Distributed Algorithms Professors and Supervisors in Hungary | ApplySharif

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László BlázovicsView profile

Senior Lecturer

Budapest University of Technology and EconomicsHungary

László Blázovics is a Senior Lecturer at the Department of Automation and Applied Informatics, Budapest University of Technology and Economics. He leads the Applied Mobile Research Group (AMORG) focusing on mobile computing and distributed systems. His office is located at Q.B222, Magyar tudósok krt. 2., Budapest 1117, Hungary, with contact available via phone (+36 1 463-1648) and email. His primary research explores: Swarm robotics including drone coordination, uniform dispersal algorithms, and obstacle avoidance Distributed systems featuring sensor networks, multi-robot frameworks, and decentralized algorithms Mobile technologies spanning 5G networks, VR teleoperation, and cognitive communication AI applications such as human activity recognition and sensor fusion techniques Publication analysis reveals consistent focus on autonomous systems since 2011, with recent expansion into 5G-enabled applications and AI-driven solutions. Earlier work established foundations in swarm robotics and distributed algorithms, while recent publications demonstrate applied research in VR streaming, drone networks, and neural networks. He maintains active involvement in the Applied Mobile Research Group (AMORG), developing simulation frameworks for multi-robot systems and investigating cognitive drone applications.

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Attila HidegView profile

Senior Lecturer

Budapest University of Technology and EconomicsHungary

Attila Hideg is a Senior Lecturer at the Department of Automation and Applied Informatics, Budapest University of Technology and Economics (College of Engineering). His research focuses on multi-robot systems, distributed algorithms, and sensor networks. Contact E-mail: Hideg.Attila@aut.bme.hu Office: Q.B229, Magyar Tudósok krt. 2, Budapest 1117, Hungary Phone: +36 (1) 463-4225 Research Highlights Hideg's work centers around optimizing coordination in multi-robot systems, including asynchronous algorithms for robot swarms, uniform dispersal of low-cost flying robots, and sensor fusion techniques for indoor mapping. He has developed simulation frameworks like RobotCore and explored energy-efficient navigation in obstacle-rich environments. Recent Publications (2024-2016) Indoor Mapping with ICP Algorithms (2024) Multi-robot Simulation Frameworks (2022, 2018) Swarm coordination and dispersal algorithms (2022, 2021, 2019, 2017, 2016) Distributed sensor data collection (2016) Optimization of timetabling genetic operators (2015)

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Imre GáborView profile

Academic

Budapest University of Technology and EconomicsHungary

Imre Gábor is an Assistant Lecturer at the Department of Automation and Applied Informatics within the Faculty of Electrical Engineering and Informatics at Budapest University of Technology and Economics (BME). He is affiliated with the Applied Computer Science Group , focusing on software engineering and system performance. Office: Q.B224, Magyar tudósok krt. 2., Budapest 1117, Hungary Email: Imre.Gabor@aut.bme.hu Phone: +36 (1) 463-2713 Fax: +36 (1) 463-2871 His research spans Java-based systems , XML serialization , web application performance , and cloud computing . Recent work analyses WebSocket benchmarking , EC2 scalability , and application server tuning . Publications in Computer Science and Software Engineering highlight his expertise in distributed systems , XML technology , and enterprise application performance . He contributes to the BME Publication Registry and collaborates via platforms like Google Scholar and ResearcherGate . He is associated with the Applied Computer Science Group , which investigates theoretical and applied challenges in modern software development and system optimization.

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Gyula MaxView profile

Senior Lecturer

Budapest University of Technology and EconomicsHungary

Gyula Max is a Senior Lecturer at the Budapest University of Technology and Economics (BME) , specifically within the Department of Automation and Applied Informatics under the Faculty of Electrical Engineering and Informatics. His research focuses on computer networks, nonlinear modeling, and network stability analysis. Research Trends: His work explores nonlinear queue management in TCP/IP networks, stability conditions in distributed systems, and dataflow characteristics in network environments. Publications span mathematical modeling, network congestion control, and automated event recognition in traffic systems. Contact: Office located in Budapest at Magyar tudósok krt. 2, Room Q.B215. Email: Max.Gyula@aut.bme.hu .

