Information Discovery Lab


Research Overview

The velocity of digital information being produced everyday continues to outpace efficient storage and processing capacities. The volume of data that must be manipulated presents unique challenges in algorithm design, and makes efficiently computing statistics, finding interesting patterns, or answering queries difficult in many practical settings. Our group focuses on developing new models to help reason about complex data representations, and discovering algorithms and data structures of both theoretical and practical interest that are capable of efficiently supporting modern data processing tasks. We are also interested in understanding how to evaluate the quality of results returned in a variety of search and data analytics scenarios, and ensure that users' information needs are satisfied.

Our specific areas of focus include text indexing, data compression, pattern search, system evaluation, and information discovery. Many computational domains that are heavily reliant on the processing of massive data sets, such as information retrieval, natural language processing, machine learning, data mining, data science, bioinformatics, and data streams benefit from this line of research.

Research Topics

  • Information Storage and Retrieval
  • Evaluating Search Quality
  • Machine-based Learning in Information Retrieval
  • Scalable Algorithm Design
  • Data Compression
  • String Processing and Indexing
  • Distributed and Parallel Computing
  • Stream Processing Algorithms


RMIT Vice-Chancellor's Senior Research Fellowship : J. S. Culpepper 2017-2020 ($598,676).

2017H1 Mozilla Research Grant : J. S. Culpepper: "Efficient and Effective Multi-Stage Retrieval in Rust." 2017 ($50,000 USD).

ARC Discovery Grant DP170102231: T. Sellis, J. S. Culpepper, C. L. A. Clarke, and J. Lin: "Trajectory Data Processing - Spatial Computing meets Information Retrieval." 2017-2019 ($416,000).

ARC Discovery Early Career Research Award DE140100275: J. S. Culpepper: "Beyond keyword search for ranked document retrieval." 2014-2016 ($392,979) + ($150,000 RMIT Support).

ARC Discovery Grant DP140101587: T. Sellis, J. S. Culpepper, N. Mamoulis, and C. S. Jensen: "Efficient and effective ad-hoc retrieval using structured and unstructured geospatial information." 2014-2016 ($422,000) + ($75,000 RMIT Support).

ARC Discovery Grant DP110101743: A. Moffat, A. Wirth, J. S. Culpepper, and A. Turpin: "Efficient and effective algorithms for searching strings in secondary storage." 2011-2013 ($360,000).

RMIT Seed Grant: J. S. Culpepper: "Novel ranking algorithms for unrestricted text indexing." 2010 ($20,000).


Can be found here.
Last Update: 23 December 2017