Academically oriented

Combined study

4 years

prof. Ing. Lukáš Sekanina, Ph.D.

Chairman :
prof. Ing. Lukáš Sekanina, Ph.D.
Councillor internal :
prof. Dr. Ing. Pavel Zemčík, dr. h. c.
prof. Dr. Ing. Zbyněk Raida
prof. RNDr. Josef Šlapal, CSc.
prof. Ing. Pavel Václavek, Ph.D.
prof. Ing. Tomáš Hruška, CSc.
prof. RNDr. Alexandr Meduna, CSc.
doc. Dr. Ing. Petr Hanáček
prof. Dr. Ing. Jan Černocký
prof. Ing. Adam Herout, Ph.D.
doc. Ing. Jan Kořenek, Ph.D.
prof. Ing. Tomáš Vojnar, Ph.D.
Councillor external :
prof.,RNDr. Jiří Barnat, Ph.D.

The goal of the doctoral degree programme is to provide outstanding graduates from the master degree programme with a specialised university education of the highest level in certain fields of computer science and information technology, including especially the areas of information systems, computer-based systems and computer networks, computer graphics and multimedia, and intelligent systems. The education obtained within this degree programme also comprises a training and attestation for scientific work.

• Graduates from the doctoral study programme are trained to independently work in research, development, or management.
• They are able to solve and/or to lead teams solving advanced conceptual, research, development, or production problems in the area of contemporary information technology and its applications.
• They can be engaged to work on creative tasks, to lead research and development teams, or to work in management of companies or organizations whenever there are required abilities to work in an independent and creative way, to analyze complex problems, and to propose and realize new and original solutions. Graduates from the doctoral study programme can also teach and/or scientifically work at universities.

FIT graduates in general and FIT doctoral graduates in particular do not have a problem finding employment at scientific, pedagogical or management positions both in Czech Republic and abroad.

• FIT   graduates of the doctoral study are capable of independent scientific, research and management work in the field of Informatics, Computer Technology and Information Technologies. Graduates are ready to solve challenging conceptual, research and development problems. They can independently conduct research, development and production in the field of modern information technology.
• Typically, they work as creative workers at top scientific research workplaces, as leaders of research and development teams and in scientific and pedagogical work at universities. Graduates of this program are also employed in higher functional positions of larger institutions and companies, where the ability to work independently, analyze complex problems and design and implement new, original solutions is required.
• And, last but not least, graduates typically continue as so-called "postdoc" in their academic careers in Czech Republic or abroad.

The requirements that the doctoral students have to fulfil are given by their individual study plans, which specify the courses that they have to complete, their presupposed study visits and active participation at scientific conferences, and their minimum pedagogical activities within the bachelor and master degree programmes of the faculty. A successful completion of the doctoral studies is conditional on the following:

• The student has to pass a doctoral state examination within which he/she has to prove a deep knowledge of methodologies, theories, and their applications in accordance with the state of the art in the areas of science that are given by the courses included in his/her individual study plan and by the theme of his/her future dissertation thesis. The doctoral state examination also encompasses an evaluation of the presumed goals of the future dissertation thesis of the student, of the chosen solution method, and of the so far obtained original results.
• The student has further to prepare and defend his dissertation thesis.

The rules are determined by the directions of the dean for preparing the individual study plan of a doctoral student.  The plan is to be based on the theme of his/her future dissertation thesis and it is to be approved by the board of the branch.

• obligatory doctoral study programme Courses, the total number of courses a student has to complete and their mapping into particular semesters.
• a Research Plan Content (brief descrition of research content - focuse at the intended research area and the doctoral thesis topic
• a Research Plan ( list of research activities focused at the intended research area and the doctoral thesis topic - conferences and seminars to be attended , work to be published)
• teaching duty according to BUT study rules and regulations
• doctoral study schedule

https://www.fit.vut.cz/fit/info/smernice/sm2018-13-en.pdf

Brno university of technology provides studies for persons with health disabilities according to section 21 par. 1 e) of the Act no. 111/1998, about universities and about the change and supplementing other laws (Higher Education Act) as amended, and according to the requirements in this field arising from Government Regulation No. 274/2016 Coll., on standards for accreditation in higher education, provides services for study applicants and students with specific needs within the scope and in form corresponding with the specification stated in Annex III to Rules for allocation of a financial contribution and funding for public universities by the Ministry of Education, Youth and Sports, specifying financing additional costs of studies for students with specific needs.

Services for students with specific needs at BUT are carried out through the activities of specialized workplace - Alfons counselling center, which is a part of BUT Lifelong Learning Institute - Student counselling section.

Counselling center activities and rules for making studies accessible are guaranteed by the university through a valid Rector's directive 11/2017 concerning the status of study applicants and students with specific needs at BUT. This internal standard guarantees minimal stadards of provided services.
Services of the counselling center are offered to all study applicants and students with any and all types of health disabilities stated in the Methodological standard of the Ministry of Education, Youth and Sports.

The study programme builds on both the ongoing follow-up Master's programme in Information Technology and the new follow-up Master's programme in Information Technology and Artificial Intelligence.
Students can also, according to their needs and outside their formalized studies, take courses and trainings related to the methodology of scientific work, publishing and citation skills, ethics, pedagogy and soft skills organized by BUT or other institutions.

1. Adaptive Anomaly Detection in Network Communications Using Machine Learning

   Tutor: Ryšavý Ondřej, doc. Ing., Ph.D.

2. Addressing the limitations of Large Language Models

   Large language models (LLM) are a very powerful tool that can be used in a wide range of different applications and they are currently the main driving force of progress in the field of artificial intelligence (AI) for several reasons – e.g. because they help AI systems incorporate a wide range of general knowledge about the world, they are able to follow natural-language instructions, and perform many tasks in few-shot mode, i.e. based on a very small number of examples, thanks to their in-context learning capabilities. They are also able to integrate other modalities (e.g. image and audio).

   Despite this unprecedented progress, LLMs also suffer from several significant limitations that currently prevent their wider and safe use in many domains. These restrictions include e.g. the tendency to generate answers that have no support in the training corpus or in the input context (hallucinations), limited ability to perform multi-step reasoning and planning (and apply critical reasoning during training), but also difficulties associated with the integration of other data modalities such as a limited ability to recognize fine-grained visual concepts, etc. LLMs are also much less sample efficient than humans when acquiring new knowledge and skills, which is a significant challenge in some cases – especially for low-resource languages.

   The aim of this research is to examine such limitations and – after focusing on one or two of them – propose strategies to mitigate them. Such strategies may include e.g.:

   • Developing the ability to perform reasoning e.g. by building upon the boostrapping reasoning paradigm, adjusting the training paradigm, training on less traditional tasks (e.g. from the reinforcement learning domain), etc.;
   • New, more effective self-correction mechanisms and self-evaluation pipelines;
   • Improvement of multimodal properties of models, e.g. the ability to recognize fine visual concepts;
   • Reducing the rate of hallucinations e.g. by designing new training and fine-tuning techniques, new kind of LLM pipelines, etc.;
   • Mechanisms for reasoning during the training process, supporting the ability to better contextualize the content (e.g. understanding that the text is meant ironically, that it is of lower quality, contains false information, etc.);
   • An active training paradigm where models reason and distill during training to acquire new knowledge and skills with improved sample-efficiency;
   • …

   The application domain might be e.g. support for fact-checking and disinformation combatting, where many of these shortcomings are absolutely critical – but there, of course, is a range of other options.

   Relevant publications:

   • Srba, I., Pecher, B., Tomlein, M., Moro, R., Stefancova, E., Simko, J. and Bielikova, M., 2022, July. Monant medical misinformation dataset: Mapping articles to fact-checked claims. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2949-2959). https://dl.acm.org/doi/10.1145/3477495.3531726
   • Pikuliak, M., Srba, I., Moro, R., Hromadka, T., Smolen, T., Melisek, M., Vykopal, I., Simko, J., Podrouzek, J. and Bielikova, M., 2023. Multilingual Previously Fact-Checked Claim Retrieval. https://arxiv.org/abs/2305.07991

   The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in cooperation with researchers from highly respected research units. A combined (external) form of study and full employment at KInIT is expected.

