STAFF PROFILE
Shane Culpepper
Position:
Adjunct Professor
College / Portfolio:
STEM College
Email:
shane.culpepper@rmit.edu.au
Campus:
City Campus
Contact me about:
Research supervision
Associate Professor Shane Culpepper is the Director of the Centre for Information Discovery and Data Analytics and a Vice-Chancellor's Principal Research Fellow.
His current research focuses on building search systems to effectively and efficiently search massive data collections, and understanding how to measure the quality of the answers found. More broadly, he is interested in Human-driven Data Science and Analytics, which focuses on devising new approaches to transform heterogeneous data collections into knowledge. This might be accomplished through algorithms, data structures, machine learning, distributed computing, statistical modelling, or some combination of all the above. For further information, please visit the Centre Website or his personal homepage.Ph.D. University of Melbourne (2008)
Note: Supervision projects since 2004
14 PhD Completions6 PhD Current Supervisions
Information Retrieval, Algorithms and Data Structures, Machine Learning, Spatial Computing
- Luo, H.,Bao, Z.,Cong, G.,Culpepper, J.,Khoa, N. (2022). Let Trajectories Speak Out the Traffic Bottlenecks In: ACM Transactions on Intelligent Systems and Technology, 13, 1 - 21
- Faggioli, G.,Zendel, O.,Culpepper, S.,Ferro, N.,Scholer, F. (2022). sMARE: a new paradigm to evaluate and understand query performance prediction methods In: Information Retrieval Journal, 25, 94 - 122
- Culpepper, S.,Faggioli, G.,Ferro, N.,Kurland, O. (2022). Topic Difficulty: Collection and Query Formulation Effects In: ACM Transactions on Information Systems, 40, 1 - 36
- Wang, T.,Huang, S.,Bao, Z.,Culpepper, J.,Arablouei, R. (2022). Representative Routes Discovery from Massive Trajectories In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining August 2022, Washington DC, United States, 14/08/2022 - 18/08/2022
- Benham, R.,Mackenzie, J.,Culpepper, S.,Moffat, A. (2021). Different Keystrokes for Different Folks: Visualizing Crowdworker Querying Behavior In: Proceedings of the 6th ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 2021), Canberra, Australia, 14 -19 March 2021
- Liu, B.,Lu, X.,Culpepper, J. (2021). Strong natural language query generation In: Information Retrieval Journal, 24, 322 - 346
- Zendel, O.,Culpepper, S.,Scholer, F. (2021). Is Query Performance Prediction With Multiple Query Variations Harder Than Topic Performance Prediction? In: SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event Canada, 11-15 July 2021
- Faggioli, G.,Zendel, O.,Culpepper, J.,Ferro, N.,Scholer, F. (2021). An Enhanced Evaluation Framework for Query Performance Prediction In: Advances in Information Retrieval - 43rd European Conference on IR Research, Virtual, Mar 28-Apr 1 2021
- Benham, R.,Moffat, A.,Culpepper, J. (2021). Bayesian System Inference on Shallow Pools In: Advances in Information Retrieval - 43rd European Conference on IR Research, Virtual, Mar 28-Apr 1 2021
- Liu, B.,Zamani, H.,Lu, X.,Culpepper, J. (2021). Generalizing Discriminative Retrieval Models using Generative Tasks In: The Web Conference 2021, Virtual, 19-23 April 2021
- Advancing Analytical Query Processing with Urban Trajectory Data. Funded by: ARC Discovery Projects commencing in 2022 from (2022 to 2025)
- New approaches to interactive sessional search for complex tasks. Funded by: ARC Discovery Projects 2019 from (2019 to 2023)
- Efficient and Effective Cascaded Ranking for Large Scale Search. Funded by: The Amazon Research Awards (Gift / Donation) from (2019 to 2023)
- Archistar Innovation Connection Grant. Funded by: Archistar Pty Ltd Contract from (2019 to 2020)
- Optimising Efficiency and Effectiveness Trade-offs in Cascaded Learning-to-Rank. Funded by: Google Faculty Research Award 2016 onwards from (2019 to 2023)