Find Academic Supervisors

Gergely VakulyaView profile

Associate Professor

• Cybersecurity
• Agricultural Technology
• Sensor Networks
• +3 more

Dr. Gergely Vakulya is an Associate Professor and Research Fellow at Óbuda University. He specializes in interdisciplinary research spanning cybersecurity, agricultural technology, sensor networks, and image processing. His work integrates hardware design, algorithm development, and real-world applications. Dr. Vakulya’s recent focus includes developing rumen bolus sensors for dairy cattle health monitoring, gamification of cybersecurity training, and innovative methods in camera exposure time measurement. He is affiliated with Óbuda University’s Budai Road and Pirosalma Street campuses, with an office at building F room 316. His research frequently addresses challenges in data collection, sensor fusion, and embedded systems. Research Interests: Dr. Vakulya’s expertise includes cybersecurity frameworks (e.g., CTF challenges), agricultural IoT systems (e.g., rumen bolus sensors for livestock monitoring), and image processing techniques (e.g., genetic algorithms for shape approximation). His work on visible light communication (VLC) and wireless sensor networks highlights his contributions to communication protocols and energy-efficient systems. Recent trends in his publications emphasize cross-disciplinary approaches, such as applying AI methods to agricultural sensor data and optimizing sensor networks for real-time applications. Advising & Grants: No specific advising relationships or grants are listed in available materials. His research infrastructure is likely supported through institutional and collaborative projects. Labs/Teams: While not explicitly stated, his research likely involves collaborations within Óbuda University’s engineering and computer science departments. His work on rumen sensors and VLC systems suggests potential affiliations with robotics, biomedical engineering, or smart agriculture research groups.

• Computer modeling of curves and surfaces
• Constructive and representational geometry
• 3D technologies
• +3 more

Ildikó Papp is an Associate Professor at the Faculty of Informatics, University of Debrecen , specializing in the Department of Data Science and Visualization . Her academic focus spans geometric modeling , 3D technologies , and experience-oriented teaching methods , bridging theoretical geometry with practical digital applications. Research Interests: Computer modeling of curves and surfaces Constructive and representational geometry 3D printing and visualization Cognitive infocommunication in education Publication Trends: Focus on QR code integration with 3D surfaces (2021-2022) Biomedical applications of FDM printing (2020) Educational impact of 3D technologies (2016-2018) Advancements in Bézier curve isoptics (2012-2013) Optimization techniques in geometric modeling (2012) Contact: Email: papp.ildiko@inf.unideb.hu

György DósaView profile

Professor

• Algorithms
• Combinatorics
• Operations Research
• +3 more

György Dósa is a Professor in the Department of Mathematics at the University of Pannonia. His research focuses on algorithm design, combinatorial optimization, and operational research problems. University: University of Pannonia Department: Department of Mathematics Email: dosagy@almos.uni-pannon.hu His work primarily addresses online and offline bin packing, scheduling algorithms, and combinatorial optimization. Recent publications analyze approximation guarantees, lower bounds for algorithm performance, and game-theoretic approaches to packing problems. Publications highlight expertise in : Bin Packing , Scheduling Algorithms , Combinatorial Optimization , Online Algorithms , and Game Theory . Key subfields include approximation theory, constraint handling, and algorithm complexity analysis.

Asztalos MárkView profile

Associate Professor

• Model-Driven Engineering
• Software Engineering
• Graph Rewriting Systems
• +3 more

Márk Asztalos is an Associate Professor at the Budapest University of Technology and Economics , affiliated with the Faculty of Electrical Engineering and Informatics and the Department of Automation and Applied Informatics . He leads research in the Visual Modeling Group, focusing on model-driven engineering, graph rewriting systems, and domain-specific languages. Research Interests: Model transformation verification, text-based modeling, graph pattern matching, and cloud/mobile system modeling. Contact: E-mail: Asztalos.Mark@aut.bme.hu , Office: Q.B226, Department of Automation and Applied Informatics, BME. His publications emphasize model transformation verification (2010-2015), graph rewriting techniques for pattern matching (2015-2017), and domain-specific language design (2014). Recent work (2020) analyzes model integration challenges in model-driven methodologies. Contact details: Address: Budapest 1117, Magyar tudósok krt. 2, Hungary Phone: +36 (1) 463-3702

Carolin HannuschView profile

Senior Lecturer

• Algebraic coding theory
• Cryptography
• Error-correcting codes
• +2 more

Dr. Carolin Hannusch is a Senior Lecturer at the Department of Computer Science, Faculty of Informatics, University of Debrecen, Hungary. She holds a B.Sc. (2009) and M.Sc. (2011) in Mathematics from the University of Debrecen, and completed her Ph.D. there in 2015. Her office is located in Room I129 of the Faculty of Informatics building. Her primary research focuses on algebraic coding theory and cryptography , with expertise in error-correcting codes, combinatorial designs, discrete mathematics, and applications in cybersecurity. She also publishes in mathematics education and digital text analysis. Her recent publications demonstrate strong interdisciplinary work across coding theory (e.g., binary self-dual codes, Goppa codes), cryptographic systems (e.g., hash functions, symmetric cryptosystems), and educational technology (e.g., digital text management, online trigonometry instruction). Awards include: Special Price at XXX. Students' Conference Hungary (2011) Universitas Prize for Young Researchers (2015) University of Debrecen's "Thesis of the Year" Prize (2018) She maintains collaborations with researchers across Europe and has presented work at international conferences including FedCSIS, CITDS, and Central European Conference on Cryptology.

