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We invite you to the defense of the dissertation of Ing. Martin Hurta from the Department of Computer Systems, FIT VUT, which will take place on Wednesday, January 21, 2026, at 10:00 a.m. in meeting room G108. The supervisor of the dissertation entitled "Advanced Cartesian Genetic Programming for Biomedical Applications" is Prof. Lukáš Sekanina.

Martin Hurta has long been involved in the field of Cartesian genetic programming (CGP), an artificial intelligence technique inspired by natural evolution. Genetic programming was historically developed for the purpose of designing electronic circuits, but today it is often used in connection with the design of FPGA-type embedded circuits. This method belongs to a category of artificial intelligence algorithms called evolutionary algorithms. There are a number of subcategories of nature-inspired algorithms, evolutionary algorithms being one of them (others include artificial neural networks). The essence of Cartesian genetic programming is the automated design of algorithms, where, in layman's terms, we have several candidate random solutions, run and evaluate them all, select the best one, and create new solutions through random changes. By repeating these operations, the solution is gradually improved, following the model of evolution, until the result is a working program. We can then imagine the solution of Cartesian genetic programming as a graph of operations working with inputs and constants. This graph takes the form of a 2D grid of nodes, which is internally represented by a string of values. The above-mentioned changes in the connections between nodes and the functions they perform take place in the grid. Following the example of genetics, experts refer to these as solution mutations.

The scalability of evaluation, i.e., the evaluation of all candidate solutions in hundreds of thousands of iterations, is a time- and energy-consuming challenge that Martin Hurta is trying to solve in his research. Although Cartesian genetic programming is capable of proposing very interesting solutions in tasks such as classification, symbolic regression, and circuit design, its real-world application is still limited. This is particularly evident when compared to machine learning techniques such as artificial neural networks. The reason for this, apart from the computational complexity itself, is also the unfamiliarity with these procedures outside the narrow community of researchers.

In his dissertation, Martin Hurta explores possible modifications and adaptations of Cartesian genetic programming that would help improve the properties of both the algorithm itself (design time, computational requirements) and the proposed solutions (classification accuracy, ratio between accuracy and hardware requirements, explainability). He then experimentally verifies the proposed methods on problems in the field of biomedical informatics and bioinformatics, which can benefit from the explainability of the proposed solutions or the possibility of their implementation in energy-efficient wearable devices (e.g., medical monitoring systems) – something that conventional machine learning methods have difficulty with. Hurta specifically mentions his collaboration with Prof. Stephen Smith from the University of York: This project uses devices that monitor the movements of patients with Parkinson's disease, who often suffer from dyskinesia (in very layman's terms: involuntary movements), using an accelerometer and gyroscope. The goal is to automatically recognize the manifestations of dyskinesia in patients' movements and enable doctors to use this information to correctly target medication (the drug Levodopa) and its dosage. Similarly, Hurta participated in the application of Cartesian genetic programming algorithms to recognize alcohol abuse and depressive disorders from EEG, and to calculate polygenic risk scores and predict plant growth based on genetic information (here it is worth mentioning his collaboration on calculations with the Institute of Biomedical Engineering at FEKT, Wolfram Weckwert from the University of Vienna, and Dirk Walther from the Max Planck Institute of Molecular Plant Physiology). It should be added that polygenic risk scores generally allow the risk of predisposition to a negative phenomenon to be determined on the basis of multiple genes; in humans, this most often involves determining the likelihood of a specific disease. When asked to identify the field of application he is most intensively involved in and favors, Hurta's choice is clear: "Parkinson's disease is the one I've been working on the longest and is also very topical. My colleagues and I are continuing to work actively on this application, and I believe that the proposed solutions could become part of diagnostic tools in the future and thus really help those affected. In addition, it is possible to consider a number of other diseases that affect human movement in the future."

