Během akademického roku jednou týdně přednáší zvaný přednášející (z ciziny i od nás) o své vědecké práci. Kolokvium, které probíhá na Fakultě informatiky, je otevřeno pro vědeckou veřejnost. Termíny přednášek můžete najít v programu. Úterý 14.00 - 15.00, D2, FI MU, Botanická 68a, Brno

Program kolokvií s abstrakty pro semestr Podzim 2021

21. 9. 2021
Dr.-Ing. Lydia Kraus, ÚVT MU
Insights Gained from Evaluating MUNI's Cybercompass Course with Computer Science Undergraduates
Abstrakt: As cyber threats endanger everyone from basic users to computing professionals, spreading cybersecurity awareness becomes more and more critical. Therefore, a dedicated team at MUNI designed an “everyday” cybersecurity course that is freely available online for students, employees, and the general public. The course, named Cybercompass, aims to raise awareness about essential cybersecurity topics. It offers simple actionable steps that anyone can use to implement defensive countermeasures. In spring this year, we evaluated how students perceive the course and whether it impacts their security behavior. We administered the course to 138 undergraduates in computer science. They completed the course as a part of their graded homework and filled out a questionnaire after each lesson. Statistical analysis of the questionnaire responses revealed that the students valued the course highly. They reported new learning, changes in their perspectives, and transfer to practice. At the same time, they suggested suitable improvements to the course. Based on the results, we have distilled specific insights that will help MUNI's course designers to improve the course and that will help security educators outside MUNI to design similar courses.
Stručný životopis: Dr.-Ing. Lydia Kraus is a usable security researcher and obtained her Doctor of Engineering from Technical University of Berlin in 2017 with a thesis on the user experience with security and privacy mechanisms on smartphones. Since September 2020, she is working as a senior researcher at CSIRT-MU, Institute of Computer Science, Masaryk University. From 2018-2020, she was a postdoctoral fellow at MUNI's Faculty of Informatics where she was a member of the Centre for Research on Cryptography and Security (CRoCS). From 2013 to 2017, she was employed at Technical University of Berlin as a researcher at the Quality and Usability Lab which was also part of Telekom Innovation Labs, the joint research and innovation unit of Technical University of Berlin and Deutsche Telekom AG. During this time, she was a fellow of Software Campus, a professional development program for future IT executives (2015-2017). From 2010 to 2012, she also had the pleasure to gain a lot of interesting impressions while living and working abroad as a researcher at the Mihajlo Pupin Institute in Belgrade, Serbia, in the field of IT-supported emergency management. She received her Diploma degree (Dipl.-Ing., M.Sc. equivalent) in Electrical engineering and Information Technology with a major in Communications engineering from Technical University of Munich (TUM) in 2009.
5. 10. 2021
doc. RNDr. Martin Tancer, Ph.D., MFF UK
Computational complexity in combinatorial topology
Abstrakt: Interactions between the theory of computation and topology date back at least to 1950's when Novikov proved that simple conectedness (of a triangulated topological spaces) is algorithmically undecidable. Nowadays, there are a plethora of interactions that mutually enrich both fields.

The aim of the talk is to survey and explain some such recent interactions---naturally those that I have been working on. This will include the algorithmic embeddability question, various decompositions of simplicial complexes, or untangling (un)knots.