Henrietta TománView profile

Assistant Professor

• Medical image processing
• Fusion systems
• Quasigroups
• +3 more

Dr. Henrietta Tomán serves as an Assistant Professor in the Department of Data Science and Visualization at the University of Debrecen's Faculty of Informatics, where she bridges advanced mathematical theory with practical medical imaging applications. Her work integrates abstract algebraic structures with cutting-edge AI systems to solve critical healthcare challenges. Her research portfolio spans three interconnected domains: Medical image processing (particularly ensemble-based segmentation and quality assessment for ophthalmic diagnostics) Geometric structures (quasigroups, loops, and differentiable manifolds) Stochastic optimization for resource-constrained AI systems Analysis of her publication trajectory reveals evolving expertise: early work (2010-2014) established foundations in geometric loop theory applied to image processing, while recent research (2020-2024) pioneers stochastic fusion techniques for medical image ensembles under computational constraints. Her most significant contributions involve translating mathematical abstractions into robust clinical decision-support tools, particularly in diabetic retinopathy detection and epidemic modeling. Current work demonstrates increasing focus on real-time AI systems that maintain accuracy under hardware limitations, reflecting urgent needs in telemedicine and mobile health applications. Dr. Tomán maintains active collaboration within the Doctoral School of Informatics and contributes to Hungary's national research initiatives in medical AI, with consistent publication output in top-tier venues spanning computer vision, medical imaging, and mathematical computing.

• Artificial Intelligence
• Theoretical Computer Science
• Fuzzy Logic
• +3 more

Gégény Dávid is a researcher at the University of Miskolc 's Faculty of Mechanical Engineering and Informatics . His work focuses on fuzzy rough sets, lattice structures, and approximation algorithms. Affiliation: Institute of Mathematics, University of Miskolc Doctoral School: József Hatvany Doctoral School of Informatics (since 2006) Research Interests : Fuzzy Rough Set Theory Lattice and Conceptual Structures Pattern Mining Algorithms Granular Computing Tolerance Relations Mathematical Modeling Publications (2019-2024) span journals like International Journal of Approximate Reasoning , Knowledge-Based Systems , and conferences including IEEE FUZZ-IEEE . Key themes include Optimistic approximations in multigranular systems Rough set applications in image processing Interpolation methods for fuzzy environments Lattice-theoretic foundations for knowledge discovery

Edith Alice KovácsView profile

Associate Professor

• Multivariate Statistics
• Probability Graph Models
• Artificial Intelligence
• +8 more

Edith Alice Kovács is an Associate Professor at the Department of Differential Equations, Budapest University of Technology and Economics (BME). She holds a PhD from the West University of Timisoara (2005) and has taught at multiple institutions, including the General College of Entrepreneurship (ÁVF) and universities in Arad. Her research focuses on multivariate statistics, probability graph models, information theory, AI/ML, network reliability, and mathematical modeling in finance/medicine. She has received the BME Innovation Award 2020 for anomaly detection work. Kovács has presented at conferences worldwide, including the European Conference on Operational Research and the IEEE SACI conference. Her teaching spans subjects like economic modeling, operations research, and data science across BME and ÁVF programs. Education: BSc and MSc in mathematics from the West University of Timisoara (1994), PhD in Mathematics (2005). Professional roles include full-time employment at BME since 2015 and prior experience at Aurel Vlaicu and Vasile Goldis Universities (1994–2000). Research interests include decision-making under uncertainty, metaheuristic algorithms, neural halos, and social network modeling. Recent work explores copula-based anomaly detection and vine copula structures. Kovács has contributed to projects like stochastic inventory modeling for TESCO and VNF fingerprinting for Nokia.

