STAFF PROFILE
Associate Professor Shane Culpepper
Position:
Principal Research Fellow
College / Portfolio:
School of Science Cluster
School / Department:
Computer Science and Information Tech
Phone:
99255224
Email:
shane.culpepper@rmit.edu.au
Campus:
Melbourne 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)
- Choudhury, F.,Culpepper, S.,Bao, Z.,Sellis, T. (2018). Finding the optimal location and keywords in obstructed and unobstructed space In: VLDB Journal, 27, 445 - 470
- Wang, S.,Bao, Z.,Culpepper, S.,Xie, Z.,Liu, Q.,Qin, X. (2018). Torch: A Search Engine for Trajectory Data In: Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '18), Ann Arbor, United States, 8-12 July 2018
- Huang, S.,Bao, Z.,Culpepper, S.,Zhang, P.,Zhang, B. (2018). A Linear-Time Algorithm for Finding Induced Planar Subgraphs In: Proceedings of 17th International Symposium on Experimental Algorithms (SEA 2018), L'Aquila, Italy, 27-29 June 2018
- Mohammad, H.,Xu, K.,Callan, J.,Culpepper, J. (2018). Dynamic Shard Cutoff Prediction for Selective Search In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, United States, 08-12 July 2018
- Benham, R.,Culpepper, J.,Gallagher, L.,Lu, X.,Mackenzie, J. (2018). Towards Efficient and Effective Query Variant Generation In: Proceedings of the First Biennial Conference on Design of Experimental Search and Information Retrieval Systems (DESIRES 2018), Bertinoro, Italy, 28-31 August 2018
- Ali, M.,Eusuf, S.,Abdullah, K.,Choudhury, F.,Culpepper, J.,Sellis, T. (2018). The maximum trajectory coverage query in spatial databases In: Proceedings of the VLDB Endowment, 12, 197 - 209
- Zamani, H.,Croft, W.,Culpepper, J. (2018). Neural Query Performance Prediction using Weak Supervision from Multiple Signals In: Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, Ann Arbor, MI, USA, July 08 - 12, 2018
- Gallagher, L.,Mackenzie, J.,Culpepper, J. (2018). Revisiting Spam Filtering in Web Search In: Proceedings of the 23rd Annual Australasian Document Computing Symposium, Dunedin, New Zealand, 11-12 December 2018
- Liu, B.,Lu, X.,Kurland, O.,Culpepper, J. (2018). Improving Search Effectiveness with Field-based Relevance Modeling In: Proceedings of the 23rd Annual Australasian Document Computing Symposium, Dunedin, New Zealand, 11-12 December 2018
- Oosterhuis, H.,Culpepper, J.,de Rijke, M. (2018). The Potential of Learned Index Structures for Index Compression In: Proceedings of the 23rd Annual Australasian Document Computing Symposium, Dunedin, New Zealand, December 11 - 12, 2018
- New approaches to interactive sessional search for complex tasks. Funded by: ARC Discovery Projects 2019 from (2019 to 2021)
- Efficient and Effective Cascaded Ranking for Large Scale Search. Funded by: The Amazon Research Awards (Gift / Donation) from (2019 to 2023)
- Optimising Efficiency and Effectiveness Trade-offs in Cascaded Learning-to-Rank. Funded by: Google Faculty Research Award 2016 onwards from (2019 to 2023)
- Trajectory Data Processing - Spatial Computing meets Information Retrieval - Administered by Swinburne University of Technology. Funded by: 010-ARC Discovery Projects 2017 from (2017 to 2019)
- Efficient and effective multi-stage retrieval in rust. Funded by: Mozilla Research Funding Grant 2017 from (2017 to 2018)
Note: Supervision projects since 2004
10 PhD Current Supervisions6 PhD Completions
Information Retrieval, Algorithms and Data Structures, Machine Learning, Spatial Computing