Information Retrieval Lab
The Information Retrieval Lab conducts research in the areas of information retrieval (e.g. search engines) and information extraction. We study and develop tools that provide effective and efficient access to big, hetereogeneous data.
Current active research themes include retrieval models, distributed information retrieval, information extraction and representation, natural language processing for information retrieval, social network analysis, multimedia indexing and retrieval, document image processing, evaluation and computational epidemiology.
We publish internationally in these areas and work with government and industry partners on research and technology transfer, as well as outreach activities for primary and secondary education.
Please contact us to talk about potential new projects, collaborations, or joining us as a doctoral student or visiting researcher.
Core Information Retrieval
Christina Lioma's main research interest is in Information Retrieval (IR) covering all back-end aspects, from crawling and indexing, to ranking, evaluation, efficiency/scalability and analytics, for instance. Her recent work is focused mainly on query/document representation (e.g. using graph theory, or logic), ranking extensions (e.g. term dependence, discourse analysis), and domain-specific retrieval (e.g. rare medical diseases, scholarly (big) data, recommender systems, digital learning systems). She is also interested in the intersection of IR and Natural Language Processing (NLP). These two areas share a lot of common ground, in terms of the statistical means employed to process information, but also challenges, in the need to scale their respective approaches to larger noisier datasets, or to transpose supervised lab-based methods to naturalistic in situ environments. Her research aims to explore how much and under which conditions we can improve IR systems through more linguistically-oriented information processing components. Occasionally she researches NLP themes (without an IR angle), e.g. plagiarism detection/authorship attribution, summarisation, sentiment/opinion mining.
Ingemar J. Cox conducts research in two broad areas of information retrieval, evaluation of IR systems and peer-to-peer information retrieval. In addition, he conducts research in data mining information available from online social networks and search engine query logs for healthcare purposes.
Jakob Grue Simonsen has a background in the mathematics of computation, specifically mathematical logic, computability theory and computational complexity theory. His primary interest in information retrieval is the application of three disparate areas to IR: Mathematics, natural language processing, and human-computer interaction.
Related Research Areas
Christian Igel's main research area is Machine Learning. Currently he is particularly interested in support vector machines and other kernel-based methods; evolution strategies for single- and multi-objective optimization; stochastic neural networks and undirected graphical models; and applications of these methods.
Marcos Vaz Salles conducts research in building scalable data-driven systems. Recent work has been focused on main-memory databases, spatial data, and cloud computing, motivated by use cases in scientific simulations, games, finance, and open geodata. A challenge of particular interest is the transformation, or "cooking", of data for analysis and visualization. In a collaboration with the Danish Geodata Agency, he has explored new approaches for high-productivity creation of zoomable maps, given the recent explosion in availability of geospatial data. His work has explored how to develop a new class of declarative cartography tools.
Associate Professor Christina Lioma, Head of Lab
People in Information Retrieval
- 2014 - IEEE International Workshop on Challenges & Issues on Scholarly Big Data Discovery and Collaboration
- 2014 - Summer School on Deep Learning for Image Analysis
- 2013 - 4th International Conference on the Theory of Information Retrieval
- 2013 - IEEE Workshop on Scholarly Big Data: Challenges and Ideas