• Molecular Polaritons
• Quantum Chemistry
• Spectroscopy
• +5 more

Tamás Szidarovszky is a Research Fellow and lecturer at the Department of Physical Chemistry, Eötvös Loránd University (ELTE), Faculty of Science. His work focuses on theoretical and computational studies of molecular polaritons, laser-induced dynamics, and rovibrational spectroscopy. He is affiliated with the Institute of Chemistry and maintains a research website highlighting his computational tools like LIMAO for simulating molecular alignment dynamics. His research interests span quantum chemistry, nonadiabatic phenomena, and molecular spectroscopy, with a particular emphasis on light-matter interactions in diatomic and polyatomic systems. Notable contributions include advancements in ab initio methods for polariton modeling and field-dressed spectral analysis. His publications (2016–2025) reflect expertise in molecular dynamics simulations, laser control techniques, and high-precision spectroscopic data. While no awards are listed, his work demonstrates significant impact in computational quantum chemistry and molecular physics. Labs/Teams: Affiliated with the Institute of Chemistry at ELTE, contributing to research groups specializing in theoretical spectroscopy and molecular dynamics.

• Environmental Engineering
• Civil Engineering
• Wastewater Treatment
• +5 more

Koncsos Tamás serves as an Assistant Professor at the Department of Sanitary and Environmental Engineering within the Faculty of Civil Engineering at Budapest University of Technology and Economics. His academic work centers on computational methods for environmental engineering challenges, particularly in water and wastewater systems. His research expertise spans: Activated sludge modeling (ASM) and wastewater treatment optimization Development of decision support systems using soft computing techniques Neuroevolutionary algorithms, neural networks, and self-organizing maps Markov decision processes for complex engineering problems Integration of computational intelligence in civil infrastructure Analysis of his publications from 2010-2025 reveals a consistent focus on applying neural networks and optimization techniques to water management challenges. His work demonstrates progression from fundamental computational methods (2010-2012) toward integrated decision support systems like WateRisk and practical applications in wastewater plant design (2021-2025). Key thematic threads include quadratic approximation methods for bioprocess modeling, risk-based scenario analysis for water resources, and the adaptation of machine learning techniques for environmental engineering constraints.

Imre VargaView profile

Associate Professor

• Condensed Matter Physics
• Quantum Chaos
• Disordered Systems
• +4 more

Imre Varga is an Associate Professor at the Department of Theoretical Physics, Budapest University of Technology and Economics. His office is located in Room F III. GF 8, and he can be contacted via email at varga.imre@ttk.bme.hu or by phone at +36 1 463 1059. Dr. Varga's research focuses on theoretical physics with emphasis on: Disordered quantum systems and Anderson localization transitions Multifractality in critical random matrix ensembles Quantum chromodynamics at high temperatures Quantum chaos and mesoscopic transport phenomena Finite-size scaling at metal-insulator transitions His publications predominantly explore critical phenomena in condensed matter and high-energy physics, with recurring themes of multifractal analysis, disorder effects, and quantum transport. Recent works investigate complexity measures in quantum systems (2025), phase space entropies (2022), and Dirac operator spectra in QCD (2015). No scientific awards or student advising relationships are mentioned in the available information. Laboratory affiliations, team collaborations, or grant information are not specified in the provided materials.

Tamás KristófView profile

Associate Professor

• Molecular Simulation
• Material Science
• Electrochemistry
• +3 more

Tamás Kristóf is an Associate Professor at the Faculty of Engineering , University of Pannonia . His research focuses on molecular simulations, electrochemical systems, and material science. Key areas include adsorption separation processes in zeolites, electrochemical corrosion mechanisms, and intercalation chemistry in clay minerals. He employs advanced computational methods such as molecular dynamics and Monte Carlo simulations to study material properties and reaction kinetics. His work bridges theoretical modeling (e.g., Butler-Volmer equation refinements) and experimental validation (e.g., electrochemical impedance spectroscopy). Recent studies explore nanoporous materials, hydrogen sulfide separation, and corrosion resistance of alloys in biofuel environments. His contributions span over 40 peer-reviewed articles, emphasizing predictive modeling and material characterization. Dr. Kristóf collaborates on interdisciplinary projects involving energy storage, environmental chemistry, and nanomaterials. His lab develops novel simulation techniques for complex systems, including ion transport in biological and synthetic channels. Despite no listed awards, his work is widely cited in electrochemistry and materials engineering journals.

• Fuzzy Logic Systems
• Artificial Intelligence
• Reinforcement Learning
• +5 more

