Research interests

Personal cognitive modeling for decision making and decision support

While considering professional decision-making, an important yet unobservable part of the decision-making is human internal context and cognitive state. Being affected by personal experience, expertise, psychological characteristics, and personal values, it frames the personal strategies and preferences in professional decision-making. Within this direction, I’m trying to develop an approach for modelling of internal operator’s cognitive state during decision-making with or without decision support system, perceiving of available information, and result in intelligent behavior.

Notable papers

  1. O.V. Kubryak, S.V. Kovalchuk, N.G. Bagdasaryan Assessment of Cognitive Behavioral Characteristics in Intelligent Systems with Predictive Ability and Computing Power // Philosophies, 2023, Vol. 8, No. 5, pp. 75. 🔗link
  2. S. Kovalchuk, A.T.S. Ireddy Prediction of Users Perceptional State for Human-Centric Decision Support Systems in Complex Domains through Implicit Cognitive State Modeling // Proceedings of the Annual Meeting of the Cognitive Science Society, 2024, Vol. 46, pp. 3257-3264. 🔗link

Human-AI and human-machine interaction in complex decision making

Interaction between modern AI agents and human operators should be considered as bidirectional adaptive process aimed toward better collaboration. Here, each side of the process can reason about the opposite one in order to achieve personal and shared goals. To improve the process we can develop advanced techniques for communication (for example, by hiring explainable AI, XAI), propose novel approaches for cognitive reasoning and shared knowledge management in AI agent about human agent state, design adaptive multi-level feedback loops in information exchange.

Notable papers

  1. S.V. Kovalchuk [et al.] Three-stage intelligent support of clinical decision making for higher trust, validity, and explainability // Journal of Biomedical Informatics, Vol. 127, 2022, pp. 104013. 🔗link 🔗arXiv
  2. O.V. Kubryak, S.V. Kovalchuk An Artificial Sensory Component in a Man–Machine System with Combined Feedback // Control Sciences, No. 6, 2024, pp. 22-31. 🔗link

Modeling and simulation of professional and domain-specific behavior and decision making

In many cases, the agents are influenced by complex environments, personal motivation, social interaction, and regulatory mechanisms. Within a complex external context of decision-making, people act like professionals doing their jobs managing problems and processes with high uncertainty by application of expertise, knowledge, and tools (including computational tools). Large-scale systems (such as healthcare system) may be become complex, with emergent phenomena being observed through social interaction and knowledge sharing. Here, I’m trying to approach on how behavior of such complex systems can be understood, explained, and optimized taking into account.

Notable papers

  1. S.V. Kovalchuk [et al.] Simulation of Patient Flow in Multiple Healthcare Units using Process and Data Mining Techniques for Model Identification // Journal of Biomedical Informatics, Vol. 82, 2018, pp. 128-142. 🔗link 🔗arXiv
  2. S.V. Kovalchuk [et al.] Towards Modeling of Information Processing Within Business-Processes of Service-Providing Organizations // Lecture Notes in Computer Science, Vol. 12137, 2020, pp. 667–675. 🔗link

Meta-modeling and model management for complex system modeling and simulation

As the complexity of a system grows the model to describe it becomes more and more complicated. As a result hybrid modeling, surrogate modeling, data-driven modeling approaches are combined within cross-domain knowledge-based reasoning and artificial intelligence to design, implement, identify and apply composite models for domain-specific tasks. How can the general approach to such a problem be developed to extend intelligent support of complex system modeling and simulation?

Notable papers

  1. S.V. Kovalchuk [et al.] A Conceptual Approach to Complex Model Management with Generalized Modelling Patterns and Evolutionary Identification // Complexity, Volume 2018, Article ID 5870987, 15 p. 🔗link
  2. S.V. Kovalchuk [et al.] Classification issues within ensemble-based simulation: application to surge floods forecasting // Stochastic Environmental Research and Risk Assessment, Vol. 31, Issue 5, 2017, pp. 1183-1197. 🔗link

Notable projects

Research grants (as a primary investigator)

Projects for industry (as a primary investigator)

Personal grants

Other notable projects (as a participant)