   Tutor: Gregor Michal, doc. Ing., Ph.D.

3. Advanced algorithms of Video, Image, and Signal processing

   The topic concerns algorithms of image, video, and/or signal processing. Its main goal is to research and in-depth analyze existing algorithms and discover new ones so that they have desirable features and so that they are possible to efficiently implement. Such efficient implementation can be but does not necessarily have to be part of the work but it is important to prepare the algorithms so that they can be efficiently implemented e.g. in CPU, in CPU with acceleration through SSE instructions, in embeded systems, even in combination with FPGA, in Intel Xeon PHI, in extremely low power systems, or in other environments. It is possible to exploit algorithms of artificial intelligence, such as neural networks, especially CNNs The application possibilities of the algorithms are also important and the application can be but does not have to be part of the work. The algorithms/applications of interest include:

   • recognition of scene contents, events, and general semantics of video sequences (such as identification of traffic situations, identification in scenes in moview, action identification, etc.),
   • classification of video sequences using machine learning (AI)through deep convolution networks neural network or similar approaches (e.g. for industrial quality inspection, object of scene characteristics search, etc.), possibly in combination with object tracking in video using modern methods, 
   • parallel analysis of video and signal (e.g. for detection of coincidence of occurrence of object in video and characteristic signal shape in surveillance applications), fusion of video and sognals,
   • modern algorithms of video, image, and/or signal exploiting "client/server" or "cloud" approaches suitable e.g. for mobile technology and/or embedded systems,
   • algorithms of video compression and analysis through frequency or wavelet transforms or similar methods...

   After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

   Collaboration on grant projects, such as TACR, MPO, H2020, ECSEL (possible employment or scholarship).

   Tutor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

4. Advanced Methods of Computational Photography

   The project is concerned with advanced methods of computational photography. The aim is to research new computational photography methods. Our interest is on HDR image and video processing, color-to-grayscale conversions, spectral imaging, generative AI and others.

   • Further information: http://cadik.posvete.cz/tmo/
   • Contact: http://cadik.posvete.cz/
   • Cooperation and research visits with leading research labs are possible (Adobe Research, USA, MPII Saarbrücken, Germany, Disney Research Zurich, Switzerland, INRIA Bordeaux, France)

   Tutor: Čadík Martin, doc. Ing., Ph.D.

5. Advanced Rendering Methods

   The project is concerned with advanced rendering and global illumination methods. The aim is to research new photorealistic (physically accurate) as well as non-photorealistic (NPR) simulations of interaction of light with the 3D scene. Cooperation and research visits with leading research labs are possible (Adobe, USA, MPII Saarbrücken, Německo, Disney Curych, Švýcarsko, INRIA Bordeaux, Francie).
   
   

   • More information: http://cadik.posvete.cz/

   Tutor: Čadík Martin, doc. Ing., Ph.D.

6. Analysis of attacks on wireless networks

   The dissertation focuses on the security of wireless local area networks. The student should become familiar with selected wireless networks and their security as part of the solution. This work aims to study the theory of wireless networks, their properties, and possibilities of attacks, test the basic types of attacks, design new protection methods, conduct experiments, evaluate the results, and propose the direction of further research.

   Participation in relevant international conferences and publication in scientific journals are expected.

   
   Co-supervised by Dr. Kamil Malinka.

   Tutor: Hanáček Petr, doc. Dr. Ing.

7. Analysis of programs with dynamic data structures

   Tutor: Rogalewicz Adam, doc. Mgr., Ph.D.

8. Assessment of mental stress, anxiety and depression from analysis of brain signals

   Problem Statement: Mental stress, anxiety and depression are  mental health conditions that often occur together. In such a case, the person is stressed and is not able to control the worry, and it correspondingly affects his/her social and occupational activities. Hence, proper assessment and diagnosis for mental stress, anxiety and depression is required in order for a person to effectively keep taking part in his/her normal daily tasks and activities.

   Issues with Current Solutions: Unfortunately, conventional assessment and diagnostic measures are subjective in nature and are used only when the symptoms are already evident due to advanced stages of mental stress, anxiety and depression. However, mental stress, anxiety and depression do not occur overnight, rather it is a long process. Hence, detection of symptoms is required at early stages of mental stress, anxiety and depression because that may result in a cure or at least it will delay the onset of serious mental health issues associated with them.

   Challenges: Unlike other diseases where the symptoms like fever and cough allow people to seek help, symptoms at early stages of mental stress and anxiety are not easily identifiable. Hence, the brain needs to be continuously monitored for any sign of change or deterioration in order to detect the symptoms at early stage.

   Solution: The solution lies in the development of an objective and quantitative method that can detect mental stress, anxiety and depression at an early stage. Perception of mental stress, anxiety and depression originates in the brain; therefore, this research investigates the neurophysiological features extracted from brain electroencephalogram (EEG) signal to measure mental stress, anxiety and depression at early stage. This will require development of method for extraction of features as well as pattern recognition approach to provide a solution. The EEG dataset is already available for this project.

   Few Words About Supervision: I have extensive experience of working in the field of neuro-signal and neuroimage processing and I am currently head of a research group in this area. This is a multidisciplinary project and it will involve working with clinicians. However, the core of the project is related to IT in terms of development of a new method. Please feel free to contact me at malik@fit.vutbr.cz

   Tutor: Malik Aamir Saeed, prof., Ph.D.

9. Automated Complexity Analysis of programs with (not only) Complex Data Structures

   Tutor: Rogalewicz Adam, doc. Mgr., Ph.D.

10. Computational Musicology: Theory, Models, Methods, and Applications

    Investigate computational musicology and computational models used in this scientific area, including grammars and automata. Study properties of these models, such as the power and descriptional complexity. Concentrate on the power of these models. Publish the results of this study at the highest possible level, including prestigeous international journals. Design new methods of making computer music based upon these models. Apply the achieved methods in music to classify or create selected music passages. Make implementations of the achieved applications. Evaluate them. Compare them against already existing implementations. Publish all  the achieved results at the highest possible level, including prestigeous international journals.

    Tutor: Meduna Alexandr, prof. RNDr., CSc.

11. Computer-aided Creativity

    Cílem disertační práce je výzkum v oblasti tzv. generativní umělé inteligence - ať už se jedná o difusní a adversiální modely pro generování videa, generativní textové modely pro vytváření příběhů, automatické generování počítačového kódu, hudby, reprezentaci znalostí fyziky a chemie a podporu vědecké kreativity, případně kombinace všech těchto přístupů. Práce se zaměří na řešení problémů interakce člověka s generovanými mezivýsledky, přirozeného označování jednotlivých částí a konceptů tak, aby bylo možné na průběžné výsledky navazovat, a na vývoj metod úpravy datových sad a postupů učení, aby bylo možné řešit společenské problémy, spojené s vytvářenými kreativními modely - otázky spravedlivosti modelů, předpojatosti a začlenění konceptů tzv. zodpovědné umělé inteligence.

    Tutor: Smrž Pavel, doc. RNDr., Ph.D.

12. Conversational Agents Combining Structural Knowledge and Learning from Text

    Konverzační agenti se pomalu stávají běžnou součástí rozhraní pro (prvotní) komunikaci se zákazníkem a odpovídání na jeho otázky. Výzkum v oblasti počítačového zpracování přirozeného jazyka se zaměřuje na vytvoření automatické klasifikace prvotní komunikace a, zejména, otázky uživatele, do předem daných tříd, k nimž existují konkrétní texty. Není však uspokojivě vyřešeno rozšiřování "znalostí" komunikačních agentů při aktualizaci strukturovaných dat, případně při přidání dalších textových materiálů.
    
    Cílem disertační práce je rozvinout existující přístupy využívající obrovské kolekce neanotovaných textových dat a způsoby kombinování strukturované a nestrukturované znalosti a optimalizace procesů při rozšiřování funkcionality stávajících i nových konverzačních agentů. Součástí práce bude i aplikace zkoumaných metod v rámci evropských projektů, na jejichž řešení se školitel podílí.