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Veréb SzabolcsView profile

Assistant Professor

Budapest University of Technology and EconomicsHungary

Dr. Szabolcs Veréb is an Assistant Professor at the Budapest University of Technology and Economics (BME) within the Faculty of Electrical Engineering and Informatics , specifically in the Department of Automation and Applied Informatics and its Power Electronics Group. His academic work focuses on power electronics, control systems, and energy storage technologies. Research interests include: Advanced control techniques for power converters Dead time compensation in dual active bridge systems Energy storage integration for grid applications Battery management system optimization Hardware-in-the-loop simulation technologies Recent work trends demonstrate specialization in: Grid-connected inverter control Dual active bridge converter optimization Real-time simulation with FPGA-based systems Application-specific battery balancing methods Contact: Vereb.Szabolcs@aut.bme.hu

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KAPITÁNY KristófView profile

Associate Professor

Budapest University of Technology and EconomicsHungary

Dr. KAPITÁNY Kristóf is an Associate Professor at the Department of Photogrammetry and Geoinformatics within the Faculty of Civil Engineering at Budapest University of Technology and Economics. His office is located in Room K. ép / I. em. 31/8, with consultation hours every Wednesday from 12:00-13:00. He teaches courses including Civil Engineering Informatics (BMEEOFTAT42) and Numerical Methods (BMEEOFTMK51, BMEEOAFMB51). His research centers on advanced imaging and computational techniques for civil engineering, with key focus areas: Object reconstruction from image series (CT/X-ray analysis of cerebral vasculature, concrete, asphalt, and artworks) Geospatial analysis of urban systems (e.g., bike-sharing networks) Material science applications (organic insulation via SEM, fiber-reinforced concrete) AI-driven efficiency in construction (algorithmic design, digital twins) Recent publications (2015-2024) demonstrate strong emphasis on computed tomography for material diagnostics, with growing exploration of AI strategies. Key domains include structural material degradation analysis, non-destructive testing, geospatial data processing, and heritage conservation, reflecting consistent innovation in imaging methodologies. Awards include the #építő250 Scholarship for academic merit. No information is currently available regarding student supervision, research grants, or laboratory affiliations.

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Attila FazekasView profile

Associate Professor

University of DebrecenHungary

Dr. Attila Fazekas serves as an Associate Professor at the University of Debrecen, Faculty of Informatics, Department of Computer Science. His office is located in the Faculty of Informatics building (Kassai út 26, Debrecen), specifically in room I116 on the first floor. His primary research focuses on Digital image processing and Shape recognition , with significant contributions to Binary classification of imbalanced datasets . His work bridges theoretical computer science with practical applications in pattern recognition and machine learning techniques for handling skewed data distributions. Dr. Fazekas actively supervises doctoral research, currently guiding PhD student Khudhair Mohammed Aad. His research methodology emphasizes developing robust algorithms for image analysis tasks where class imbalance presents significant challenges. He maintains professional presence through institutional profiles including Tudóstér, Google Scholar, and LinkedIn, with regular updates to his research activities as recently as September 2023.

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Csaba NoszályView profile

Assistant Professor

University of DebrecenHungary

Csaba Noszály is an Assistant Professor at the University of Debrecen , affiliated with the Faculty of Informatics and the Department of Applied Mathematics and Probability Theory . His work focuses on probability theory, stochastic processes, network modeling, and random graphs, with applications in numerical methods and statistical analysis. Research Areas : Probability theory, random graphs, network evolution models, stochastic optimization, and mathematical statistics. His publications explore continuous-time network evolution, triangle-based models, Taylor’s power law in networks, and algorithms for global minimization of conic functions. Publications : Csaba has contributed to studies on 3-interaction networks, random graph cliques, distance metrics in 2D samples, and strong laws of large numbers for random fields. Academic Role : He is a full-time faculty member with a primary focus on teaching and research in applied mathematics and probability.

