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Professor Shane Culpepper
Professor

Shane Culpepper

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Overview

Background

Shane Culpepper is Professor of Artificial Intelligence at the University of Queensland in St. Lucia, Australia. Before joining the University of Queensland in 2023, Professor Culpeper held a continuing academic position at RMIT University in Melbourne, Australia. He received his PhD in Computer Science from the University of Melbourn in 2008. His research focuses primarily on building better Search and Recommendation Systems. Over his 16 year career, Professor Culpepper has supervised 19 PhD students and co-authored more than 120 peer reviewed papers with 127 different research collaborators on problems such as algorithm efficiency and scalability, new machine learning algorithms for search and recommendation systems, and evaluating search and recommendation engine quality. Professor Culpepper is also an active member in the international research community. In the last 5 years, he 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 Princpal 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 reseach grants totalling ~$3.5 Million AUD. For more information, see his personal hoomepage.

Availability

Professor Shane Culpepper is:
Available for supervision

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

115 works between 1995 and 2024

1 - 20 of 115 works

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

A fully on-disk updatable learned index

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

Enhancing human annotation: leveraging large language models and efficient batch processing

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

Optimizing data acquisition to enhance machine learning performance

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

Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices

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

Facility relocation search for good: when facility exposure meets user convenience

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.

Entropy-based query performance prediction for neural information retrieval systems

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

Representative routes discovery from massive trajectories

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

Can users predict relative query effectiveness?

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

sMARE: a new paradigm to evaluate and understand query performance prediction methods

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. doi: 10.1145/3465058

Let trajectories speak out the traffic bottlenecks

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

Topic difficulty: collection and query formulation effects

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

Strong natural language query generation

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

Dynamic ridesharing in peak travel periods

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

Different keystrokes for different folks: visualizing crowdworker querying behavior

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

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

  • Master Philosophy

    Search and Recommendation using Large Language Models

    Principal Advisor

    Other advisors: Dr Joel Mackenzie

Media

Enquiries

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communications@uq.edu.au