    Tutor: Smrž Pavel, doc. RNDr., Ph.D.

13. Cybersecurity aspects of the Internet of Things

    The dissertation focuses on the security of IoT systems. This work aims to study the theory of IoT systems, their properties, and possibilities of attacks, test the basic types of attacks, design a new method of protection, conduct experiments, evaluate results, and design further research.

    Participation in relevant international conferences and publication in scientific journals are expected.

    Co-supervised by Dr. Kamil Malinka.

    Tutor: Hanáček Petr, doc. Dr. Ing.

14. Decentralized Finance and System Security

    First, this thesis aims to investigate the current state-of-the-art decentralized finance with an emphasis on system security and privacy. The examples of considered applications would involve watchtowers, and various market makers, such as liquidity swaps, decentralized sets and exchanges, lending platforms, balancers, etc. The thesis will propose novel decentralized finance approaches and empirically evaluate the results. The focus will be put on the security and privacy analysis of proposed approaches.

    Tutor: Homoliak Ivan, doc. Ing., Ph.D.

15. Development of neuromarker(s) for assessment of alcohol addiction

    Problem Statement: Alcohol addiction is a chronic and complex brain disorder causing devastating individual and social problems. Additionally, alcohol causes 3.3 million deaths a year worldwide, close to 6% of all deaths. Many of these deaths are associated with alcohol addiction. Therefore, it's important to look into methods for the diagnosis as well as the treatment of alcohol addiction.

    Issues with Current Solutions: Conventionally, screening and assessment of alcohol-related problems are mainly based on self-test reports. However, the accuracy of self-test reports has been questioned, especially for heavy drinkers, because the self-test reports may misguide the diagnosis due to the patient's memory loss (the patients cannot measure their alcohol consumption) and/ or dishonest behavior. Therefore, this research proposes to develop an objective and quantitative method for the detection of alcohol addiction.

    Challenges: As alcohol addiction results in changes in brain dynamics, hence, it is vital to investigate and develop a method based on brain activity. However, the main challenge in developing such an objective and quantitative method lies in its implementation for screening in smaller clinical setups. This limits the investigation to electroencephalogram (EEG) which is low cost, highly mobile and has good temporal resolution. Other modalities like MRI, PET etc are not feasible to be employed in smaller clinical settings.

    Solution: With current innovations in brain EEG signals, the brain pathways involved in addiction can be investigated. In the last few decades, EEG research has been used to understand the complex underlying processes associated with the pathophysiology of addiction. Interpreting such processes using brain networks using EEG can not only help in diagnosing addiction but also assist in treating addiction. This research aims to develop neuromarker(s) based on brain network interpretation using EEG. The neuromarker will involve the features extraction and corresponding development of the machine learning model.

    Few Words About Supervision: I have recently moved to FIT at Brno University of Technology. I have decade long experience of working in the field of neuro-signal and neuroimage processing and I am currently in the process of setting up a research group in this area. This is a multidisciplinary project and it will involve working with clinicians. However, the core of the project is related to IT in terms of development of a new method. Please feel free to contact me at malik@fit.vutbr.cz

    Tutor: Malik Aamir Saeed, prof., Ph.D.

16. Discontinuous Computation: Theory, Models, Methods, Applications

    Starting from the entire body of knowledge concerning formal models of discontinuous computation, this project will proceed towards the development of new versions of these models, which reflect the current discontinuous computation in a more sophisticated and adequate way. Properties of these newly developed systems will be studied in detail. Their applications will be discussed an implemented all scientific areas that involve discontinuous computation, such as in bioinformatics.
    
    The first results will be published in Acta Informatica in 2025.

    Tutor: Meduna Alexandr, prof. RNDr., CSc.

17. Efficient finite automata for automated reasoning

    We will develop techniques for working with finite automata with applications in automatic reasoning, verification and decision procedures for logics.
    Although automata an old and fundamental concept of computer science with a huge number of applications, and very well understood at a basic theoretical level, techniques for their use in practice are a very rich and lively field of research.

    The field has very strong practical motivation in areas such as verification and testing, regular pattern search, artificial intelligence and automatic inference, decision procedures for logics and SMT-solving, system design and analysis, and automated synthesis.
    For example, the most efficient algorithms for regular pattern search are based on automata, but it is still not clear how to generalize these algorithms to backreferences, repetition counters, or so-called look-arounds. Deciding logics over strings, aka string-solving, has applications in verification of programs with strings, especially in security analysis of web applications (susceptibility to SQL-injection, cross-site-scripting attacks), analysis of cloud access policies, or of models of critical systems in avionics. An open problem is the efficient implementation of automatic decision procedures for integer arithmetic and other logics. Regular model-checking is an automatic method applicable to the verification of a wide range of systems with infinite state space, such as programs with dynamic data structures or communication protocols. Automated techniques can be used to automatically synthesize programs such as communication interfaces and drivers.

    In these and other areas, there are a number of deep theoretical questions about decidability and problem complexity.
    For example, how to model back-reference using finite automata? What is decidable about programs that manipulate strings, such as web applications, or programs with dynamic data structures? At what cost?

    Practical questions concern the efficient implementation of existing automata algorithms and theoretical knowledge.
    How to avoid state explosion in complex automata manipulations? How to represent them compactly, using techniques of minimization, syntactic extensions, abstraction, approximation?
    How to work efficiently with compact representations of automata, such as alternating automata, symbolic automata, automata with counters and registers? What are the theoretical properties of these extensions? How to efficiently implement the developed techniques so that they really work on concrete practical problems?

    In addition to Doc. Holík, part of the VeriFIT research group, Dr. Lengál, Doc. Rogalewicz, Doc. Češka, Prof. Vojnar. The group reaches the top international level, with hundreds of publications in the most prestigious forums, dozens of software tools, numerous international awards, and intensive international cooperation with prestigious research institutions (Microsofr research, Redmond; Academia Sinica, Taipei, Uppsala Univerisy, Univeristy of Braunschweig, University of Edinburgh, Univeristy of Kaserslautern, University of Paris, Université Grenoble Alpes, Chinese Academy of Sciences).
    We collaborate with industry (Red Hat, Honeywell). Our PhD graduates thus have opportunities to obtain interesting specialized positions in industry or pursue careers in academia.
    The group has been successful in obtaining grants, so PhD students can be rewarded financially.

    Tutor: Holík Lukáš, doc. Mgr., Ph.D.

18. Efficient Technology for Dealing with Logics and Automata (Not Only) for Formal Analysis and Verification -- co-supervised by dr. O. Lengal

    Different types of logics and automata are among the most fundamental objects studied and applied in computer science for decades. Nevertheless, there are many unsatisfactorily solved problems in this area, and new, exciting problems related to ever new applications of logics and automata are constantly emerging (e.g., in the formal verification of finite and infinite state systems with various advanced control or data structures, in decision procedures, in program or hardware synthesis, in quantum program verification, or even in methods for efficient search in various types of data or network traffic).

    The subject of the dissertation will primarily be the development of the state of the art in efficient work with various logics (e.g. over pointer structures, strings, various arithmetic, temporal logics, etc.). To this end, approaches based on different types of decision diagrams, automata, but also e.g. approaches based on the existence of a model of bounded size or on efficient reductions between different types of logical theories will be investigated. In connection with this, methods for efficient work with decision diagrams and different types of automata (automata over words, trees, infinite words, automata with counters, etc.) will be developed. The work will include theoretical research as well as prototype implementation of the proposed techniques and their experimental evaluation.

    The work will be solved in collaboration with the VeriFIT team working on the development of techniques for working with logics and automata and their applications at FIT BUT. In particular, Dr. O. Lengal will act as a specialist trainer. After the completion of Dr. Lengal's habilitation, it is expected that he will move to the role of principal supervisor, while T. Vojnar may continue to remain in the role of specialist supervisor. There is also the possibility of close cooperation with various foreign partners of VeriFIT: Academia Sinica, Taiwan (Prof. Y.- F. Chen); TU Vienna, Austria (Assoc. F. Zuleger); LSV, ENS Paris-Saclay (Assoc. M. Sighireanu); IRIF, Paris, France (Assoc. A. Bouajjani, Assoc. P. Habermehl, Assoc. C. Enea), Verimag, Grenoble, France (Assoc. R. Iosif); Uppsala University, Sweden (prof. P.A. Abdulla, prof. B. Jonsson); or School of Informatics, University of Edinburgh, UK (prof. R. Mayr).

