Quantum Systems of Informatics Group
Quantum Systems of Informatics Group was created in 2001. We work in the field of quantum information, mainly on quantum programming languages and simulations of quantum computers.
Quantum Systems of Informatics Group was created in 2001. We work in the field of quantum information, mainly on quantum programming languages and simulations of quantum computers.
The Applied Informatics Group was established in 2022 with the aim of enhancing the efficiency of commercialization of technologies developed at Institute. It supports other teams within the Institute in the area of applied informatics during the preparation and implementation of projects, as well as when transferring results to the business environment. In particular, it estimates the time and resource requirements of planned tasks and prepares software prototypes and demonstrators. The team also maintains the Institute's ICT infrastructure.
Systems Modelling and Performance Evaluation and Security Group (SMaPESG) conducts research on models of computer systems and novel methods of modelling. In the scope of interests of the Group there are mainly models of computer networks. We are engaged in simulation and analytical modelling, both queuing models and other methods. Our experience embraces using of well-established modelling packages, like NS, OmNET++ or PRISM as well as creation of advanced proprietary software working in parallel environments.
Our team specializes in Internet of Things (IoT) research, with emphasis on wireless communication and network protocols. We design and analyze the performance of network protocols, address issues related to interoperability, and the semantic description of data and operation of IoT systems. In addition, we investigate auto-configuration, energy consumption minimization, and localization in embedded devices.
We specialize in:
The Machine Learning Group specializes in the design and selection of machine learning algorithms and models for application projects, primarily those related to computer vision and time series. The team also conducts basic research in the field of machine learning methods for hyperspectral imaging, and recently has focused primarily on deep learning—specifically, issues related to “dead neurons,” continuous learning, and reinforcement learning. Originating from the Multimedia Systems Group, the team members have extensive experience in data analysis and mining (including hyperspectral images, biomedical data, 3D images, and signal data), processing methods (including statistical classifiers and deep learning architectures), as well as the technical and organizational aspects of carrying out research and implementation projects (including the process of preparing and supporting the implementation of machine learning systems for specific problems).