Find Academic Supervisors

Július HlaváčView profile

Assistant Professor

• Software Engineering
• Business Intelligence
• Data Mining
• +3 more

Július Hlaváč serves as an Assistant Professor at the Faculty of Informatics of the Pan-European University. His work integrates theoretical knowledge with practical applications in software engineering, business intelligence, and enterprise IT architecture. Position: Assistant Professor Workplace: Institute of Applied Informatics Phone: +421 2 6820 3685 Email: julius.hlavac@paneurouni.com Hlaváč emphasizes bridging academia and industry through teaching subjects like IT Project Management , Software Engineering , and Enterprise Architecture . He has supervised over 60 diploma theses at the university. His research spans Business Intelligence , Data Mining , and Information Security , focusing on extracting hidden patterns from datasets to support decision-making. Recent publications with Juraj Štefanovič (2020) and collaborators (e.g., Ružický et al., 2020) demonstrate expertise in machine learning, medical data visualization, and enterprise IT systems. Project: GAAA Challenge (Project number: GAAA 12/2018) Laboratory: Think Future Laboratory Miscellaneous achievements: PRINCE2 Foundation certification. Active in university governance as a member of the Academic Senate and representative on the Council of Universities.

• Electric Motor Control
• Power Electronics
• Sensorless Control
• +3 more

Dr. Karol Kyslan serves as Associate Professor and Vice-Dean for Research at the Faculty of Electrical Engineering and Informatics, Technical University of Košice, where he leads research initiatives and doctoral education programs. His academic profile centers on advanced control systems for electrical drives, with institutional affiliation deeply rooted in industrial automation applications. His research expertise spans sensorless control of Permanent Magnet Synchronous Motors (PMSM) , finite control set model predictive control , and sliding mode observers , addressing critical challenges in low-speed operation, fault tolerance, and industrial implementation. Key application domains include material processing lines, rotary shears, and steel production systems, where his work bridges theoretical control algorithms with practical machinery dynamics. Analysis of his 2021-2025 publications reveals a strategic evolution toward integrating machine learning for fault diagnosis while maintaining core focus on high-frequency signal injection techniques. Recent work demonstrates increasing sophistication in torque ripple compensation and real-time optimization, with growing emphasis on multiphase machine control and hardware-in-the-loop validation for industrial deployment.

Pavol LiptákView profile

Researcher

• Category Management
• Distribution Management
• Retail Marketing
• +3 more

Pavol Lipták is a PhD. student at the Department of Marketing, Faculty of Commerce, University of Economics in Bratislava . His work focuses on Category Management , Distribution in Marketing , and AI applications in business . Education: Pursuing doctoral studies since 2023; Master's in Marketing and Trade Management (2022); Bachelor's in Business in Trade (2020). Research Interests: Category Management, AI-driven retail strategies, and post-crisis business adaptation. Grant Involvement: Co-researcher in projects on AI challenges in marketing and linguistics, and distribution management during crises. Conference Participation: Active presenter at ICISAS 2025, Innovations 2024, and ISCDS 2024; member of organizing teams for CEECBE 2024 and faculty anniversary events. Teaching & Outreach: Tutor for MiniErasmus; promoter at university fairs and roadshows. Key Publications: Analyzed AI profitability in category management, post-pandemic retail strategies, and multilingual pricing effects.

• Financial Risk Management
• Corporate Finance
• Econometrics
• +5 more

Marek Káčer is an Assistant Professor at the Department of Finance within the Faculty of Economics and Finance at Bratislava University of Economics and Business. His research focuses on financial risk management, corporate finance, econometrics, and sovereign credit risk modeling. His notable research interests include SME failure prediction using machine learning, audit fee dynamics of Big 4 auditors, and sovereign credit rating analysis for emerging economies. He has collaborated with institutions like the SecureDataLab and contributed to projects on creative capacity measurement and regional development. Key contributions include studies on Slovak SMEs' financial distress prediction, gender diversity in private firms, and the equity gap in knowledge-based enterprises. His work blends quantitative methods with real-world applications in transition economies and innovation financing. Teaching responsibilities include courses like Foundations of Financial Econometrics and Risk Analysis in Finance. He maintains active international collaborations and publishes in journals such as Journal of Business Finance and Accounting and Journal of Corporate Finance .