    Within the framework of the topic, the student can also actively participate in various grant projects, such as. Representation of Boolean Functions using Adaptive Data Structure", GA23-06506S "AIDE: Advanced Analysis and Verification for Advanced Software", the newly accepted GA project 25-18318S "QUAK: Quantum Program Analysis using Automata Toolkit", or the Horizon Europe project SEP-210979090 "VASSAL: Verification and Analysis for Safety and Security of Applications in Life".

    Tutor: Vojnar Tomáš, prof. Ing., Ph.D.

19. Embedded Systems for Video/Signal Processing

    The topic focuses embedded image, video and/or signal processing. Its main goal is to research capabilities of "smart" and "small" units that have such features that allow for their applications requiring smyll, hidden, distributed, low power, mechanically or climatically stressed systems suitable of processing of some signal input. Exploitation of such systems is perspective and wide and also client/server and/or cloud systems. The units themselves can be based on CPU/DSP/GPU, programmable hardware, or their combination. Smart cameras can be considered as well. Applications of interest include:

    • classification of images or objects using machine learning (AI) using traditional methods or through deep convolution networks neural network or similar approaches (e.g. for industrial quality inspection, etc.),
    • parallel analysis of signal(s) and video (e.g. for robust detection of occurrence of object in industrial or surveillance applications),
    • modern algorithms of video, image, and/or signal exploiting "client/server" or "cloud" (with focus on the technlogy) suitable e.g. for mobile technology and/or embedded systems,
    • other similar topics can be individually consulted and considered.

    A possibility exists in collaboration on grant projects, especially the newly submitted TAČR, H2020, ECSEL ones (potentially employment or scholarship possible).

    Tutor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

20. Energy-aware Embedded Inteligence

    Cílem disertační práce je výzkum modelů vestavěné inteligence, která explicitně pracuje s energetickou náročností konkrétních operací a optimalizuje svoji činnost na základě konkrétních omezení na straně jednotlivých zařízení, případně celého systému. Součástí bude i realizace vybraných modelů na vhodném typu hardware, který bude možné využít v mezinárodních projektech, na jejichž řešení se vedoucí podílí.

    Tutor: Smrž Pavel, doc. RNDr., Ph.D.

21. Explainable Artificial Intelligence

    Použití některých metod strojového učení, například v poslední době populárních hlubokých neuronových sítí, přináší problémy architektury tzv. černé skříňky, která sice může v některých případech správně rozhodovat, ale není možné snadno interpretovat způsob rozhodování, ověřovat, v jakém kontextu jsou závěry ještě věrohodné a nakolik mohou vést drobné změny vstupu ke zcela jiným závěrům.
    
    Cílem disertační práce je rozvinout existující přístupy k měření "dokazatelně správných" modelů umělých neuronových sítí a propojit je s technikami generování konfliktních (adversarial) příkladů, aby bylo možné kontrolovat a revidovat existující řešení, využívaná v praxi. Součástí práce bude i aplikace zkoumaných metod v rámci evropských projektů, na jejichž řešení se školitel podílí.

    Tutor: Smrž Pavel, doc. RNDr., Ph.D.

22. Formal Models of Distributed Computation

    Starting from the entire body of knowledge concerning formal systems of distributed computation, this project will proceed towards the development of new versions of these systems, which reflect the current distributed computation in a more sophisticated and adequate way. Properties of these newly developed systems will be studied in detail. Their applications will be primarily discussed an implemented in bioinformatics and language translators.
    
    The first results will be published in Acta Informatica in 2025.

    Tutor: Meduna Alexandr, prof. RNDr., CSc.

23. Formal Models of Parallel Computation

    Starting from the entire body of knowledge concerning formal systems of parallel computation, this project will proceed towards the development of new versions of these systems, which reflect the current parallel computation in a more sophisticated and adequate way. Properties of these newly developed systems will be studied in detail. Their applications will be primarily discussed an implemented in bioinformatics and language translators.

     

    The first results will be published in Acta Informatica in 2025.

    Tutor: Meduna Alexandr, prof. RNDr., CSc.

24. Generative AI and Image Processing using Neural Networks

    The project is concerned with advanced methods of image processing and generative AI. The aim is to research new methods using machine learning, in particular deep neural networks.

    • Contact: http://cadik.posvete.cz/
    • Cooperation and research visits with leading research labs are possible (Adobe Research, USA, MPII Saarbrücken, Germany, Disney Research Zurich, Switzerland, INRIA Bordeaux, France)

    Tutor: Čadík Martin, doc. Ing., Ph.D.

25. Genetic programming for interpretable machine learning

    Tutor: Sekanina Lukáš, prof. Ing., Ph.D.

26. Genetic programming utilizing machine learning

    Tutor: Sekanina Lukáš, prof. Ing., Ph.D.

27. Handling unseen domains in information extraction from speech

    Machine learning relies on training which covers processing domains that are expected during the machine application. Subsequently, accuracy of the processing is typically severely impaired when encountering domain not covered during the training. The proposed research will study techniques for handling this inherent machine learning problem. 

    Tutor: Heřmanský Hynek, prof. Ing., Dr. Eng.

28. Hardware Trojans Protection Using Digital Circuits Authentisation

    Zvyšující se náklady na výrobu čipů a tlak na technologický vývoj spočívající zejména v neustálém zmenšování prvků vedou ve stále větší míře a u většího počtu producentů k přesunu výroby do levnějších lokalit, zpravidla k externím subjektům. Jen málokterý výrobce si může dovolit mít vlastní výrobu polovodičů. Odvrácenou stranou úspor prostřednictvím outsourcingu jsou zvýšená rizika modifikací návrhu s cílem zajistit přístup (k datům, k řízení), vypnutí či možnosti ovlivnění funkce cizích vyrobených čipů nasazených do aplikací, aniž by to zákazník poznal. Již jsou známy případy úspěšného využití takových technik. V této souvislosti se mluví o tzv. hardwarových trojských koních. Vyvíjí se proto techniky detekce takových modifikací, případně obrany proti nim. Jednou z možností detekce hardwarových trojských koní je například tzv. IP watermarking.

    Cílem práce bude

    • experimentovat s technikami nekonvenční implementace číslicových obvodů, zejména na úrovni hradel a v takovém provedení, aby mohly být integrovány na křemíkové čipy,
    • nalézt vhodná řešení a aplikace, kde by využití nekonvenční implementace vedlo ke zvýšení odolnosti číslicových obvodů vůči záměrným modifikacím třetí stranou,
    • nalézt techniky umožňující detekci takových modifikací obvodu s využitím nekonvenční implementace číslicových prvků.

    Tutor: Růžička Richard, doc. Ing., Ph.D., MBA

29. Hardware-aware neural architecture search

    Tutor: Sekanina Lukáš, prof. Ing., Ph.D.

30. Human-AI collaboration in dataset creation

    The models created in machine learning can only be as good as the data on which they are trained. Researchers and practitioners thus strive to provide their training processes with the best data possible. It is not uncommon to spend much human effort in achieving upfront good general data quality (e.g. through annotation). Yet sometimes, upfront dataset preparation cannot be done properly, sufficiently or at all.

    In such cases the solutions, colloquially denoted as human-in-the-loop solutions, employ the human effort in improving the machine learned models through actions taken during the training process and/or during the deployment of the models (e.g. user feedback on automated translations). They are particularly useful for surgical improvements of training data through identification and resolving of border cases.

    Human-in-the-loop approaches draw from a wide palette of techniques, including active and interactive learning, human computation, and crowdsourcing (also with motivation schemes of gamification and serious games). With recent emergence of large language models (LLM), the original human-in-the-loop techniques can be further boosted to create extensive synthetic training sets with comparatively small human effort.

