جستجوی استاد راهنمای دانشگاهی

• Construction Technology
• Construction Management
• Civil Engineering
• +۳ مورد دیگر

Євген Юрійович Антипенко is a Professor at the Department of Design within the Faculty of Architecture and Design at Zaporizhzhia Polytechnic National University. He joined the university in 2014 and holds the academic title of Professor with a Doctor of Technical Sciences degree. His educational background includes a specialist diploma with honors in Industrial and Civil Construction from Zaporizhzhia State Engineering Academy, where he qualified as a Civil Engineer. He further advanced his academic credentials with a Candidate's thesis in Project and Program Management (2004) and a Doctoral dissertation in Technology and Organization of Industrial and Civil Construction (2011). Professor Antipenko's research focuses on construction technology, construction management, civil engineering, and resource optimization. His work examines the technological and organizational aspects of construction production, as well as management and optimization of construction industry enterprises. His publications demonstrate expertise in resource allocation, project scheduling, and construction planning methodologies. Certificate from Zaporizhzhia Regional State Administration for significant contribution to science and technology (2010) Scholarship from Cabinet of Ministers of Ukraine for young scientists (2011-2012) Full Member of Academy of Construction of Ukraine (2014) Professor Antipenko actively participates in national and international conferences, including the International Scientific and Practical Conference 'Finance of Ukraine' and the All-Ukrainian Scientific-Practical Conference on Energy-Saving Technologies in Construction. His professional expertise spans Ukrainian, English, and Russian languages, facilitating international collaboration in his field.

• Graph algorithms
• Combinatorial optimization
• Artificial intelligence
• +۲ مورد دیگر

Матвій Борисович Ільяшенко is an Associate Professor at the Department of Computer Systems and Networks, Faculty of Computer Science and Technologies, Zaporizhzhia Polytechnic National University. He holds a Ph.D. in Computer Engineering from the Institute of Modeling Problems in Energy (NAS of Ukraine, Kyiv) and graduated from ZNTU with honors in 2005. Ph.D. in Computer Engineering (2008) Specialty: Computer Systems and Networks (2005) His research focuses on graph algorithms, combinatorial optimization, and artificial intelligence systems. Recent publications explore blockchain integration in digital twins, neuroevolution for anomaly detection, and spiking neural network synthesis with probabilistic coding. His work spans distributed computing, medical diagnostics, and industrial cybersecurity. Key trends in his publications include: 1) Advanced graph isomorphism algorithms for resource allocation; 2) Neuroevolutionary techniques in time series analysis; 3) Blockchain-enhanced digital twin architectures; 4) Stochastic programming for big data reduction; 5) Probabilistic neural models with neuropattern mechanisms. He teaches Fundamentals of optimization of computer systems and networks and has contributed to specialized digital systems semantic analysis, GRID network resource reservation, and communication channel efficiency optimization. His work bridges theoretical algorithms with practical implementations in industrial and medical domains.

Radu-Lucian LUPŞAمشاهده پروفایل

مدرس

• Image Processing
• Computer Graphics
• Graph Algorithms
• +۳ مورد دیگر

Radu-Lucian LUPŞA serves as a Lecturer at Babeş-Bolyai University's Faculty of Mathematics and Computer Science in Romania. His academic career spans over two decades with a consistent focus on computer science, particularly in image processing and graph algorithms. His teaching responsibilities include courses on Graph Algorithms and Parallel and Distributed Programming, demonstrating his expertise across theoretical and applied computer science domains. LUPŞA's research interests center around image processing, with significant contributions to compression methods, dithering algorithms, and multiresolution analysis. His work bridges theoretical computer science with practical applications in graphics and healthcare optimization. The evolution of his research shows a progression from fundamental image compression techniques in the late 1990s to more specialized applications in medical imaging and transportation logistics by the late 2000s. Analysis of his publications reveals strong expertise in computational geometry, algorithm design, and optimization problems. His research demonstrates consistent application of mathematical rigor to practical computing challenges, particularly in the areas of image representation and processing. The interdisciplinary nature of his work is evident in publications spanning computer graphics, healthcare logistics, and mathematical optimization. LUPŞA completed his PhD in 2006 with the dissertation titled Contributions to the analysis, processing and representation of images , which serves as a foundation for his subsequent research trajectory. His academic output shows a period of high productivity between 1996-2008 with publications in respected venues including IEEE conferences and academic journals. As an educator, LUPŞA has developed comprehensive teaching materials for graph algorithms, including lecture notes, practical assignments, and evaluation methods. His teaching approach emphasizes both theoretical foundations and practical implementation, as evidenced by the detailed laboratory assignments and programming tasks he has designed for students.