Szilveszter Kovács is a Professor at the Institute of Informatics II within the Faculty of Engineering and Information Technology at the University of Miskolc, where he has been actively contributing since 1995. His academic journey at the university includes positions at the Department of General Informatics (1995-2005) and later at the Institute of Informatics II (2013-present). Professor Kovács' research primarily focuses on fuzzy logic systems , particularly Fuzzy Rule Interpolation (FRI) techniques and their integration with reinforcement learning. His pioneering work on HFRIQ-learning (Heuristically accelerated Fuzzy Rule Interpolation Q-learning) addresses critical challenges in decision-making under uncertainty. He has developed innovative approaches including Extended Incircle FRI for multidimensional applications and ethologically inspired behavior modeling systems that draw from biological principles to create more adaptive AI. His publication record demonstrates consistent scholarly output with 249 publications and 1,678 citations. Recent work (2022-2025) shows sustained productivity across journals like Periodica Polytechnica and Acta Polytechnica Hungarica, as well as IEEE conference proceedings. His research spans theoretical advancements in fuzzy systems and practical applications in decision support and behavior modeling. Professor Kovács maintains active collaborations, most notably with Tamás Tompa and Mohd Aaqib Lone, reflecting his engagement with both theoretical development and practical implementation of intelligent systems. His work on knowledge base optimization has significant implications for creating more efficient expert systems that can operate effectively with limited rule sets. His laboratory resources appear to be centered around computational infrastructure for developing and testing fuzzy logic systems, with his personal website serving as the primary hub for research dissemination. Professor Kovács continues to advance the field of computational intelligence with recent publications extending through 2025, demonstrating his ongoing active research program.

Szabolcs TengelyView profile

Professor

• Number Theory
• Diophantine Equations
• Cryptography
• +5 more

Professor Szabolcs Tengely is a distinguished mathematician at the University of Debrecen's Faculty of Science and Technology, where he serves in the Institute of Mathematics. He maintains his office in the Mathematics and Earth Sciences building (Room M415) and holds regular office hours for students. His academic career spans numerous publications and significant contributions to number theory. Position: Professor Institution: University of Debrecen Faculty: Faculty of Science and Technology Department: Institute of Mathematics Professor Tengely's research primarily focuses on number theory with special emphasis on Diophantine equations, arithmetic progressions, and recurrence sequences. His work bridges pure mathematics with applications in cryptography and computational mathematics. He has made notable contributions to understanding the properties of Lucas sequences, Markoff-Rosenberger triples, and solutions to various Diophantine problems. An analysis of his recent publications reveals a strong focus on advanced Diophantine problems, particularly those involving higher power rational Diophantine tuples, cubic Diophantine equations related to the Mordell-Schinzel conjecture, and connections between number theory and combinatorial structures. His research often combines classical number theory techniques with modern computational approaches. Professor Tengely has supervised numerous graduate and undergraduate students through their thesis work, demonstrating his commitment to academic mentorship and the development of future mathematicians. His supervision spans topics in discrete mathematics, cryptography, and computational number theory, reflecting the breadth of his expertise.

Gergely NeuView profile

Academic

• Theoretical Machine Learning
• Reinforcement Learning Theory
• Online Optimization
• +3 more

Gergely Neu is a Research Assistant Professor in the Department of Information and Communication Technologies (DTIC) at Universitat Pompeu Fabra in Barcelona, Spain. He leads a research group focused on theoretical aspects of sequential decision making and machine learning. Dr. Neu's research primarily centers on theoretical machine learning with a focus on: Reinforcement Learning Theory Online Optimization Bandit Problems Statistical Learning Theory Generalization Bounds Algorithms with provable performance guarantees His work emphasizes developing algorithms with strong theoretical guarantees in both computational and statistical complexity while maintaining practical implementability. Dr. Neu's research program seeks to bridge the gap between theoretical foundations and practical applications of machine learning systems, particularly in sequential decision-making contexts where performance guarantees are crucial. Analysis of Dr. Neu's recent publications reveals a strong focus on advancing the theoretical understanding of reinforcement learning, bandit algorithms, and generalization in machine learning. His work often employs techniques from convex analysis, information theory, and regret analysis to derive novel theoretical results with practical implications. A notable trend in his recent work is the exploration of offline reinforcement learning, contextual bandits, and the development of tighter generalization bounds using innovative analytical approaches that connect online learning with PAC-style guarantees. Dr. Neu has received numerous prestigious awards and recognitions for his research contributions: ERC Starting Grant (2020) Bosch AI Young Researcher Award (2020) Google Faculty Research Award (2019) "la Caixa" Junior Leader fellowship (2018) ELLIS Scholar appointment (2021) Outstanding Reviewer Award from ICML'16 Blue Ribbon for Outstanding Reviewing from IJCAI'16 Dr. Neu actively mentors several PhD students and postdoctoral researchers, fostering the next generation of theoretical machine learning researchers. His ERC Starting Grant provides substantial funding for his research program, enabling him to pursue ambitious theoretical investigations while training early-career researchers. He has successfully placed alumni in prestigious positions including assistant professorships at TU Delft and Politecnico di Milano. Dr. Neu's research group operates within the vibrant AI and Machine Learning research community at Universitat Pompeu Fabra, collaborating closely with other researchers in the Department of Information and Communication Technologies and maintaining strong ties with the Statistics group at the Department of Economics and Business. The group participates actively in the international machine learning theory community, regularly contributing to top conferences such as COLT, NeurIPS, ICML, and ALT, where Dr. Neu has served in leadership roles including Program Chair for COLT'23.