    The domains of application of human-in-the-loop are predominantly those with a lot of heterogeneity and volatility of data. Such domains include online false information detection, online information spreading (including spreading of narratives or memes), auditing of social media algorithms and their tendencies for disinformation spreading, support of manual/automated fact-checking and more.

    Relevant publications:

    • Cegin, J., Simko, J. and Brusilovsky, P., 2023. ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing https://arxiv.org/pdf/2305.12947.pdf
    • J. Šimko and M. Bieliková. Semantic Acquisition Games: Harnessing Manpower for Creating Semantics. 1st Edition. Springer Int. Publ. Switzerland. 150 p. https://link.springer.com/book/10.1007/978-3-319-06115-3

    The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in cooperation with industrial partners or researchers from highly respected research units. A combined (external) form of study and full employment at KInIT is expected.

    Tutor: Šimko Jakub, doc. Ing., PhD.

31. Image and video quality assessment

    The project deals with image and video quality assessment metrics (IQM). The aim is to explore new ways how to incorporate human visual system properties into IQM. In particular, we will consider perception of HDR images, and utilization of additional knowledge (in form of metadata, 3D information, etc.) about the tested scenes using machine learning (e.g. neural networks).

    • Further information: http://cadik.posvete.cz/iqm/
    • Contact: http://cadik.posvete.cz/
    • Cooperation and research visits with leading research labs are possible (MPII Saarbrücken, Germany, Disney Research Zurich, Switzerland)

    Tutor: Čadík Martin, doc. Ing., Ph.D.

32. Improving natural language processing

    The recent development of large language models (LLMs) shows the potential of deep learning and artificial neural networks for many natural language processing (NLP) tasks. Advances in their automation have a significant impact on a plethora of innovative applications affecting everyday life.

    Although large-scale language models have been successfully used to solve a large number of tasks, several research challenges remain. These may be related with individual natural language processing tasks, application domains, or the languages themselves. In addition, new challenges stemming from the nature of large language models and the so-called black-box nature of neural network-based models.

    Further research and exploration of related phenomena is needed, with special attention to the problem of trustworthiness in NLP or new learning paradigms addressing the problem of low availability of resources needed for learning (low-resource NLP).

    Interesting research challenges that can be addressed within the topic include:

    • Large language models and their properties (e.g., hallucination understanding)
    • Trustworthy NLP (e.g., bias mitigation, explainability of models)
    • Adapting large language models to a specific context and task (e.g. via PEFT, RAG)
    • Advanced learning techniques (e.g., transfer learning, multilingual learning)
    • Domain-specific information extraction and text classification (e.g., novel methods for sentiment analysis, improving conversation quality in chatbots)

    Relevant publications:

    • Pikuliak, M., Hrčková, A., Oreško, Š., Šimko, M. Women Are Beautiful, Men Are Leaders: Gender Stereotypes in Machine Translation and Language Modeling. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 3060–3083, ACL, 2024
      https://doi.org/10.18653/v1/2024.findings-emnlp.173
      
    • Pikuliak, M., et al. SlovakBERT: Slovak Masked Language Model. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 7156–7168, ACL, 2022
      http://dx.doi.org/10.18653/v1/2022.findings-emnlp.530

    The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in cooperation with industrial partners or researchers from highly respected research units from abroad. A combined (external) form of study and full employment at KInIT is expected.

    Tutor: Šimko Marián, doc. Ing., Ph.D.

33. Improving performance of Large Language Models for downstream tasks

    Large language models (LLMs) are increasingly being used for a wide range of downstream tasks where they often show a good performance in zero/few-shot settings compared to specialized fine-tuned models, especially for tasks in which the LLMs can tap into the vast knowledge learned by them during the pre-training. However, they lag behind the specialized fine-tuned models in tasks requiring a more specific domain knowledge and adaptation. Additionally, they often suffer from problems such as hallucinations, i.e., outputting coherent, but factually false or nonsensical answers; or generating text laden with biases propagated from pre-training data. Various approaches have recently been proposed to address these issues, such as improved prompting strategies including in-context learning, retrieval-augmented generation or adapting the LLMs through efficient fine-tuning.

    Each of these approaches (or combination thereof) presents opportunities for new discoveries. Orthogonal to this, there are multiple important factors of models like their level of alignment with human values, their robustness, explainability or interpretability and advances in this regard are welcome as well (generally in AI and particularly in the mentioned approaches).

    There are many downstream tasks, where research of the LLM adaptation methods can be applied. These include (but are not limited to) false information (disinformation) detection, credibility signals detection, auditing of social media algorithms and their tendencies for disinformation spreading, and support of manual/automated fact-checking.

    Relevant publications:

    • Macko, D., Moro, R., Uchendu, A., Lucas, J.S., Yamashita, M., Pikuliak, M., Srba, I., Le, T., Lee, D., Simko, J. and Bielikova, M., 2023. MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 9960–9987, Singapore. Association for Computational Linguistics. https://aclanthology.org/2023.emnlp-main.616/
    • Vykopal, I., Pikuliak, M., Srba, I., Moro, R., Macko, D., and Bielikova, M., 2023. 2024. Disinformation Capabilities of Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14830–14847, Bangkok, Thailand. Association for Computational Linguistics. https://aclanthology.org/2024.acl-long.793/

    The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in collaboration with industrial partners or researchers from highly respected research units involved in international projects. A combined (external) form of study and full employment at KInIT is expected.

    Tutor: Bieliková Mária, prof. Ing., Ph.D.

34. Interoperability of Blockchains

    First, this thesis aims to investigate the current state-of-the-art of the interoperability of various decentralized ledgers. Second, the thesis will deeply analyze the pros and cons of the considered solutions with an emphasis on system security. Third, the thesis will propose new approaches to interoperability. Proposed approaches will be empirically evaluated and their security and privacy will be analyzed. 

    Tutor: Homoliak Ivan, doc. Ing., Ph.D.

35. Logic synthesis employing machine learning

    Tutor: Mrázek Vojtěch, Ing., Ph.D.

36. Machine Learning Models that Reason

    V poslední době se stává čím dál patrnější, že k překlenutí propasti mezi současnými nejlepšími modely strojového učení a lidským učením nestačí jen zvyšovat počty parametrů a čekat na výkonnější hardware, který zvládne zpracování bilionů parametrů. Zdá se, že je třeba hledat nové modely, schopné objevovat a uvažovat na úrovni vysokoúrovňových pojmů a vztahů mezi nimi.
    
    
    
    Cílem disertační práce je výzkum nových modelů strojového učení, které překonají potřebu enormního množství příkladů, které jsou potřeba pro naučení chování, zvládnutelného lidmi velmi rychle (například počítač potřebuje sehrát velké množství her ke zvládnutí jednoduché videohry, zatímco člověk to zvládne velmi rychle, lidé ze sady proměnných rychle určí, jaká je příčinná souvislost mezi nimi, dokáží argumentovat sledem úvah atd.), a omezí problém sebejistého chybování (overconfident incorrectness) současných modelů. Budou zkoumány postupy učení, přidávající iterativně nové relevantní informace a také metody, podporující přímé pravděpodobnostní odvozování. Výsledky budou demonstrovány na vybraných problémech, zahrnujících mj. vysvětlování videa či tvorbu inferenčních grafů, operujících nad pojmy a vztahy mezi nimi.

    Tutor: Smrž Pavel, doc. RNDr., Ph.D.

37. Measuring output quality of large language models

    The advent of large language models (LLMs) is raising research questions about how to measure quality and properties of their outputs. Such measures are needed for benchmarking, model improvements or prompt engineering. Some evaluation techniques pertain to specific domains and scenarios of use (e.g., how accurate are the answers to factual questions in such and such domain? how well can we use the generated answers to train a model for a specific task?), others are more general (e.g., what is the diversity of paraphrases generated by an LLM? how easy to detect it is that the content is generated?).