Mihaela Elena Breabanمشاهده پروفایل

استاد

• Evolutionary Computation
• Machine Learning
• Big Data Analytics
• +۵ مورد دیگر

Mihaela Elena Breaban serves as Professor and Director of the Doctoral School at the Faculty of Computer Science, Alexandru Ioan Cuza University of Iasi, Romania. Her academic leadership extends to coordinating doctoral studies while maintaining active research in computational intelligence and data analysis. She holds office C416 at General Berthelot 16, 700483 - Iasi, and maintains international research collaborations, notably as a member of the Data Science & Soft Computing Group at Goldsmiths, University of London. Her research spans Evolutionary Computation (as part of ECODAM research group), Machine Learning and Big Data Analytics , Databases , and Constraint satisfaction . Breaban's work focuses on developing evolutionary algorithms for projection pursuit, clustering techniques, and optimization problems. Her methodological contributions include PSO-kMeans hybridization, nonlinear projection pursuit for outlier detection, and multi-objective evolutionary approaches for feature selection. These techniques have found applications in medical imaging, real estate dynamics modeling, and vehicle routing problems. Analysis of her publication record reveals a consistent trajectory from foundational work in evolutionary algorithms and constraint satisfaction toward increasingly sophisticated applications in medical diagnostics and big data analytics. Her recent publications demonstrate a strong emphasis on deep learning approaches for brain tumor segmentation and lung disease detection from CT scans, representing a significant shift toward biomedical applications of computational intelligence techniques. Scientific recognition includes: Winner of the ImageCLEFmed Tuberculosis competition As Director of the Doctoral School, Professor Breaban oversees doctoral education across the Faculty of Computer Science. Her teaching portfolio spans undergraduate and graduate levels, including Probability and Statistics (Info 1), Databases (Info 2), Genetic Algorithms (Info 3), Nature-inspired Optimization Models (Master OC), Data Mining (Master OC), and Big Data Analytics (Master ISS, OC, SD). While specific grant information isn't detailed in available sources, her international collaborations and competition wins suggest successful research funding. She actively participates in research communities through membership in the ECODAM research group at the Faculty of Computer Science, Iasi, and the Data Science & Soft Computing Group at Goldsmiths, University of London. These affiliations facilitate cross-institutional research on evolutionary computation and data science applications.

Madalina Raschipمشاهده پروفایل

دانشیار

• Constraint satisfaction
• Evolutionary Computation
• Hybrid metaheuristics
• +۴ مورد دیگر

Madalina Raschip is an Associate Professor at the Faculty of Computer Science, Alexandru Ioan Cuza University of Iasi , Romania. Her academic work spans constraint satisfaction, evolutionary computation, hybrid metaheuristics, and data mining. She earned her PhD at the same university and completed post-doctoral studies at the University of Neuchatel, Switzerland, supported by a Sciex Fellowship. Research Interests : Constraint satisfaction, evolutionary algorithms, hybrid optimization, data mining, graph neural networks, and automated algorithm selection. Teaching : Offers courses in Artificial Intelligence, Data Structures, Deep Learning in NLP, and Experimental Analysis of Algorithms. Publications : Focus on hybrid metaheuristics, constraint programming, sorting networks, and biomedical applications. Her work includes collaborations on medical data analysis, ant colony systems, and SAT solving. Awards : Sciex Fellowship for postdoctoral research. Research Groups : Member of the Evolutionary Computing Research Group and DECO (Data Engineering for Constraints Optimization).

Adrian Marius DEACONUمشاهده پروفایل

دانشیار

• Combinatorial optimization
• Network flow analysis
• Inverse problems in networks
• +۵ مورد دیگر

Adrian Marius DEACONU is an Associate Professor at the Department of Mathematics and Computer Science, Faculty of Mathematics and Computer Science, University of Brașov. His research focuses on combinatorial optimization, network flow analysis, inverse problems in networks, graph theory, and algorithm design with applications in transportation networks, photovoltaic systems, disaster logistics, and computational epidemiology. Recent work includes developing GPU-accelerated algorithms for maximum flow problems, fuzzy optimization approaches for disaster relief logistics, and parameter estimation methods for photovoltaic cells. He has contributed to theoretical advancements in inverse flow problems and pathfinding algorithms under constraints such as network losses and adversarial disruptions. His publications span diverse applications including vaccination efficacy modeling, robust vehicle routing under attacks, and geomagnetic map construction using inverse methods. He maintains an active research agenda in network optimization with a focus on bridging theoretical mathematics and practical engineering solutions.

• Fractal Geometry
• Fixed Point Theory
• Iterated Function Systems
• +۴ مورد دیگر

Cristina Maria PĂCURAR is an Assistant Professor at the Department of Mathematics and Computer Science, Faculty of Mathematics and Computer Science, University of Pitesti. Her research focuses on fractal geometry, fixed point theory, and iterated function systems with applications in epidemiology and optimization. She has published extensively on topics such as fractal interpolation, contraction mappings, and mathematical modeling of disease spread. Research interests include: Fractal interpolation schemes and their applications Fixed point theory in metric spaces Optimization algorithms for tourism and pandemic scenarios Analysis of complex systems using fractal dimensions Her notable works include analyzing the spread of COVID-19 using fractal methods and optimizing tourist routes during pandemics. She has also contributed to post-quantum cryptography and hash-based signature schemes. Publications highlight interdisciplinary approaches, combining pure mathematics with real-world applications in health and tourism. Ongoing work explores new directions in fixed point theory and fractal interpolation techniques.

