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Data and Information Systems

The rapid growth of big data creates unprecedented demand and opportunities for developing powerful intelligent information systems that help people manage and extract knowledge from data.

Our faculty work on a wide range of research problems, tackling the many challenges associated with developing such intelligent systems and their applications. Research includes helping people search and find relevant data and information; mining massive amounts of heterogeneous data sets to discover actionable knowledge; optimizing the entire workflow of data access, analytics, and exploration; and analyzing large social networks and to optimize human-computer collaboration centered on data.

Our faculty work closely with industry, and many of our algorithms are used in a wide range of information system applications, especially in database and data analytics systems, data mining systems, search engines, and web information service systems.

Faculty & Affiliate Faculty

Data Provenance, Scientific Data Management, Data Citation, Workflow Management, Machine Learning

Text Mining, Information Synthesis, Collaborative Information Behaviors, Recognizing Textual Entailment, Summarization, Evidence-Based Discovery, Meta-Analysis, Socio-Technical Systems

Cosmological Data Mining

Informatics, Scientometrics, Research Evaluation

Data Mining, Database Systems, Information Retrieval, Web Search/Mining, Social Media Analytics

Natural Language Processing/Computational Linguistics, especially Semantics and Pragmatics (language use) with application to Conversational AI, Dialogue Systems, Behavior Analytics, Affective Computing

Mining Biological Text, Biological Named Entity and Relation Extraction

Data Mining, Heterogeneous Learning, Rare Category Analysis, Healthcare

Natural Language Processing, especially on Information Extraction and Knowledge Base Population, as well as its connections with Computer Vision and Natural Language Generation

Resilience and Fault-Tolerance, Many-Task Computing, Parallel and Distributed Computing, Sustainable and Open Science Software

Data and Knowledge Management, Scientific Workflow Systems, Data Curation  

Database Systems, Big Data Analytics, Approximate Computing, Machine Learning for Systems

Medical Informatics, Mobile Health

Reproducibility in Computational Science, Data Science, Policy Issues Surrounding Open Data/Code Sharing

Deep Learning for Drug Discovery, Clinical Trial Optimization, Computational Phenotyping, Clinical Predictive Modeling, Mobile Health and Health Monitoring, Tensor Factorization, and Graph Mining

Network Analysis, Behavioral Modeling, Applications of Game Theory

Data Mining, Network and Graph Mining

Computational and Geographic Information Science; CyberGIS; Multi-Scale Geospatial Problem Solving

Spatial Databases, Computational Transportation, Location-Based Services, Mobile Data Management, Connectomics

Intelligent Information Systems, Information Retrieval, Data Mining, Big Data Applications

Adjunct Faculty

Data Management, Data Mining, Database Theory, Interactive Systems, Crowdsourced Computation 

Knowledge Representation, Natural Language Processing, Machine Learning 


  • The DAIS Seminar features research talks by invited speakers, DAIS faculty, and graduate students. Speakers are announced on the DAIS mailing list (as are other items of interest to the DAIS community). It is quick and easy to subscribe to the DAIS mailing list.
  • Illinois Computer Science Speaker Series

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