    Through replication studies, benchmarking experiments, metric design, prompt engineering and other approaches, the candidate will advance the methods and experimental methodologies of LLM output quality measurement. Of particular interest are two general scenarios:

    1. Dataset generation and/or augmentation, where LLMs are prompted with (comparatively small) sets of seeds to create much larger datasets. Such an approach can be very useful, when dealing with a domain/task with limited availability of original (labelled) training data (such as disinformation detection).
    2. Detection of generated content, where stylometric-based, deep learning-based, statistics-based, or hybrid methods are used to estimate whether a piece of content was generated or modified by a machine. The detection ability is crucial for many real-world scenarios (e.g., detection of disinformation or frauds), but feeds back also to research methodologies (e.g., detecting the presence of generated content in published datasets or in crowdsourced data).

    The candidate will select (but will not be limited to) one of the two general scenarios, identify, and refine specific research questions and experimentally answer them.

    Relevant publications:

    • Cegin, J., Simko, J. and Brusilovsky, P., 2023. ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing https://arxiv.org/pdf/2305.12947.pdf
    • Macko, D., Moro, R., Uchendu, A., Lucas, J.S., Yamashita, M., Pikuliak, M., Srba, I., Le, T., Lee, D., Simko, J. and Bielikova, M., 2023. MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing https://arxiv.org/pdf/2310.13606.pdf

    The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in cooperation with industrial partners or researchers from highly respected research units. A combined (external) form of study and full employment at KInIT is expected.

    Tutor: Šimko Jakub, doc. Ing., PhD.

38. Modern Algorithms of Computer Graphics

    The topic concerns algorithms of computer graphics and image synthesis. Its main goal is to research new algorithms so that their features and application possibilities are better understood so that they are improved or newly created.  If suitable, it is possible to work on various platforms, includeing parallel CPUs, such as x86/64, ARM, Xeon PHI, GPU, etc. or other cores in CUDA, OpenCl, VHDL, etc. Algorithms of interest include:

    • rendering using selected computer graphics methods (such as ray tracing, photon mapping, direct rendering of "point clouds", etc.),
    • modeling of scenes and redering using artificial intelligence, including image synthesis using neural netowrks (especially CNNs),
    • processing and rendering of "lightfield" images, their acquisition, or possibly compression, reconstruction of 3D scenes from images and/or video, eventually also fusing with other sensors, such as LIDAR or RADAR,
    • modern algorithms of geometry suitable for applications in cpmputer graphics and perhaps also 3D printing,
    • emerging algorithms of 3D synthesis, holography, wavelet transform, frequency transform, etc.

    After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

    Collaboration on grant projects, such as TACR, H2020, ECSEL possible (employment or scholarship).

    Tutor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

39. Multi-level modeling and coordination of actions in holonic systems

    Tato disertační práce je zaměřena na rozvoj prostředků pro víceúrovňové modelování a koordinaci akcí v kontextu holonických systémů s využitím Robot Operating System (ROS). Holonické systémy, představující autonomní entity nazývané holony, nabízejí flexibilní přístup k organizaci autonomních prvků v rámci vyšších organizačních úrovní. Koncept holonů nachází uplatnění v širokém spektru oblastí, včetně průmyslové robotiky, inteligentní dopravy, autonomních vozidel, senzorických systémů a obecně v oblastech kyberneticko-fyzikálních systémů (CPS), Průmyslu 4.0 a Internetu věcí (IoT). Robot Operating System (ROS) je framework pro vývoj distribuovaných inteligentních systémů a robotů, umožňující komunikaci a spolupráci mezi autonomními entitami. Cílem této práce je poskytnout nové metody a nástroje, které povedou k efektivní integraci holonických konceptů do prostředí ROS. Zároveň se očekává, že práce přinese inovativní přístupy ke koordinaci akcí mezi autonomními entitami na různých úrovních organizace systému. Experimentální ověření navržených prostředků proběhne v reálných i simulovaných prostředích, s důrazem na možné aplikace v průmyslu a rozvíjejících se technologiích.

    Tutor: Janoušek Vladimír, doc. Ing., Ph.D.

40. Multimodal analysis for assessment of mental health

    Problem Statement: The importance of mental health has increased significantly over the past decade. However, the methods for the assessment of mental health issues at early stages are still in their infancy compared to the availability of corresponding methods for early assessment of physical health issues. Hence, it is required that due research is done to develop methods for early assessment of abnormalities leading to mental health problems.

    Issues with Current Solutions: Unlike physical health parameters, the mental health is assessed through a number of subjective parameters. Hence, there is lack of objective and quantitative methods for mental health assessments. In addition, the patients seek help when their mental health problem is at advanced stage. So, there is lack of continuous monitoring for mental health issues.

    Challenges: Many of the abnormalities related to mental health issues are subtle in nature and are related to behavior and other changes in facial expressions, speech and handwriting. In addition, there are changes in cortisol levels, skin conductance, heart rate variability and breathing rate. Hence, there are multiple modalities that should be included for measuring and quantifying any abnormalities related to mental health.

    Solution: Every modality has its pros and cons. For example, in neuroimaging, functional magnetic resonance imaging has high spatial resolution (in mm) and low temporal resolution (in seconds) while electroencephalogram has low spatial resolution (in cm) and high temporal resolution (in milliseconds). Combining both of them will result in high spatial as well as high temporal resolution. This research deals with the assessment of abnormalities leading to mental health problems by utilizing multimodal approach. The various modalities may include, but not limited to, electroencephalogram (EEG) brain signals, facial videos, speech audios, handwriting and text from social media. The physiological parameters from various modalities include, but not limited to, the heart rate, breathing rate, dominant emotion, fatigue and stress. Dominant emotion can be classified as positive or negative and then sub-classified as sad, happy, angry etc. Data mining and data fusion techniques will be developed for this multimodal analysis. The corresponding multimodal data is available for this project.

    Few Words About Supervision: I have extensive experience of working in the field of neuro-signal and neuroimage processing and I am currently head of a research group in this area. This is a multidisciplinary project and it will involve working with clinicians. However, the core of the project is related to IT in terms of development of a new method. Please feel free to contact me at malik@fit.vutbr.cz

    Tutor: Malik Aamir Saeed, prof., Ph.D.

41. Non-Traditional Visal Computing Algorithms

    The topic of the work includes algorithms of comptuer graphics, image, and video, i.e. "Visual Computing", such as HDR (High Dynamic Range) image, multispectral image, stereoimage, possibly also image augmented with material features, temperature, etc. The goal is to understand the features and possibilites but also applications, to analyze the algorithms in-depth, prepare new and improve them. The possible algorithms include:
    
    

    • Acquisition of HDR image and video, its compression, assembling from several standard images, videos, etc.,  
    • acquisition, processing, and visualization of mutispectral images (images with more than three spectral components),
    • processing of stereo images and other images containing the depth information using both structural light illumination or no structural light,
    • algorithms suitable for mobile technology and/or embedded systems focusing the above types of iamges, or images acquired from drones,
    • metods of visualization of HDR images and video, tone-mapping, real-time tone mapping,
    • algorithms and applications exploiting Wavelet, frequency, and/or other similar transformations.

     

    After mutual agreement, individually selected algorithms can be considered as well as soon as they do belong to the general topic.

    Collaboration on grant projects, such as TACR, MPO, H2020, ECSEL possible (employment or scholarship).

    Tutor: Zemčík Pavel, prof. Dr. Ing., dr. h. c.

42. Philosophical and Logical Roots of Computer Science

    Investigate philosophical, logical and mathematical studies by crucially important thinkers, whose works are central to the philosophical foundations of computer science. Pay a special attention to the works by Rudolf Carnap, Kurt Gödel, Bertrand Russel, Ludwig Wittgenstein, Alan Turing, Bertrand Otto Neurath, Herbert Feigl, Philipp Frank, Friedrich Waismann, Hans Hahn, Hans Reichenbach, Gustav Hempel, Alfred Tarski, Willard Van Orman Quine a Alfred Ayer. Make a systematic and compact body of knowledge that explains the philosophical and logical fundamentals of computer science in detail. Publish achieved results in prestige international-level journals. A summary of all these results publish as a monograph at Springer.

    Tutor: Meduna Alexandr, prof. RNDr., CSc.

