The Distributed Systems and Web Communications Research Group

Coordinator: Assoc. Prof. Dr. Rareş Boian
 

The Distributed Systems and Web Communications research group focuses on theoretical and applied research grouped around Internet communications and the WWW phenomenon. Theoretical research focuses on appropriate modeling of distributed and web phenomena. An approach to web project development is proposed starting from requirements models, in the context of the model-driven development process. In this context, several theoretical, practical and methodological directions of study are considered:

  1. developing appropriate models
  2. designing tools for creating applications and / or intelligent information extraction
  3. automatic traffic analysis combined with self-adaptation mechanisms to support web browsers
  4. the development of models and tools specific to streaming media and virtual reality
    web service models
  5. semantic web

Research directions: Distributed systems, Computer networks, Web communications and related optimizations, Web service architectures, Cloud systems, Multimedia streaming, Distributed virtual environments, Virtual reality.

The Analysis and Optimization Research group

Coordinators: Prof. Dr. Kassay Gábor, Prof. Dr. Nicolae Popovici
 

The problem of equilibrium and its applications (study of the problem in terms of the existence of solutions, study of solutions in terms of stability and sensitivity, solving algorithms), monotone operators, variational inequalities, scalar, vector and multivocal optimization.

Research directions: Functional analysis, convex analysis, optimization, applications.

The Interdisciplinary Applications Based on High Performance Computing Research Group

Coordinator: Assoc. Prof. Dr. Virginia Niculescu
 

The High Performance Computing Based Interdisciplinary Applications research group operates within the Center for Modeling, Optimization and Simulation (MOS) with the aim of fundamental and applied research in various interdisciplinary fields. Using the high-performance computing infrastructure of Babeș-Bolyai University that reaches 40TFlops Rmax, the group focuses on areas such as: model optimization, simulation and visualization, analysis of large volumes of data, parallel and distributed programming, high-performance computing.

Research directions: Parallel and distributed programming, Model optimization, Simulation and Visualization, Image processing and virtual reality, Analysis of large volumes of data, Numerical and statistical methods

The Analysis of Formal Concept Research Group

Coordinator: Assoc. Prof. Dr. Christian Săcărea

 

Formal concept analysis (FCA) has become a standard method of analyzing data and extracting, processing and representing knowledge. We aim to address the following scientific topics:

  1. Fundamental research: development of the theory of the Analysis of Triadic and temporal Concepts.
  2. Applied research: FCA applications in web log analysis, Corporate Intelligence applications, the use of virtual reality and augmented reality in the visualization of conceptual structures.

Research directions: Analysis of formal concepts and applications.

The Machine Learning Research Group

Coordinator: Prof. Dr. Gabriela Czibula
 

The machine learning research group focuses on fundamental, applied and interdisciplinary research in the field of machine learning. Our research focuses on both theoretical and algorithmic contributions to machine learning (supervised, unsupervised, reinforcement learning, classification and regression models based on relational association rules, hybrid and dynamic machine learning models) and interdisciplinary applications of machine learning in various fields, such as: software engineering (search-based software engineering), bioinformatics, computational biology, natural language processing, social networks, etc. We also aim to explore new areas where machine learning-based solutions are applicable.

Research directions: Supervised, unsupervised, reinforcement learning, Hybrid and dynamic models, Relational association rules, Search-based software engineering, Machine learning applications in fields such as: software engineering, bioinformatics, computational biology, natural language processing, social networks, etc.

The Software Engineering Research Center

Coordinator: Assoc. Prof. Dr. Simona Motogna
 

The software engineering research group works in the following fields:

  • Program analysis and verification: by using formal mechanisms (Session logic, K-framework) to specify and verify different properties of programs;
  • Software quality: evaluating and estimating quality factors for large applications, their different versions and their relationship to object-oriented metrics;
  • Model-based engineering: studies of executable models and their language of description (fUML) and their impact on the software development cycle;
  • Component-based software engineering: considers software development as a network of independently developed components and addresses the following issues: component selection, component configuration based on constraints, and various optimizations.

Applied Computational Intelligence Research Center

Coordinator: Prof. Dr. Horia F. Pop
 

Research into the theoretical, applied and practical aspects of these computational paradigms inspired by fields such as biological, social and linguistic, the use of artificial intelligence techniques, especially those of computational intelligence in the industrial and commercial sectors, is why we especially aim to link computational intelligence techniques to real-world projects and applications in fields such as finance, game theory, biological sciences and medicine, bioinformatics, industry, linguistics and software engineering. Intelligent methods of data analysis, analysis of formal concepts, applications of computational intelligence in bioinformatics, application of automated training techniques in software engineering, computational linguistics and methods of quantitative and qualitative analysis of texts, intelligent management and analysis of medical data, software-based engineering on intelligent search, management and analysis of medical data, methods and applications of reinforcement learning.

Research directions:
Artificial Intelligence, Computational Intelligence, Machine Learning, Soft Computing, Real Applications.