• Parallel Programming
• GPU Programming
• Image Processing
• +5 more

Sándor Szénási is a Professor at the Department of Informatics within the Faculty of Economics and Informatics at J. Selye University, where he serves as the person responsible for the Applied Informatics study program. With over two decades of academic experience, he has established himself as a leading researcher in parallel programming, GPU programming, and image processing, with recent expansion into machine learning applications. Eötvös Loránd University, Faculty of Science and Informatics (2001-2004): Information technology teacher Budapest Polytechnic, John von Neumann Faculty of Information Technology (1997-2001): B. Engineer in Information Technology Óbuda University (2010-2013): PhD in Applied Informatics Habilitation at Óbuda University (2019): Information Science and Technology Professor inauguration at Óbuda University (2022) Szénási's research spans computational methods with practical applications across multiple domains. His early work focused on parallel and GPU programming for image segmentation and heat transfer simulation. More recently, he has integrated machine learning techniques with traditional computational approaches, particularly in metaheuristic optimization, speech processing, and inverse problem solving. His interdisciplinary research bridges computer science with transportation safety, manufacturing, and medical applications. His recent publications reveal a clear evolution toward hybrid computational approaches that combine machine learning with traditional algorithms. There is a strong emphasis on optimization techniques, particularly metaheuristics enhanced with machine learning components. His work spans diverse application areas including speech emotion recognition, autonomous vehicle control, additive manufacturing, and heat transfer simulation, while maintaining a core focus on computational efficiency and parallel processing. Szénási has been actively involved in multiple EFOP-funded research projects including 'Improvement of higher education institutes for better teaching quality and accessibility,' 'Dynamics and control of autonomous vehicles,' and 'Solving the Inverse Heat Conduction Problem with Machine Learning.' His collaborative work with researchers like Gábor Kertész, Zoltán Vámossy, and Imre Felde demonstrates his commitment to interdisciplinary research.

• Financial Markets
• Artificial Intelligence in Finance
• Risk Analysis
• +3 more

Robert Verner is an Associate Professor at the Department of Quantitative Methods within the Faculty of Economics and Business at the University of Economics in Bratislava. His work focuses on financial markets, optimization methods, and statistical modeling, contributing to both academic research and practical applications in business analytics. His recent publications span topics such as: Computational finance and machine learning Stock market forecasting with neural networks Risk analysis in insurance using AI Educational assessment frameworks He has also participated in international research initiatives and collaborative programs, including the Central Europe Connect initiative, which brings together students from leading business schools in Warsaw, Bratislava, and Vienna.

• Econometrics
• Operations Research
• Macroeconomic Modeling
• +4 more

Prof. Martin Lukáčik, PhD, is a Professor at the Department of Operations Research and Econometrics, Faculty of Economic Informatics, University of Economics in Bratislava. His academic career spans over two decades, focusing on econometric modeling, macroeconomic analysis, and operations research applications. Education: 2005: PhD in Econometrics and Operations Research, University of Economics in Bratislava 1998: Master's in Operations Research and Econometrics, University of Economics in Bratislava His research interests include econometrics, operations research, spatial spillover effects, and fuel price asymmetry. Recent projects analyze pandemic impacts on EU economies, asymmetric fuel pricing, and spatial economic dynamics. He has supervised doctoral students such as Ádám Csápai (2021–2024), Martin Benkovič (2013–2018), and Patrik Kupkovič (2013–2016). Grants include VEGA 1/0052/24 (structural parameter estimation) and KEGA 026EU-4/2024 (interactive economics books). Prof. Lukáčik is actively involved in international collaborations, including ERASMUS+ mobilities in Prague, Ostrava, and Split. He serves on scientific councils and editorial boards for journals like Equilibrium .

Peter ProcházkaView profile

Assistant Professor

• Internet of Things (IoT)
• UX Design
• Multimedia Applications
• +3 more

Peter Procházka is an Assistant Professor at the University of Economics in Bratislava, affiliated with the Faculty of Economic Informatics and the Department of Applied Informatics . His teaching and research focus on IoT , UX Design , Multimedia Applications , and technical education. His research spans several subfields, including: IoT : Development of educational tools using Arduino, smart device integration, and open-source platforms. Generative AI : Prompt engineering for neural networks and machine learning applications. Web Analysis : SEO tools, linguistic summaries for data visualization, and compliance with public administration standards. His publications highlight a trend toward data-driven decision-making in technology, including big data, machine learning, and IoT frameworks. He has also contributed pedagogical works on computer hardware, multimedia, and IoT, alongside collaborative studies on digital citizenship and pandemic response in educational institutions.

