The research center of Applied Analysis focuses on fundamental, applicative and interdisciplinary research in the following fields: nonlinear functional analysis, complex analysis, optimization, ordinary and partial differential equations, theoretical mechanics and mathematical modeling.
Our research concentrates on:
Fixed point and variational methods for nonlinear equations and systems;
Loewner theory and open problems of univalent functions;
Nonlinear multicriteria optimization;
The Poincare problem and periodic solutions for differential systems;
Geometry of Banach spaces and generalized metric spaces;
Nonlinear elliptic boundary value problems in Lipschitz domains and different spaces, using methods of potential theory and variational and topological techniques;
Numerical and approximation methods for nonlinear equations;
Analysis of some mathematical models from biology and medicine.
Research directions: Category theory and Homological algebra, Universal algebras and multialgebras, Group graded rings, Hopf algebra actions,Abelian groups and Modules, Representations of finite groups and finite dimensional algebras, Applications of algebra in Coding theory
Supervisor: Dr. Simona Motogna, Associate Professor
Software Engineering research group interests are focused on the following directions:
Program Analysis and Verification: formal mechanisms (such as Session Logic, K-framework) applied to specify and verify different program properties;
Software Quality: studies related to evaluation and estimation of software quality factors in large applications and in different versions, and their relation to OO metrics;
Model Driven Engineering: studies related to Executable Models and their specification language (fUML) and their impact on software development;
Component Based Software Engineering: deals with developing software as a composition of various third party components. Our studies address problems such as: component selection, constraint based configuration of components and different optimizations.
Research directions: Program Analysis and Verification, Software Quality, Model Driven Engineering, Component Based Software Engineering
The equilibrium problem and its applications (investigating the existence of solutions, stability and sensitivity of solutions, algorithms for solving the problem), monotone operators, variational inequalities, scalar, vector and set-valued optimization.
Supervisors: Dr. Radu Precup, Professor and Dr. Adrian Petruşel, Professor
The research group on Nonlinear Operators and Differential Equations (NODE) focuses on fundamental, applicative and interdisciplinary research in the field of Nonlinear Analysis and the Theory of Integral, Differential and Partial Differential Equations. Our research concentrates on:
Development of operator methods (Fixed Point Theory, Critical Point Theory, Coincidence Point Theory, Spectral Operator Theory) for equations and system of nonlinear equations;
Applications of the operator methods for the study of various integral, differential and partial differential equations and systems;
Poincare problem and periodic solutions for systems of differential equations;
Banach spaces and generalized metric spaces geometry;
The scientific activity of the research group takes into account institutional and personal international cooperation with researchers from Spain, France, Italy, Russia, Hungary, Serbia, China, Taiwan, Thailand, and so on. An important role of the research group scientific life is the research seminar on Nonlinear Operators and Differential Equations (NODE) which has activities every Thursday from 10.30 to 11.30, in room e, Mathematica building.
Development of various operator methods for the study of different nonlinear equations and systems of equations, at least from the following perspectives: existence, uniqueness, qualitative properties of the solutions;
Applications of the abstract operator methods to nonlinear integral, differential and partial differential equations;
Modelling and simulation for real-life problems from economy, biology, medicine, etc.
Wavelets-based collocation methods for integral equations and equations arising in fluid flow problems; applications of symbolic-numerical algorithms; linear approximation processes; interpolation operators for curved domains; computer aided geometric design by using non-negative normalized B-basis functions of extended Chebyshev spaces; long-time behavior of the solutions of certain stochastic PDE.
Research directions: Numerical methods, Approximation operators, Computer aided geometric design
The research group on Machine Learning (ML) focuses on fundamental, applicative and interdisciplinary research on Machine Learning. Our research concentrates on pure theoretical and algorithmic contributions to machine learning (supervised, unsupervised and reinforcement learning models, classification and regression models based on relational association rule mining, hybrid and dynamic ML models, ) as well as on interdisciplinary applications of machine learning in various domains, like: software engineering (SBSE – Search-Based Software Engineering), bioinformatics and computational biology, bioarchaeology, computer vision, natural language processing, social media, etc. We aim to explore new domains in which machine learning solutions are applicable.
Research directions: Supervised, unsupervised and reinforcement learning, Hybrid and dynamic machine learning models, Relational association rule mining, Search-based software engineering, Applications of machine learning in various domains, like: software engineering, bioinformatics and computational biology, bioarchaeology, computer vision, natural language processing, social media, etc.
Supervisor: Dr. Virginia Niculescu, Associate Professor
The “High Performance Interdisciplinary Applications” research group functions within the Center of Modeling, Optimization and Simulation (MOS) and targets fundamental research for interdisciplinary application domains. Using HPC-UBB infrastructure capable of 40TFlops Rmax, the research includes modeling and simulation of different natural phenomena, optimization, visualization, data analysis, parallel and distributed programming, big data analytics, and high performance computing.
Research directions: Distributed and parallel programming, Models optimization, Simulation and visualization, Image processing and virtual reality, Big Data Analytics, Numerical and statistical simulations