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János TóthView profile

Adjunct Assistant Professor

University of DebrecenHungary

Dr. János Tóth is an Adjunct Assistant Professor at the Department of Data Science and Visualization within the Faculty of Informatics at the University of Debrecen. His research focuses on optimization of ensemble systems and big data analytics. Institution: University of Debrecen School: Faculty of Informatics Department: Data Science and Visualization Rank: Adjunct Assistant Professor Email: toth.janos@inf.unideb.hu Research Interests: Dr. Tóth specializes in optimizing ensemble systems for medical image analysis and big data processing. His work spans: Parameter optimization techniques for ensemble models Integration of classical image processing with deep learning Medical data visualization solutions Applications in diabetic retinopathy detection Handling unstructured Hungarian medical reports Cloud-based machine learning pipelines Publication Trends: Over the past decade, Dr. Tóth's research has shown consistent focus on medical image analysis (particularly fundus and Pap smear imaging) combined with ensemble learning approaches. His work includes algorithm optimization (2014-2022), data visualization techniques (2012-2024), and natural language processing for Hungarian medical reports (2024). Recent publications (2024) demonstrate continued innovation in machine learning applications for both healthcare and business domains.

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Ádám VASView profile

Assistant Professor

University of DebrecenHungary

Ádám VAS is an Assistant Professor at the Faculty of Informatics , University of Debrecen , affiliated with the Department of IT Systems and Networks . His work focuses on distributed systems and computational networks for meteorological applications. Role: Assistant Professor Email: vas.adam@inf.unideb.hu Research Interests Ádám specializes in Distributed Systems , particularly the DSN-PC system , which integrates sensor networks and computational resources for numerical weather prediction. His work spans hardware design (e.g., microcontroller-based sensor stations), software development (e.g., data integration algorithms), and real-world deployment of hybrid sensor-computational networks. He also contributes to Ultrasound Computed Tomography , focusing on measurement techniques and image reconstruction. Article Trends His publications (2010–2019) emphasize Distributed Systems for meteorology, sensor network simulations, and ultrasound CT device prototyping. Key areas include network topology, autonomous systems, and low-level hardware-software integration. Scientific Awards Advising and Grants No information listed.

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Bukyoung JhunView profile

Research Fellow

Central European UniversityHungary

Bukyoung Jhun is a Research Fellow at the Department of Network and Data Science at Central European University. His research focuses on applying statistical physics to complex systems, including network epidemiology, latent geometry of networks, and phase transitions. He holds a PhD in Physics from Seoul National University, where he also earned his BSc and MSc in Physics. Education: BSc in Physics, Seoul National University MSc in Physics, Seoul National University PhD in Physics, Seoul National University (2023) Research Interests: His work bridges statistical mechanics and network science, addressing challenges in complex systems such as epidemic dynamics, network control via graph neural networks, and the latent geometric structures underlying real-world networks. He investigates critical phenomena in open quantum systems and infrastructure resilience against cascading failures. Key Contributions: Recent work explores reinforcement learning applications for decentralized power grid optimization, mean-field theory for dissipative systems, and hypergraph models for epidemic containment strategies. His publications highlight interdisciplinary approaches to understanding phase transitions and network-based interventions. Lab/Affiliations: Active within CEU’s Department of Network and Data Science, his research aligns with the university’s focus on computational and theoretical analyses of complex systems.

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Gerardo IñiguezView profile

Associate Professor

Central European UniversityHungary

Gerardo Iñiguez is an Associate Professor in Complex Systems at Tampere University's Faculty of Information Technology and Communication Sciences. He holds visiting positions at Central European University (Department of Network and Data Science), Aalto University (Computer Science), and UNAM (Center of Complexity Science). His research focuses on network science, computational social science, and data science, particularly studying social dynamics, contagion processes, and urban mobility. He co-founded the data science startup Predify (Mexico) and is involved in initiatives like the European Humane AI Net project. Iñiguez also contributes to interdisciplinary fields through game development and science communication. Education: PhD in Computational Science from Aalto University (Finland), MSc and BSc in Physical Sciences and Physics from UNAM (Mexico). His work bridges academia and industry, with a focus on applying network theory to real-world problems. Notable achievements include studies on rank dynamics, social network evolution, and the impact of deception in social systems. He actively publishes in top journals like Nature Communications and Science Advances. Research themes include complex contagion models, opinion dynamics, and the interplay between social structure and behavior. He explores how generic mechanisms shape hierarchy formation and uses statistical inference to validate models against empirical data. Current projects aim to optimize urban policies and understand polarization in digital societies.

