STAFF PROFILE
Dr Shane Culpepper
Position:
Senior Research Fellow
College / Portfolio:
School of Science Cluster
School / Department:
Computer Science and Information Tech
Phone:
+61 3 9925 5224
Email:
shane.culpepper@rmit.edu.au
Campus:
Melbourne City Campus
Contact me about:
Research supervision
Dr Shane Culpepper is a Senior Lecturer at RMIT University in the School of Computer Science & Information Technology. He runs the Algorithm Design Lab (ADL), which is closely affiliated with the Information Storage and Retrieval (ISAR) Research Group ran by Professor Mark Sanderson, and the Big Data and Data Analytics (BDDA) Research Group ran by Professor Timos Sellis.
Dr Shane Culpepper's current research focuses on how to design efficient algorithms and data structures for a wide variety of information storage and retrieval problems. Broadly speaking his research interests include information retrieval, text indexing, data compression, natural language processing, experimental algorithmics, and scalable distributed / parallel computing. For further information and a current CV, please visit his personal homepage.
Ph.D. University of Melbourne (2008)
- Crane, M.,Culpepper, J.,Lin, J.,Mackenzie, J.,Trotman, A. (2017). A comparison of document-at-a-time and score-at-a-time evaluation In: Proceedings of the 10th International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, United Kingdom, 6-10 February 2017
- Choudhury, F.,Bao, Z.,Culpepper, J.,Sellis, T. (2017). Monitoring the top-m rank aggregation of spatial objects in streaming queries In: Proceedings of the 33rd IEEE International Conference on Data Engineering (ICDE 2017), San Diego, United States, 19-22 April 2017
- Wang, S.,Bao, Z.,Culpepper, J.,Sellis, T.,Sanderson, M.,Qin, X. (2017). Answering Top-k Exemplar Trajectory Queries In: Proceedings of the 33rd IEEE International Conference on Data Engineering (ICDE 2017), San Diego, CA, 19-22 April 2017
- Kim, Y.,Callan, J.,Culpepper, J.,Moffat, A. (2017). Efficient distributed selective search In: Information Retrieval, 20, 221 - 252
- Li, J.,Sellis, T.,Culpepper, J.,He, Z.,Liu, C.,Wang, J. (2017). Geo-social influence spanning maximization In: IEEE Transactions on Knowledge and Data Engineering, 29, 1653 - 1666
- Lu, X.,Moffat, A.,Culpepper, J. (2017). Can Deep Effectiveness Metrics Be Evaluated Using Shallow Judgment Pools? In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval 2017, Tokyo, Japan, 7-11 August 2017
- Chen, R.,Gallagher, L.,Blanco Gonzalez, R.,Culpepper, J. (2017). Efficient cost-aware cascade ranking in multi-stage retrieval In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval 2017, Tokyo, Japan, 7-11 August 2017
- Damessie, T.,Nghiem, J.,Scholer, F.,Culpepper, J. (2017). Gauging the Quality of Relevance Assessments using Inter-Rater Agreement In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval 2017, Tokyo, Japan, 7-11 August 2017
- Damessie, T.,Scholer, F.,Jarvelin, K.,Culpepper, J. (2016). The effect of document order and topic difficulty on assessor agreement In: Proceedings of the 6th ACM International Conference on the Theory of Information Retrieval (ICTIR 2016), Newark, United States, 12-16 September 2016
- Nghiem, J.,Ma, C.,Culpepper, J.,Sellis, T. (2016). Spatial textual Top-k search in mobile peer-to-peer networks In: Proceedings of the 27th Australasian Database Conference (ADC 2016), Sydney, Australia, 26-29 September 2016
- Trajectory Data Processing - Spatial Computing meets Information Retrieval - Administered by Swinburne University of Technology. Funded by: 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)
- Efficient and effective ad-hoc search using structured and unstructured geospatial information. Funded by: ARC Discovery Grant 2014 from (2014 to 2016)
- Beyond keyword search for ranked document retrieval. Funded by: ARC Discovery Early Career Researcher Award (DECRA) 2014 from (2014 to 2016)
- Efficient and effective algorithms for searching strings in secondary storage. Administered by University of Melbourne. Funded by: ARC Discovery Projects via other University Grant pre-2014 from (2011 to 2013)
Note: Supervision projects since 2004
3 PhD Completions8 PhD Current Supervisions
Space-efficient data structures, data compression and coding, data streams, bioinformatics, information storage and retrieval, natural language processing, statistical machine learning, and combinatorics on words.