Department of Statistical Modelling and Artificial Intelligence

Department of Statistical Modeling and Artificial Intelligence (SMAIL) develops advanced computational, artificial intelligence, and statistical methods for the analysis and understanding of complex data. Our research combines computer science, machine learning, algorithmic approaches, and statistical modeling to address challenging scientific and technological problems.

We study the theoretical foundations of artificial intelligence and statistics, design innovative models, develop efficient estimation and learning algorithms, and implement new methods in scientific software. Our research is grounded in rigorous theoretical foundations and evaluated through simulation studies and real-world applications. Methodologically, we strive for scientific excellence, developing approaches that advance the state of knowledge in statistical modeling, artificial intelligence, and data-driven computation. Particular attention is devoted to principled statistical reasoning, reliable data-driven decision making, and the integration of diverse sources of information using both frequentist and Bayesian frameworks.

We have extensive experience in applying artificial intelligence methods and analyzing real-world data using statistical approaches across diverse domains. Our approaches are used in areas including social sciences and psychometrics, biomedical research, environmental and energy systems, and other fields where advanced data analysis and intelligent algorithms play a key role.

Work Groups

  • Applied Artificial Intelligence: This group primarily focuses on developing and advancing AI methods, while also applying them to real-world problems, integrating machine learning, statistical modeling, and data-driven algorithms.
  • Automated Reasoning: This group develops tools that allow computers to reason in a mathematically precise way, which is indispensable for the reliable design of automated systems. To this end, the group advances the state of the art in fields such as proof assistants, inductive logic programming, planning, formal verification, and SMT solvers.
  • Computational Psychometrics: this group focuses on developing advanced computational and statistical models for analysing ratings and measurement data in education, psychology, and the social sciences, with an emphasis on reliability, validity, and extracting detailed insights through novel estimation methods and reproducible software tools.
  • Statistical Modeling: This group focuses on developing and applying statistical models to real-world, interdisciplinary problems, while also conducting methodological research to advance statistical methodology.

Projects

Selected publications

Department Members

Core researchers

Project staff

Head: Jan Kalina