiple query variations

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

Fast Large-Scale Trajectory Clustering

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

Information needs, queries, and query performance prediction

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

Accelerated query processing via similarity score prediction

Funding

Current funding

  • 2024 - 2026
    Scaling Disk-Resident Learned Indexes for Database Systems
    ARC Discovery Projects
    Open grant
  • 2024 - 2026
    Scaling Disk-Resident Learned Indexes For Database Systems (ARC Discovery Project Administered by RMIT)
    Royal Melbourne Institute of Technology University
    Open grant
  • 2023 - 2025
    Advancing Analytical Query Processing with Urban Trajectory Data
    ARC Discovery Projects
    Open grant
  • 2023 - 2025
    Advancing Analytical Query Processing with Urban Trajectory Data (ARC Discovery Project Administered by RMIT)
    Royal Melbourne Institute of Technology University
    Open grant

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

Need help?

For help with finding experts, story ideas and media enquiries, contact our Media team:

communications@uq.edu.au