Lehel Csatóمشاهده پروفایل

استاد

• Machine Learning
• Probabilistic Robotics
• Data Analysis
• +۴ مورد دیگر

Lehel Csató is a Professor at the Faculty of Mathematics and Informatics of Babeș-Bolyai University in Cluj-Napoca, Romania. His academic journey includes a PhD from Aston University and a postdoctoral position at the Max Planck Institute for Biological Cybernetics. He specializes in Machine Learning, Probabilistic Robotics, and Nonparametric Bayesian Methods, with a focus on Gaussian Processes and their applications in data analysis and robotics. Teaching includes courses in Logic and Functional Programming (Hungarian), Numerical Modelling in Data Analysis (English), and Artificial Intelligence (Hungarian). His research emphasizes sparse Gaussian Process approximations and their use in reinforcement learning, robotics, and medical image analysis. He contributed to the development of the Sparse Online Gaussian Process toolbox. Publications span topics from deep learning architectures to optimization techniques in neural networks. He has organized the ClujUAV drone navigation competition and collaborated on interdisciplinary projects involving robotics, vision, and medical data analysis.

Dorin Andricaمشاهده پروفایل

استاد

• Critical Point Theory
• Nonlinear Analysis
• Approximation Theory
• +۳ مورد دیگر

Dorin Andrica is a Professor at the Faculty of Mathematics and Computer Science at Babeș-Bolyai University in Cluj-Napoca, Romania. With a career spanning over three decades at the same institution, his work bridges multiple disciplines including Critical Point Theory , Nonlinear Analysis , Approximation Theory , and Number Theory , particularly focusing on Elementary Mathematics and Olympiad/Contest problems . PhD in Mathematics (Babeș-Bolyai University, 1992) Two decades of research grants, including the European Network 'Geometric Analysis' (2001–2004) and DAAD Fellowship (1993). His publications often explore Diophantine equations , prime gaps , and geometric inequalities , with notable contributions like Andrica's Conjecture — a famous unsolved problem in number theory. Recent works highlight dynamic geometry, cyclotomic polynomials, and advanced combinatorial identities, reflecting his interdisciplinary approach. Andrica has co-organized the European Summer School 'Spaces with singularities and monopols' (Cluj-Napoca, 1999) and serves on editorial boards of multiple journals, including Studia Univ. 'Babeș-Bolyai'-Mathematica and Gazeta Matematica . His Erdős number is 2.

Isabela Dramnescمشاهده پروفایل

دانشیار

• Automated Reasoning
• Logic
• Mathematical Theory Exploration
• +۱ مورد دیگر

Isabela Dramnesc is an Associate Professor at the West University of Timisoara , Faculty of Mathematics and Computer Science, Department of Computer Science. Her research focuses on Automated Reasoning , Logic , Mathematical Theory Exploration , and Algorithm Synthesis . EU Erasmus+ projects: ARC (2019-2022, KA203 Strategic Partnerships) and AiRobo (2023-2026, KA220 Cooperation Partnerships). Teaching: Graph Theory and Combinatorics , Automated Theorem Proving , Algorithm Synthesis , and Logic Programming at bachelor's and master's levels. Publications: Two peer-reviewed articles in the Journal of Symbolic Computation (2015, 2019) on algorithm synthesis and automated reasoning for data structures. Contact: isabela.dramnesc@e-uvt.ro

• Discrete Optimization
• Graph Theory
• Fuzzy Logic Systems
• +۴ مورد دیگر

Elina Valentynivna Tereshchenko serves as Associate Professor and Head of the Department of Systems Analysis and Computational Mathematics at Zaporizhzhia Polytechnic National University's Faculty of Computer Science and Technologies. Holding a Candidate of Physical and Mathematical Sciences degree (equivalent to PhD), she graduated from Dnipropetrovsk State University in 1992 with specialization in 'automatics and control in technical systems'. Her research spans discrete optimization , graph theory , and fuzzy logic systems , with recent publications focusing on ontology engineering, team formation algorithms, and agricultural data analysis. Current projects include ontology control systems in Neo4j (2025), competition-based team optimization models (2024), and sunflower cultivation ontologies for Ukrainian agricultural contexts. Her 15 most recent publications (2022-2025) reveal strong interdisciplinary work connecting computer science with agricultural informatics, cybersecurity, and economic modeling. Key trends include graph-theoretic formulations for team formation, fuzzy logic applications in access control, and optimization under resource uncertainty. Professional affiliations include: ORCID: 0000-0001-6207-8071 Scopus: 57202151469 Google Scholar: https://scholar.google.com/citations?user=DwEYAVcAAAAJ&hl=uk She teaches core subjects including discrete mathematics , optimization methods , algorithm theory , and decision-making theory . Her departmental leadership involves developing educational programs such as the bachelor's curriculum 'Intelligent Technologies and Decision Making in Complex Systems' (2021). Research infrastructure includes work on the HELIANTHUS ontology for sunflower cultivation and analytics modules for war damage assessment systems. Current projects integrate graph theory with competitive dynamics for high-performance team design.