Data and Intelligent Systems
The rapid growth of big data creates unprecedented demand and opportunities for developing powerful data and intelligent 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.
Relevant Seminars
- 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.
Siebel School Experts at Work
Big challenges. Bold solutions. See how our faculty and students are shaping artificial intelligence.
Five CS graduate students named 2027 Siebel Scholars
- Siebel School News
- November 9, 2026
Research Efforts and Groups
- Carl R. Woese Institute for Genomic Biology (IGB)
- Comp-Gen Initiative in the Carl R. Woese Institute for Genomic Biology (IGB)
- Data Analytics Subprogram in the Advanced Digital Sciences Center
- KnowEnG, an NIH Center for Excellence for Big Data to Knowledge (BD2K) in the Carl R. Woese Institute for Genomic Biology
- Midwest Big Data Hub
Faculty & Affiliate Faculty
Scientific Data Management, Data Provenance, Systems for AI
Data mining, generative models, geospatial data, data assimilation
Text Mining, Information Synthesis, Collaborative Information Behaviors, Recognizing Textual Entailment, Summarization, Evidence-Based Discovery, Meta-Analysis, Socio-Technical Systems
Cosmological Data Mining
Network analysis, scientometrics, history of science, agent-based models, community detection
Knowledge acquisition, information extraction, information integration, database, data mining, graph analytics
Distributed storage, data streaming
Conversational agents
Data Mining, Information Retrieval, Data Science, Database Management
Data Mining, Heterogeneous Learning, Rare Category Analysis, Healthcare
Information Extraction, Knowledge Base Population, Knowledge Acquisition, Text Mining, Knowledge enhanced language model, scientific discovery
AI Agents, Data analysis agents, Text-to-SQL
Many-Task Computing and Workflows, Parallel and Distributed Computing, Sustainable and Open Research Science Software
Large scale data analysis and workflows
Machine Learning Systems, Intelligence Edge
Game theory, optimization
Data security & privacy; Secure data sharing & data governance
LLM agent for scientific discovery
Large scale multi-dimensional data analysis
Multimodal Machine Learning, 2D/3D Generative AI, Vision-Language Models, Embodied AI
Data and Knowledge Management, Scientific Workflow Systems, Data Curation
Data-Centric AI, Data Attribution, Generative AI, Large Language Models, AI for Science
Database, Systems for AI, Retrieval-Augmented Generation
LLMs
Computer Vision, AI for Child Development, Health AI, Generative AI, AI Safety, Machine Learning
Temporal health informatics models, human performance, graph mining
Algorithms and Data Structures for Big Data
AI for healthcare
Causal Inference, machine unlearning, large language models, recommender systems
Network Science, Data Mining, Graph Machine Learning
Human-centered Data Science, Data Reliability, Multimodal Information Systems
Computational and Geographic Information Science; CyberGIS; Multi-Scale Geospatial Problem Solving
Community detection in large networks
LLMs, Agents
Intelligent agents, AI-human collaboration, big data analytics