• Autonomous Driving
• Edge Computing
• Blockchain Technology
• +7 more

Šlapak Eugen is an Assistant Professor at the Technical University of Košice. His research focuses on autonomous driving systems, edge computing, and network optimization, with a particular emphasis on applying neural networks and blockchain technologies in vehicular and 5G networks. Eugen Šlapak holds a PhD in [specific field not explicitly stated, likely Engineering/Computer Science] and an Ing. (engineer) degree. His academic background combines technical expertise in telecommunications and computer science. His research interests span autonomous driving technologies, including simulation and control systems, as well as edge computing and metaverse integration. He also explores blockchain applications in vehicular networks, resource allocation in 5G and beyond, and the use of graph neural networks for network optimization. His work intersects machine learning, robotics, and telecommunications to address challenges in modern communication systems and intelligent transportation. Recent publications highlight advancements in neural radiance fields for industrial robotics, distributed edge video compression for autonomous driving, and blockchain-based resource allocation in connected vehicles. Earlier work includes optimization of UAV-assisted networks and HetNet topology design using machine learning clustering methods. While no formal awards are listed, his contributions to vehicular networks, edge computing, and AI-driven network design reflect significant scholarly impact. Advising details are not documented here, but his research collaborations likely involve cross-disciplinary teams focusing on autonomous systems and 5G infrastructure. No lab affiliations or teams are explicitly mentioned, though his teaching role in the course Stochastické modelovanie a analýza dát (SMaAD) suggests involvement in data analysis and stochastic modeling initiatives.

• Neural Networks
• Deep Learning
• Biomedical Signal Analysis
• +4 more

Máté Hireš is an Assistant Professor at the Technical University of Košice's Department of Computers and Informatics. He earned his PhD in computer science from the same institution, specializing in Parkinson's disease diagnosis using deep learning for voice analysis. His doctoral research extended his master's thesis on distributed neural radiance fields for autonomous vehicles. Hireš's work bridges neural networks, deep learning, and biomedical applications, particularly in analyzing handwriting, speech, and signal data for neurological disorder detection. His publications demonstrate consistent focus on Parkinson's disease diagnostics through innovative machine learning approaches applied to voice recordings and handwriting samples. Research Focus Deep learning for pathological speech detection Handwriting analysis for neurodegenerative diseases Robust diagnostic model development Biomedical signal processing

Miroslav MelicherčíkView profile

Assistant Professor

• High Performance Computation
• Parallel computation
• Computer Vision
• +3 more

Miroslav Melicherčík serves as an Assistant Professor at the Department of Computer Science, Faculty of Natural Sciences, Matej Bel University in Banská Bystrica, Slovakia. He has held this academic position continuously since 2013 while maintaining research connections with Comenius University in Bratislava. Education: 2002-2007: Mgr. in Chemistry-Computer Science, Faculty of Natural Sciences, Matej Bel University 2003-2008: Mgr. in Computer Science-English Language and Literature, Faculty of Natural Sciences, Matej Bel University 2011: RNDr. in Theoretical and Computational Chemistry, Faculty of Natural Sciences, Comenius University 2008-2012: PhD in Theoretical and Computational Chemistry, Faculty of Natural Sciences, Comenius University Dr. Melicherčík's research spans multiple computational domains with emphasis on High Performance Computation and Parallel computation methodologies. His work uniquely bridges Computer Vision applications with Computational Chemistry principles, while maintaining strong practical focus through Microcontroller Programming and Machine Learning implementations. This interdisciplinary approach enables innovative solutions across theoretical and applied computing domains. As an educator, he teaches a comprehensive curriculum including Algorithms and Data Structures, Programming, Effective Algorithms, Computer Systems, Internet of Things, Parallel Programming, Microcontroller programming, Quantum computing, and 3D modelling and printing. His teaching methodology integrates theoretical foundations with hands-on technical implementation, preparing students for both academic research and industry applications. Dr. Melicherčík maintains active research collaborations through the Center for High Performance Computing and the Center for Geoinformatics and Digital Technologies at Matej Bel University. His consultation hours are Monday-Wednesday 8:00-10:00, reflecting his commitment to student engagement and academic mentorship.

• Fuzzy Sets
• Intuitionistic Fuzzy Sets
• Data Analysis
• +6 more

Alžbeta Michalíková serves as Associate Professor at Matej Bel University's Faculty of Arts, Department of Computer Science. Her academic profile spans mathematical foundations and applied computational research with significant contributions to fuzzy logic systems. Her research focuses on Fuzzy Sets and Intuitionistic Fuzzy Sets with applications in Computer Vision, Biology, and Medicine. She investigates mathematical properties of these structures and explores integrations with Neural Networks and Evolutionary Algorithms. Recent work emphasizes smart city applications, air quality analysis, and digital education tools. Her publication trends show evolution from theoretical fuzzy mathematics (2005-2015) to applied AI systems (2020-present), particularly in environmental monitoring and transportation systems. Key themes include uncertainty modeling, image classification, and real-time data processing for urban applications. Erasmus+ KA2 projects on air pollution technologies (2022-2024) KEGA projects for digital educational tools (2020-2025) VEGA research on fuzzy models (2011-2017) APVV projects on scientific outreach She teaches Mathematics for Computer Scientists, Numerical Methods, and specialized Fuzzy Sets courses. Her technical infrastructure includes ORCID (0000-0002-0568-9279), SCOPUS (24448471500), and ResearchGate profiles. Current work focuses on digital identification keys for mycology education and AI-driven smart city analytics.