There are many potential practical applications, and according to Martin Hurta, there is a reason for this. "There are so many methods of artificial intelligence and machine learning, and practically each one has its own possible applications, a field in which it can shine. The advantage of Cartesian genetic programming is that its solution can be written as a mathematical equation. And there are areas where this is critical—often in medicine, the space industry, military technology, and so on. Thanks to the mathematical equation, experts in these fields can say, 'It may be artificial intelligence, but it has designed this specific solution, this equation, and that is what will go into our device. It won't just be some kind of black box,'" says Hurta, describing the key advantage of genetic programming. He adds: "It would be great if our classifiers were to find their way into other medical 'boxes'."

At FIT, Martin Hurta is part of the research group EvoAI Hardware, led by his supervisor, Prof. Lukáš Sekanina. "I see a doctorate as a natural step. I want to continue at the faculty. I am part of a new large project led by Dr. Vojtěch Mrázek, within which I can continue working on similar topics. I'm glad to have a 'stamp' on my progress; people are always happy to have their position secured," says Martin Hurta, commenting on his current situation and possible future with a parallel to computer games.

We wish Martin Hurta a successful defense and much success in his future research.

Would you like to improve your skills in working with AI tools? The European project VASSAL is organizing two workshops at our faculty focused on the practical and effective use of AI tools a) in administrative and IT work, b) in scientific research. The program is divided into two full-day workshops on January 15 and 16, alternating between morning (9:00 a.m.–12:00 p.m.) and afternoon (1:00 p.m.–3:00 p.m.) blocks. Both training sessions will be led by Prof. Daniel Mertens, biochemist, lecturer, and head of research groups at the German Cancer Research Center (Das Deutsche Krebsforschungszentrum) and the University of Ulm.

The first workshop focuses on the use of artificial intelligence (primarily in the sense of large language models) in administrative and IT work. Participants can expect a specific agenda of artificial intelligence applications for meeting and correspondence preparation, data and project management, and simplified reporting. The second workshop focuses on the use of LLMs in coding (vibe coding) and for scientific creativity/brainstorming and communication. The event is aimed at researchers, Ph.D. students, technical and economic staff, and generally anyone interested in the everyday use of AI tools.

Prof. Mertes' courses are based on specific practical experience and joint problem solving. The workshops are free, but registration is required.

For more information, visit the VASSAL project website.

The technology company Phonexia, which was founded in 2006 as a spin-off of the Faculty of Information Technology at Brno University of Technology, is changing owners. After twenty years of building an international position in the field of voice analytics and biometrics, it is becoming part of the portfolio of the South Korean investment fund Crescendo Equity Partners.

Phonexia is an example of how successful a technology project from Central Europe can be when it is based on cutting-edge university research. Today, it can be described as a global provider of advanced voice solutions trusted by security and intelligence agencies and the military.

Phonexia was founded in 2006 and is linked to the previous development of voice technologies and machine speech processing at FIT, specifically within the BUT Speech research group. "At that time, we decided to give our efforts a more formal and effective form for cooperation with partners. We also needed the results of our research to be in the form required by industry standards and the technology to be computationally executable on standard hardware," says co-founder Professor Jan Černocký, summarizing the motivation for founding Phonexia. The ties between the faculty and Phonexia were particularly strong in the beginning, with the company licensing and making intensive use of, for example, the phoneme recognizer developed at FIT for its early products. Cooperation with faculty research continues today. Of course, the question arises as to whether this will continue after the change in ownership structure. "We emphasized continuity; we didn't want to sell the company for parts. On the contrary, we were looking for someone who would maintain ties with faculty research and possibly even strengthen them," says one of the company's founders, Doc. Lukáš Burget, summarizing the vision for the near future.

One chapter in the life of the former faculty spin-off is coming to a close. We wish Phonexia every success in its future endeavors. And we hope that its story will be repeated by other companies that will emerge in the future from research conducted at FIT.

For more information about the company's history and the circumstances of its sale, see our press release.