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Éva HajnalView profile

Associate Professor

Óbuda UniversityHungary

Dr. Éva Hajnal is an Associate Professor and Vice Dean for Research at the Alba Regia Technical Faculty of Óbuda University. Her work focuses on interdisciplinary research spanning environmental monitoring technologies, artificial intelligence applications in agriculture, and sensor development. She leads efforts in livestock health monitoring using rumen bolus sensors and has contributed to ecological assessment methodologies using diatoms. Her email is hajnal.eva@amk.uni-obuda.hu , and she is located at Room 25 in Building K on Budai út 45. Her research integrates computer science with ecological challenges, addressing issues like groundwater quality and wind energy modeling. Research interests include: Biosensor development for livestock health AI-driven environmental data analysis Ecological status assessment using diatoms Virtual reality and motion tracking systems Online mapserver technologies Recent work emphasizes long-term rumen temperature analysis, low-computational algorithms for cattle heart rate estimation, and multilingual handwritten character datasets. Her research trends show strong focus on applying computational methods to ecological and agricultural challenges. She has contributed to projects like wastewater management control systems and biodiversity indices for freshwater ecosystems. No specific scientific awards are listed. Her administrative role as Vice Dean for Research involves overseeing research strategy and collaboration at the faculty level. No advising or grant details are provided in available texts. Her work is associated with the university's technical faculty and environmental engineering teams, contributing to both academic and applied research initiatives.

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Géza VáradyView profile

Associate Professor

University of PécsHungary

Géza Várady is an Associate Professor at the University of Pécs (PTE), holding roles such as Vice Dean for Scientific Affairs at the Faculty of Engineering and Information Technology. He has served as Head of the Department of Technical Informatics and has held various academic leadership positions. His career spans over two decades, with research focusing on computer vision, image processing, and lighting technology. He holds a PhD from the Doctoral School of Informatics at the University of Pannonia. Education includes a Master’s degree in Computer Engineering and a PhD in Informatics. He has also held research positions internationally, such as a Leonardo Fellowship at Schefenacker GmbH in Stuttgart, Germany. His research interests include mesopic vision models, color correction systems, 3D depth sensing using monocular cameras, and drone-based applications. He leads the drone research team and has supervised doctoral students in areas like image data correction and 3D modeling. Notable achievements include the 2024 Publication Excellence Award, IBM Faculty Awards for adaptive lighting systems, and the Walsh Weston Award for contributions to lighting science. He actively participates in academic governance, serving on national committees like the Hungarian Academy of Sciences’ Engineering Sciences Committee and the John von Neumann Computer Society. Publications span over 100 articles in journals like Lighting Research and Technology and Technical Gazette , focusing on topics ranging from photogrammetry to autonomous drone control. He has authored textbooks on MPI programming and computer architecture.

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Peter LangView profile

Professor

Budapest University of Technology and EconomicsHungary

Peter Lang is a Full Professor at the Department of Building Service Engineering and Process Engineering, Budapest University of Technology and Economics. His research focuses on chemical engineering, particularly distillation process optimization, energy efficiency, and sustainability in separation technologies. Specializes in batch extractive distillation Develops energy-efficient distillation systems Investigates sustainability indicators aligned with SDGs His recent publications emphasize pressure-swing distillation sequences, heat pump-assisted distillation, and recycling strategies for solvent recovery. Key methodologies include genetic algorithms, dynamic simulation, and process intensification techniques. While no explicit scientific awards are listed, his work has been cited over 9,000 times on ResearchGate.

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