43. Planning under uncertainty using formal methods and reinforcement learning

    Předmětem disertační práce bude zejména vývoj teoretických základů pro nové škálovatelné metody řízení systémů  pracujících v pravděpodobnostním prostředí. Zaměříme se na syntézu konečně stavových kontrolérů pro stochastické procesy s částečným pozorováním s využitím pokročilých metod formální kvantitativní analýzy, technikami induktivní syntézy a strojového učení. Práce se rovněž zaměří na využití těchto metod v oblasti řízeních prakticky relevantních pravděpodobnostních systémů, a na důkladné vyhodnocení aplikovatelnosti těchto metod. Výsledky této práce přispějí k pokroku v oblasti automatizace návrhu systémů.

    Výzkumu efektivních metod pro syntézu kontrolérů je v současnosti věnována značná pozornost v oblastech formální verifikace, návrhu a implementace programovacích jazyků, umělé inteligence a systémové biologie, o čemž svědčí zaměření řady špičkových konferencí (např. CAV, TACAS, PLDI či CMSB). Syntézou programů a modelů se rovněž zabývá řada velkých projektů na špičkových universitách a výzkumných institucích (např. Berkeley University či Microsoft Research).

    Práce bude řešena ve spolupráci s týmem VeriFIT pod vedením doc. M. Češky. Dále se počítá s úzkou spoluprací se skupinou prof. J.P. Katoena (řešitel ERC Advanced grantu) z RWTH Aachen University (Německo) a se skupinou assoc. prof. N. Jansena (řešitel ERC Starting grantu) z Radboud University Nijmegen (Nizozemí).  

    V případě zodpovědného přístupu a kvalitních výsledků je zde možnost zapojení do grantových projektů (např. české projekty GAČR či evropské projekty Horizone Europe).

    Tutor: Češka Milan, doc. RNDr., Ph.D.

44. Pre-onset detection of Alzheimer's Disease (AD) by investigating brain dynamics

    Problem Statement: Among all the types of dementia, Alzheimer's disease (AD) is the most common form with 70 % of those affected by dementia having AD. As the prevalence of AD increases with age, the number of people living with AD is expected to rise over the next decades due to better quality of life that results in increase in age across many countries. All this has resulted in an increased focus on ensuring pre-onset detection of AD and the corresponding intervention, which can lead to slowing the progression of the disease by providing adequate diagnostics.

    Issues with Current Solutions: Preclinical AD happens 10 to 15 years before the onset of the disease resulting in changes in the brain without showing any actual symptoms of the disease like memory loss etc. Pre-onset means detecting AD in or before the preclinical stage. The existing state-of-the-art methods mainly focus on the detection of later stages of AD, and the detection of preclinical AD is still an open research problem. Hence, this research targets pre-onset detection of AD (that is, early detection of Preclinical AD) because that will have huge impact on the lives of people. This can lead to early intervention and may result in further slowing the progression of the disease.

    Challenges:  At the stage of preclinical AD, the related signs and symptoms are not clear, and hence people at this stage do not seek any help. Therefore, a method for pre-onset detection of AD should be part of the regular health screening process and hence should be available in small clinical setups.

    Solution: Method for detection of preclinical AD will involve investigating underlying brain mechanisms to monitor and track changes related to pre-onset detection of AD. Magnetic resonance imaging (MRI) will be used as a reference to investigate the brain dynamics however it cannot be used in practice due to its high-cost and specialized setup environment which limits its usage at the screening stage. Electroencephalogram (EEG) will be used in this research which is widely available, is low cost, has a good temporal resolution, and has high mobility. Therefore, this project aims to investigate the changes in underlying brain mechanisms using EEG to develop EEG-based neuromarker for pre-onset detection of AD. The neuromarker will involve the features extraction and corresponding development of the machine learning model.

    Few Words About Supervision: I have recently moved to FIT at Brno University of Technology. I have decade long experience of working in the field of neuro-signal and neuroimage processing and I am currently in the process of setting up a research group in this area. This is a multidisciplinary project and it will involve working with clinicians. However, the core of the project is related to IT in terms of development of a new method. Please feel free to contact me at malik@fit.vutbr.cz

    Tutor: Malik Aamir Saeed, prof., Ph.D.

45. Recommender and adaptive web-based systems

    The recommender systems are an integral part of almost every modern Web application. Personalized, or at least adaptive, services have become a standard that is expected by the users in almost every domain (e.g., news, chatbots, social media, or search).

    Obviously, personalization has a great impact on the everyday life of hundreds of million users across many domains and applications. This results in a major challenge - to propose methods that are not only accurate but also trustworthy and fair. Such a goal offers plenty of research opportunities in many directions:

    • Novel machine learning approaches for adaptive and recommender systems
    • Trustworthy recommendation methods for multi-objective and multi-stakeholder environments
    • Explaining recommendations
    • Fairness and justice in recommendations
    • Biases in the recommendations

    
    There are several application domains where these research problems can be addressed, e.g., search, e-commerce, news, and many others.
    
    Relevant publications:

    • V. Bogina, T. Kuflik, D. Jannach, M. Bielikova, M. Kompan, C. Trattner. Considering temporal aspects in recommender systems: a survey. User Modeling and User-Adapted Interaction, 1-39, 2022. https://doi.org/10.1007/s11257-022-09335-w 
    • I. Srba, R. Moro, M. Tomlein, B. Pecher, J. Simko, E. Stefancova, M. Kompan, A. Hrckova, J. Podrouzek, A. Gavornik, and M. Bielikova. Auditing YouTube’s Recommendation Algorithm for Misinformation Filter Bubbles. ACM Trans. Recomm. Syst. 1, 1, Article 6, March 2023. https://doi.org/10.1145/3568392 

    
    The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in cooperation with industrial partners or researchers from highly respected research units from abroad. A combined (external) form of study and full employment at KInIT is expected. 

    Tutor: Kompan Michal, doc. Ing., PhD.

46. Scalable Elections and Electronic Voting based on Blockchains

    One goal of this thesis is to propose a scalable decentralized e-voting system based on smart contracts, with maximum voter privacy, fault tolerance, and coercion resistance. The first challenge is to optimize costs for running expensive zero-knowledge proof verification at smart contracts by off-chain constructs. The second challenge is the scalability w.r.t. to the number of participants and vote choices, which depends on the convenient type of the blockchain and its consensus mechanism, such as semi-permissionless blockchains (PoS) and permissioned blockchains (PoA). Another topic of this direction is partial tally-hiding protocols and their application in the context of public blockchains. The last topic is the eligibility of participants and its public verifiability/guarantees.

    Tutor: Homoliak Ivan, doc. Ing., Ph.D.

47. Scientific Machine Learning

    For decades, the behavior of systems in the physical world has been modeled by numerical models based on the vast scientific knowledge about the underlying natural laws. However, the increasing capabilities of machine learning algorithms are starting to disrupt this landscape. With large enough datasets, they can learn the recurring patterns in the data.

     

    However, a pure machine learning model usually has poor interpretability, needs a lot of data to train which can be hard to come by in many scientific domains, and might not be able to generalize properly. These concerns are being addressed by the field of scientific machine learning (SciML) – an emerging discipline within the data science community. It introduces scientific domain knowledge into the learning process. SciML aims to develop new methods for scalable, robust, interpretable and reliable learning.
    
    Physics-informed neural networks are a part of SciML - we include the physical constraints in the model through appropriate loss functions or tailored interventions into the model architecture. Through physics-informed machine learning, we can create neural network models that are physically consistent, data efficient, and trustworthy.

     

    The goal of the research is to explore how to incorporate scientific knowledge into the machine learning models, thus creating hybrid models based on SciML principles that include both data-driven and domain-aware components. The research could also be directed towards a combination of SciML and transfer learning (that reuses a pre-trained model on a new problem). The aim of such a combination is to take advantage of both approaches.
    
    SciML can be applied in many domains – we focus mainly on power engineering, e.g. supporting the adoption of renewables and on Earth science with emphasis on positive environmental impact improving climate resilience, but any other domain could be selected.

