Data and Information Systems

The rapid growth of big data creates unprecedented demand and opportunities for developing powerful intelligent data and information systems that help people organize, search, analyze, and manage data, information and knowledge.

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 structuring and organizing massive data and information, helping people search and find relevant data and information; mining actionable knowledge from massive, heterogeneous typed data; optimizing the entire workflow of data access, analytics, and exploration; analyzing large social/information networks, optimizing human-computer collaboration centered on data, and exploring broad data-intensive applications.

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.

Strengths and Impact

DAIS faculty have created an active, dynamic and collaborative research environment, leading a large group of graduate students, pioneering research in the frontiers of database systems, data mining, information retrieval, Web information systems, as well as their social and scientific applications, and generating impactful results.

Many graduates are professors in major U.S. or international universities (e.g., Georgia Tech, Univ. of Michigan, UCLA, UCSD, UCSB, USC, Penn State, Purdue, Notre Dame, Univ of Virginia, Vanderbilt) and industry research labs (e.g., Google Research, IBM Research, Microsoft Research, Adobe Research), and major industry (e.g., Google, Facebook, Microsoft) and received prominent awards (e.g., NSF Career awards, KDD/SIGMOD Dissertation Awards, KDD innovation awards).

The research results generated by the DAIS groups have been highly cited and some results have been popularly used in industry and academia and collected in textbooks. DAIS faculty have been highly active in the corresponding international research communities, winning many research paper awards, major research awards, and student awards, invited to deliver keynote conference speeches and conference tutorials, and claim competition awards on multiple occasions.


  • The DAIS Seminar features invited researchers, DAIS faculty and graduate students. Seminars will be 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: brings prominent leaders and experts to campus to share their ideas and promote conversations about important challenges and topics in the discipline.

Faculty & Affiliate Faculty

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

Data Mining, Spatio-temporal Data Analysis, High-dimensional Models, Applications in Climate Science, Ecology, Recommendation Systems

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

Cosmological Data Mining

Scientometrics, Knowledge Diffusion, Data Mining, Network Analysis

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

Computational Social Science, Data Science, Machine Learning

Social Computing, Computational Social Science, Human-Centered Data Science

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

Data Mining, Text Mining, Information Networks, Database Systems, Data Analytics, Data Science Applications

Data Mining, Heterogeneous Learning, Rare Category Analysis, Healthcare

Natural Language Processing, especially on Information Extraction, Knowledge-driven Natural Language Generation, Text Mining, and Knowledge Graph Construction for Scientific Discovery

Analytics with Machine Learning, Databases with Machine Learning, Machine Learning Security, Machine Learning + Cryptography 

Many-Task Computing and Workflows, Parallel and Distributed Computing, Sustainable and Open Research Science Software

Data and Knowledge Management, Scientific Workflow Systems, Data Curation  

Robotics, Human-Computer Interaction, Immersive Computing (AR/VR), Game Design

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

Machine Learning & Data Science, Health Informatics, Network Science

Medical Informatics, Mobile Health

Data Structures

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

Scientometrics, Bibliometrics, Data Science, Statistical Inference, Graph Algorithms, Historical Linguistics

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

Machine Learning for Graphs and Databases, Foundation Models, Large Language Models, Generative AI, AI Agents

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

Machine Learning Theory and Applications, Optimization, Reinforcement Learning, Robustness, Generative AI, Large Language Models

Adjunct Faculty

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

Knowledge Representation, Natural Language Processing, Machine Learning 

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