12. 10. 2021
Ing. Václav Oujezský, Ph.D., FI MU
Methods of verification of the passive optical network
Abstrakt: The presentation discusses and presents the possibilities of analysis of management traffic in gigabit passive optical networks, particularly the recommendation ITU-T G.984.3 and the detection of the content of frames deviating from the recommendation. The frames are captured by FPGA (Field Programmable Gate Array) programmable network card and processed with a software parser. Further techniques are discussed. The proposed method can bring a way how to test if vendors of network devices follow the recommendation and if the content of the frames can be treated as secure and trustworthy.
19. 10. 2021
prof. Sergio Cabello, Faculty of Mathematics and Physics, University of Ljubljana, Lublaň, Slovinsko
Interactions between geometry, graphs and algorithms
Abstrakt: I will describe some of the interactions between graphs and geometry, many of them with an algorithmic slant. In particular, I will discuss the computation of maximum matching in graphs defined geometrically and problems in geometric optimization. Emphasis will be on breadth rather than depth and I will mention several open problems.
26. 10. 2021
Mgr. Jiří Chmelík, Ph.D., FI MU
Augmented and Virtual reality in Research and Education
Abstrakt: The talk presents current trends in the usage of augmented and virtual reality in research and education. Several lines of research will be discussed along with former and current projects at FI MU. In the second part, a new platform for education in a virtual environment, currently in development at MUNI, will be presented. The vision and current status will be discussed, as well as the interdisciplinarity of the project.
9. 11. 2021
Ing. Leonard Walletzký, Ph.D., FI MU
The development of Smart Cities in the Czech Republic
Abstrakt: The main topic of the presentation will be the description of the development of Smart City methodology used in the Czech Republic, the contribution of the Faculty of informatics and team of Laboratory of Service systems. Also, there will be presented the main challenges and findings in the domain of Smart City, including the continuing development of the conceptual model of Smart City, and possible overlaps to the research of other labs of the faculty.
16. 11. 2021
prof. Ing. Václav Přenosil, CSc., FI MU
Results of the Laboratory of Embedded Systems
Abstrakt: The Laboratory of Design and Architecture of Digital Systems (EmLab) deals with teaching and research and development of applications called embedded systems. Research and development are focused primarily on the following areas:
  • fast Digital Pulse-Shape Analyze for Spectrometry of Neutrons and Gamma Rays
  • design, implementation, analysis, testing, and operation of embedded systems
  • electronic of Unmanned Vehicles
  • security of information and communication systems
The presentation will focus on getting acquainted with the results of some research and development projects solved within EmLab.
23. 11. 2021
Dr. Matthew Kwan, Institute of Science and Technology Austria, Vídeň, Rakousko
Friendly bisections of random graphs
Abstrakt: Resolving a conjecture of Füredi, we prove that almost every n-vertex graph admits a partition of its vertex set into two parts of equal size in which almost all vertices have more neighbours on their own side than across. Our proof involves some new techniques for studying processes driven by degree information in random graphs, which may be of general interest.

This is joint work with Asaf Ferber, Bhargav Narayanan, Ashwin Sah and Mehtaab Sawhney.

7. 12. 2021
doc. RNDr. Tomáš Brázdil, Ph.D., FI MU
RationAI: Rational and conservative artificial intelligence (not only) in digital pathology
Abstrakt: I will present the new research group RationAI concerned with explainable artificial intelligence (AI) techniques in biomedicine. I will concentrate on a single case study, analyzing whole slide images (WSI) from digital pathology. Our primary concern is to find out how good cutting-edge methods are and to make them useful for pathologists. Our aims will be illustrated using our deep learning-based system for cancer detection in WSI of prostate cancer. Subsequently, I will explain how explainability can be achieved using (slightly modified) standard methods for explainable AI and how pathologists interpret our explanations. Finally, I will present further plans of the RaionAI group in pathology and other areas of AI in biomedicine.
14. 12. 2021
prof. Stanislav Sobolevsky, Center for Urban Science And Progress, New York University, New York, USA
Urban Network Analysis: from Traditional Network Science to Graph Neural Networks
Abstrakt: This big urban data creates fresh opportunities to gain an unparalleled understanding of complex urban systems and respond to urban challenges, mitigating unwanted exposures and optimizing urban operation through smart digital solutions. While recent network analysis and AI techniques help address the complexity and interconnectedness of the urban data. I will introduce the spectrum of techniques from traditional network analysis to graph neural networks used by my teams at NYU and MIT to study the spatio-temporal transactional data on human mobility and interactions, as well as their applications to smart urban planning, transportation innovation, and urban analytics. We shall also discuss a concept of hierarchical graph neural networks – a novel fusion of network science and deep learning techniques our team at Masaryk University is developing, as well as its applications to predictive modeling and detection of patterns, impacts, and emergent phenomena in spatio-temporal networks of urban activity and/or quantifying populational exposure to urban stressors.
Stručný životopis: Stanislav Sobolevsky started his career in Belarus in fundamental mathematics, earning a Ph.D. (1999) and a Doctor of Science habilitation degree (2009) in Belarus, studying the branching of the differential equations solutions. Has held faculty and leadership positions at the Belarusian State University at that time. Later transitioned to applied cross-disciplinary research in data and network science, joining MIT SENSEable City Lab toco-lead a team of researchers and urban innovators. Since 2015 joined the faculty at the New York University as an Associate Professor of Practice. Established an Urban Complexity Lab studying urban activity through big urban data leveraging network analysis, machine learning, and AI. Authored over 100 research publications in top-tier journals and conferences, collecting thousands of citations. Applied projects of the Urban Complexity Lab on transportation modeling, trajectory mining, anomaly, pattern, and vulnerability detection in temporal urban networks attracted support from federal agencies, private and public sectors.