     

    Relevant publications:

    • Kloska, M., Rozinajova, V., Grmanova, G. Expert Enhanced Dynamic Time Warping Based Anomaly Detection. Expert Systems with Applications (2023) https://arxiv.org/pdf/2310.02280.pdf
    • Pavlik, P., Rozinajova, V., Bou Ezzeddine, A. Radar-Based Volumetric Precipitation Nowcasting: A 3D Convolutional Neural Network with UNet Architecture. Workshop on Complex Data Challenges in Earth Observation 2022 at CAI-ECAI (2022) https://ceur-ws.org/Vol-3207/paper10.pdf

     

    The research will be performed at the Kempelen Institute of Intelligent Technologies (KInIT, https://kinit.sk) in Bratislava in cooperation with industrial partners or researchers from highly respected research units from abroad. A combined (external) form of study and full employment at KInIT is expected.

    Tutor: Rozinajová Věra, Doc., Ph.D.

48. Security and Usability of Authentication Methods in Web2 and Web3

    This thesis is aimed at investigating the evolving landscape of authentication methods, examining their security, usability, and suitability for both traditional web2 and the emerging web3 ecosystem.

    Web2 Methods:

    The thesis critically analyzes the security vulnerabilities and usability limitations of conventional methods like passwords, OTPs (One-Time Passwords), and social logins. It delves into the advancements offered by passkeys, FIDO (Fast Identity Online) standards, and protocols like OAuth and OpenID Connect, evaluating their strengths in enhancing user experience and mitigating security risks.

    Web3 Methods:

    The research explores the unique challenges and opportunities presented by the decentralized nature of web3. It investigates innovative authentication mechanisms such as:

    • ERC 4337: Analyzing its potential to improve user experience and address the limitations of current account abstraction solutions.
    • Threshold Signature Schemes: Examining their role in enhancing security and enabling secure key management in decentralized environments.
    • Shamir Secret Sharing: Evaluating its applicability for secure key distribution and recovery in web3 applications.
    • Multi-Party Computation: Investigating its potential for privacy-preserving authentication protocols in decentralized systems.
    • Smart Contract-Based Wallets: Analyzing the security implications and usability considerations of different smart contract wallet designs.
    • OTPs for Smart Contract Wallets: Exploring the feasibility and security of integrating traditional OTP mechanisms with smart contract wallets.
    • Multi-Factor Key Derivation Functions: Investigating their suitability for enhancing the security and usability of authentication in web3 environments.

    The thesis should conduct a comparative analysis of web2 and web3 authentication methods, highlighting their respective strengths and weaknesses in terms of security, usability, privacy, and decentralization. In particular, embedded cryptocurrency wallets is a trending topic that is focused on the usability of web3 wallets. The thesis should design new approaches or improve the existing ones.

    Tutor: Homoliak Ivan, doc. Ing., Ph.D.

49. Synthesis of probabilistic programs

    Předmětem disertační práce bude zejména vývoj teoretických základů pro nové škálovatelné metody syntézy pravděpodobnostních programů. Zaměříme se na využití syntézy řízené syntaxí a na dosud neprozkoumanou oblast spojení pokročilých metod formální kvantitativní analýzy s přístupy založenými na SAT/SMT, prohledávání stavového prostoru a technikami induktivní syntézy. Práce se rovněž zaměří na využití těchto metod v procesu návrhu prakticky relevantních pravděpodobnostních systémů, a na důkladné vyhodnocení aplikovatelnosti těchto metod. Výsledky této práce přispějí k pokroku v oblasti automatizace návrhu systémů.

    Výzkumu efektivních metod syntézy je v současnosti věnována značná pozornost v oblastech formální verifikace, návrhu a implementace programovacích jazyků, umělé inteligence a systémové biologie, o čemž svědčí zaměření řady špičkových konferencí (např. CAV, TACAS, PLDI či CMSB). Syntézou programů a modelů se rovněž zabývá řada velkých projektů na špičkových universitách a výzkumných institucích (např. Berkeley University či Microsoft Research).

    Práce bude řešena ve spolupráci s týmem VeriFIT pod vedením doc. M. Češky. Dále se počítá s úzkou spoluprací se skupinou prof. J.P. Katoena (řešitel ERC Advanced grantu) z RWTH Aachen University (Německo) a se skupinou assoc. prof. N. Jansena (řešitel ERC Starting grantu) z Radboud University Nijmegen (Nizozemí).  

    V případě zodpovědného přístupu a kvalitních výsledků je zde možnost zapojení do grantových projektů (např. české projekty GAČR či evropské projekty Horizone Europe).

    Tutor: Češka Milan, doc. RNDr., Ph.D.

50. System Security in Blockchains and Consensus Protocols

    The goal of this thesis is to theoretically and practically analyze selected categories of consensus protocols in terms of throughput and security. The thesis should contain the evaluation of consensus protocols by simulations enabling to test the response of protocols under different network conditions and honest/adversarial consensus power. New scenarios of attacks should be investigated -- e.g., assuming violation of the protocol assumptions or incentives. The work should also leverage principles from game theory and statistical analysis. Examples of attacks to investigate are selfish mining attacks, pool-specific attacks, double spending attacks, attacks on shards, posterior corruption attacks, denial of service on the leader committee, long-range attacks, nothing-at-stake attacks, grinding attacks, etc. This topic is broad and can be further subdivided to multiple PhD students.

    Tutor: Homoliak Ivan, doc. Ing., Ph.D.

51. The impact of the life cycle of large language models on cybersecurity

    In recent years, there has been an increase in the use of neural networks for generating synthetic content, which goes hand in hand with the rise of new cybersecurity challenges. Generative models can have various impacts on cybersecurity, ranging from positive to negative.A significant area is the security of deployment and operation of generative models, primarily large language models.

    The goal of this thesis is to identify problem areas in a selected field of LLM deployment and operation (e.g., model inference attacks, model theft, or information theft) and analyze new trends, approaches, defenses, and their characteristics, impacts, and potential applications. The work should then propose new protection methods based on the analysis and research on the state of security for the selected areas.

    Recommended areas of work focus:
    Attacks and defenses in the side channel domain of large language models
    Attacks and defenses in the inference domain of large language models
    Defences in the area of adversarial attacks on large language models

    Participation in relevant international conferences and publication in peer-reviewed or scientific journals are expected.

    Tutor: Malinka Kamil, Mgr., Ph.D.

52. Visual Geo-Localization and Augmented Reality

    The project deals with geo-localization of images and videos in unknown environments using computer vision and computer graphics methods. The aim is to investigate and develop new image registration techniques (with geo-localized image database or 3D terrain model). The goal is an efficient implementation of proposed methods on mobile devices as well as search for additional applications in the area of image processing, computational photography, and augmented reality.

    • Further information: http://cadik.posvete.cz/locate/
    • Contact: http://cadik.posvete.cz/
    • Cooperation and research visits with leading research labs are possible (Adobe Research, USA, MPII Saarbrücken, Germany, Disney Research Zurich, Switzerland, INRIA Bordeaux, France)

    Tutor: Čadík Martin, doc. Ing., Ph.D.

53. Zero-knowledge constructs in the context of system security and the public blockchains

    This thesis is intended to explore the possible applications of ZK constructs in the context of system security and/or public blockchains. ZK constructs are used to provide public verification of the correctness of a certain computation or operation without revealing any private data related to the computation/operation. In this way, it is possible to implement, for example, public voting or auction protocols that preserve the privacy of data publicly produced by distributed participants. The most common ZK constructs are often instantiated by schemes that provide homomorphic encryption, such as ElGammal encryption or integer arithmetic fields over modulo N. However, the feasibility of these constructs in the domain of the public blockchain may vary due to possible high costs, or security aspects. The goal of this thesis is to analyze and quantify these existing options and implement the most meaningful (and novel) applications in the system security and or decentralized blockchains.

    Tutor: Homoliak Ivan, doc. Ing., Ph.D.

54. Tutor: Burget Radek, doc. Ing., Ph.D.

55. Tutor: Burget Radek, doc. Ing., Ph.D.

56. Tutor: Burget Radek, doc. Ing., Ph.D.

57. Tutor: Janoušek Vladimír, doc. Ing., Ph.D.