Research teams

The group focuses on ensuring privacy and security in machine learning by using cryptographic techniques (e.g., homomorphic encryption, secure multi-party computation) and differential privacy mechanisms.

The group focuses on modern machine vision methods based on deep learning. It works on various tasks, such as image classification, object detection, segmentation, image generation, and analysis using large vision-language models. Its research focuses on challenges related to knowledge adaptation and transfer, model robustness, control of generative systems, and combining information from different modalities. 

The team works on applying artificial intelligence methods to develop tools that support medical diagnostics based on image data (e.g., radiological, histopathological, etc.). One of the main areas of research is the development of an algorithm to support the histopathological diagnosis of lymphoma. Technical issues include tasks such as classification (e.g., cancer cell vs. healthy cell), segmentation (e.g., tumor area), and modeling of specific processes (e.g., cell differentiation).

The team develops AI algorithms for battlefield analysis and decision support in combat situations. We are building unique capabilities that use artificial intelligence algorithms to enhance the capabilities of BMS (battlefield management systems). We are working on advanced techniques of data analysis, machine learning, optimization, and game theory to use them to improve the command decision-making process at both the tactical and operational levels. Our goal is to achieve a statistically significantly higher average decision-making effectiveness compared to a human operator, while minimizing the risk of undesirable events. 

In order to develop solutions supporting activities in the strategic and operational areas, it is necessary to accurately map and describe the area of potential operations, with particular emphasis on non-urbanized and forested areas. The team will develop AI algorithms for battlefield analysis and analytical description in 2D and 3D space. We will build unique solutions, including digital twins, using artificial intelligence algorithms to enhance the capabilities of battlefield management systems (BMS). Advanced data analysis and machine learning techniques, combined with extensive forestry knowledge, will streamline decision-making at both the tactical and operational levels, especially in non-urban and forested areas.

The team is working on modern techniques for creating 3D images, focusing particularly on NeRF and Gaussian Splatting methods. NeRF is a method that uses artificial intelligence to create realistic 3D models from photographs. It works well, but it is slow and time-consuming. Gaussian Splatting, on the other hand, is a newer method that uses distributions (called Gaussians) to describe a scene instead of neural networks. This makes it much faster and easier to edit objects. It allows for interactive changes similar to those made on classic 3D models. The team is also researching how to combine Gaussian Splatting with generative AI models to have more control over the appearance and quality of 3D scenes. 

The team conducts research related to the analysis of existing and potential future cyber threats, monitors their development trends, and develops innovative ways to counteract them. To this end, modern technologies and algorithms, including artificial intelligence methods, are used. The solutions implemented include intelligent methods for detecting vulnerabilities in software and ICT systems, detecting disinformation, and counteracting covert communication techniques.

As part of its research on graph neural networks, the team is developing neural architectures that compress graphs into numerical vectors and synthesize graphs. These methods are used in chemistry and pharmacy to model relationships between chemical compounds and generate hypothetical chemical compounds. As part of its research on multi-agent reinforcement learning, the team is developing methods of communication in a multi-agent environment. These methods are used in the optimization of control of distributed systems, such as power grids or swarms of drones. 

The team deals with large language models (LLMs), their effective training and tuning. They are investigating the possibilities of using a multi-agent approach to solving tasks, optimized agent creation, and the use of other modalities, i.e., image and sound. An important element is ensuring mechanisms for introducing multilingualism into language models. The team is also working on extracting information from multimodal data and its semantic analysis using language models.

In our research, we want robots to not avoid touch, but to use it to better operate and understand their surroundings. We focus on combining vision and touch so that robots can learn the physics of the world, such as how hard to grip something or how to walk on a slippery surface. We use modern technologies such as machine learning, computer vision, and advanced signal processing. We implement our solutions on physical robots. 

The research team designs, builds, and tests advanced drone systems, both in terms of hardware and software. We focus on solutions that use heterogeneous drone swarms designed to perform diversified tasks. We implement modern artificial intelligence algorithms in our systems, with a particular focus on virtual and augmented reality technologies, which significantly facilitate safe and intuitive human-drone interaction. The solutions we create are aimed at developing effective, autonomous systems that support users in everyday situations, with a particular emphasis on search and rescue applications and critical infrastructure monitoring.

The team conducts research related to the application of artificial intelligence in the field of mental health, in particular to understand neuroatypical experiences (including those of people with autism) based on first-person data. The team is also working on the use of large language models in therapy, studying the interactions between agents and humans from a bioethical perspective. 

The research team conducts research on methods of automatic data analysis and content classification in the context of privacy protection and regulatory compliance. In particular, we investigate the use of natural language processing (NLP) techniques and other methods to identify sensitive information and assess the risk of its unauthorized disclosure or misuse. The group's key research areas include the development of algorithms for the precise detection of protected content, semantic analysis of documents to determine their level of confidentiality, and the adaptation of classification processes to changing regulations.

The team deals with financial market modeling, bringing together experts in finance, algorithmics, and artificial intelligence to create innovative solutions for analyzing and predicting economic behavior. Under the leadership of Prof. Piotr Sankowski and Prof. Tomasz Trzciński, it develops models using machine learning, deep learning, and reinforcement learning. The team's goal is to develop methods that better capture the dynamics and non-stationarity of markets and enable interpretable forecasts. The team combines a research approach with a practical impact on the real market.