Research and Groups

Continual Machine Learning Artificial neural networks are powerful models used in various fields because they can learn to represent useful features. When we train neural networks, we usually start with random settings and adjust them based on the data available at that moment. However, this approach is different from how humans learn. Humans continuously build on their knowledge over time; they are lifelong learners.

Medical Pathology Diagnostics Our goal is to shape the future of medical diagnostics by developing AI-based solutions. We focus on the analysis of medical imaging data, such as radiological and histopathological data (digital pathology).

Parallel and Dynamic Graph Algorithms The characteristics of today’s real-world networks make even the most basic graph processing tasks (such as computing connected components or shortest paths) challenging.

Learning in Control, Graphs and Networks The team develops neural networks that generate graphs. These solutions are oriented towards the automatic design of structures

AI for Security Amongst other things, we develop multi-level management systems to protect critical infrastructure as well as systems for securing key state services against both kinetic and cyber threats.

Algorithms in Autonomous UAVs Unmanned aerial vehicles (UAVs), also commonly known as drones, are becoming increasingly common in various aspects of life.

Intelligent Algorithms and Data Structures Algorithms, especially the ones used in machine learning, promise to aid people in making decisions.

Precision Forestry The use of remote sensing data in obtaining information about forests has a long, almost 100-year history.

Psychiatry and Computational Phenomenology Most mental disorders are highly complex and have high phenotypic variability, partially vague diagnostic criteria, and a significant overlap ratio.

Sequential Decision Making We believe that the development of techniques for effective analysis and decision-making in sequences will lead to the creation of intelligent and autonomous systems. This will translate into many practical solutions, ranging from controlling robots or autonomous vehicles to multi-step decision or deductive procedures, such as mathematical proofs.

Sustainable Computer Vision For Autonomous Machines Our solutions could potentially be used in drones as a tool supporting the protection of national parks, including animals against poaching. They allow for fast and efficient monitoring of large land areas in remote locations…

Systems Security and Data Privacy Several machine learning applications involve issues where privacy plays a special role.

Computer Graphics Computer graphics, a sub-discipline of computer science, is traditionally concerned with algorithms for digital synthesis and manipulation of visual and geometric content.

Zero-waste Machine Learning in Computer Vision Today, both science and industry rely heavily on machine learning models, predominantly artificial neural networks, that become increasingly complex and demand a significant amount of computational resources.

Neural Rendering Our team’s primary objective is to develop new representations for both NeRFs and Gaussian Splatting to address a fundamental challenge in neural rendering.

Physical Interaction Robotics In our research, we aim to challenge the current approach to robotics that avoids contact.