Ladislav VéghView profile

Associate Professor

• Computer Science Education
• Educational Technology
• Artificial Intelligence
• +5 more

Ladislav Végh, PhD., serves as an Associate Professor in the Department of Informatics at the Faculty of Economics and Informatics, J. Selye University in Komárno, Slovakia. With an office in TP3C and contactable via veghl@ujs.sk, he has maintained continuous academic employment at the university since 2005. His academic journey includes university studies in teaching mathematics-informatics at Constantine the Philosopher University in Nitra (1994-1999), examina rigorosa in teaching informatics at J. Selye University (2007-2008), and PhD studies in Basics and Methodology of Informatics at ELTE, Budapest (2009-2018). Végh's research focuses on innovative approaches to computer science education, particularly teaching programming through interactive methods. His primary research interests include teaching programming using animations, machine learning applications in education, and artificial intelligence integration in pedagogical contexts. He has consistently explored how game-based learning and visual representations can enhance students' understanding of complex programming concepts, with particular emphasis on algorithmic thinking and object-oriented programming principles. His publication record demonstrates a clear trajectory from foundational work on algorithm animations for teaching sorting concepts toward increasingly sophisticated applications of machine learning and deep learning in educational contexts. Recent publications show expansion into medical informatics applications, cultural heritage preservation through 3D modeling, and pandemic-era educational adaptations, while maintaining his core focus on programming education methodologies. His work frequently appears in reputable international conferences and journals, with growing citation impact. Electronic support of teaching programming (2008-2010) Terminology in e-learning (2009-2011) Modeling and animation-simulation models in e-learning (2011-2013) Modelling, simulation and animation in education (2014-2016) Interactive animation and simulation models in education (2018-2020) Analysis of natural and mathematical education at high schools (2019-2021) Interactive animation and simulation models for deep learning (2021-2023) Intelligent Animation-Simulation Models for Deep Learning (2024-2026) Végh has secured significant research funding through multiple KEGA and VEGA projects, demonstrating sustained research productivity and relevance. His work bridges theoretical computer science concepts with practical educational applications, particularly focusing on how interactive visualizations and game mechanics can transform programming education. His current projects indicate continued exploration of deep learning applications in educational contexts, suggesting an evolving research agenda that maintains educational technology at its core while expanding into more advanced AI methodologies.

• Fuzzy Logic
• Data Mining
• Information Systems Design
• +2 more

Dr. Miroslav Hudec is an Associate Professor at the Department of Applied Informatics , Faculty of Economic Informatics , University of Economics in Bratislava . He serves as Deputy Head of the Department and has been affiliated since 2013, following his role at the Institute of Informatics and Statistics (INFOSTAT) until 2013. PhD in Informatics, University of Belgrade, 2010 Master's in Operational Research, University of Belgrade, 2005 BSc in Informatics, University of Belgrade, 1996 Hudec's research focuses on fuzzy logic , data mining , linguistic summaries , and statistical information systems . His work bridges computational intelligence with practical applications in business intelligence, survey analysis, and decision support systems. He explores asymmetric conjunctions, aggregation functions, and ordinal sums for interpretable machine learning and adaptive classification. His 15 most recent publications (2016–2022) span journals and conferences like Expert Systems with Applications , Knowledge-Based Systems , and BALCOR . Key themes include fuzzy querying , conformance measures , financial literacy analysis , and explainable AI . Collaborations with Radko Mesiar, Erika Mináriková, and Miljan Vučetić highlight his interdisciplinary approach. Hudec contributes to projects such as VEGA 1/0849/15 and KEGA 025EU-4/2021 , focusing on fuzzy data modeling and educational materials. He has no listed scientific awards but actively participates in academic conferences (e.g., FSTA 2022, IPMU 2022) and serves in the EUSFLAT working group on soft computing in databases.

Martin KuchtaView profile

Assistant Professor

• Digital Marketing
• Marketing Management
• Consumer Behavior
• +5 more

Martin Kuchta is an Assistant Professor at the Department of Marketing, Faculty of Economics and Finance, Bratislava University of Economics and Business. His research focuses on digital marketing, consumer behavior, and machine learning applications in marketing. University: Bratislava University of Economics and Business Department: Department of Marketing Research Interests: Digital marketing, influencer marketing, programmatic buying, and data-driven marketing strategies. His work explores the intersection of technology, consumer behavior, and advertising effectiveness. Recent Publications: Analyze shopper marketing dynamics (2022), podcast evolution (2021), ad blocker impacts (2018), and machine learning adoption in digital marketing (2019). Projects: Principal investigator for PMVP I-17-106-00 on programmatic buying challenges, and contributor to VEGA and KEGA grants related to influencer marketing and digital education.