جستجوی استاد راهنمای دانشگاهی

• Operations Research
• Mathematical Programming
• Fuzzy Optimization
• +۸ مورد دیگر

Maria Teresa León Mendoza is an Associate Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, Universitat de València. She is affiliated with the IARM (Image Analysis, Modelling and Retrieval) research group, reflecting her interdisciplinary work in data analysis and image processing. Doctorate: Universitat de València, 1992 Thesis: Un algoritmo primal para el problema continuo de programación semi-infinita lineal Supervisor: Dr. Enriqueta Vercher González Her research focuses on Operations Research , Mathematical Programming , and Fuzzy Optimization , with significant contributions to semi-infinite programming, data envelopment analysis (DEA), and portfolio selection under uncertainty. She has also applied mathematical methods to diverse areas such as image retrieval and musical tuning systems, demonstrating a broad interdisciplinary reach. Her publication record since 1992 shows a consistent focus on optimization under uncertainty, fuzzy methods for decision-making, and numerical algorithms. She frequently collaborates with researchers like Vicente Liern and Enriqueta Vercher, and her work is published in top journals such as European Journal of Operational Research and Fuzzy Sets and Systems . There is no mention of scientific awards in the provided sources. Maria Teresa León Mendoza has supervised at least one PhD student (Dr. Enriqueta Vercher González, who later became her supervisor, suggesting a long-standing academic collaboration), and she has been involved in multiple research projects, though specific grant details are not listed. Her work bridges theoretical optimization and practical applications in economics, finance, and engineering. She is actively involved in the IARM research group, which specializes in image analysis and retrieval, indicating ongoing research in machine learning and pattern recognition applications.

• Computational Chemistry
• Physical Chemistry
• Density Functional Theory
• +۹ مورد دیگر

Sergey Vyboyshchikov Preobrazhenskaya is an Associate Professor in the Department of Chemistry at the University of Girona, Spain. He is affiliated with the Institute of Computational Chemistry and Catalysis (IQCC) and leads research within the DiMoCat group, focusing on theoretical and computational approaches to chemical reactivity, solvation, and bonding analysis. His research interests span Computational Chemistry , Density Functional Theory , Solvation Theory , Atomic Charges , Energy Decomposition , and Machine Learning applications in chemistry . He develops efficient methods such as the Electronegativity-Equalization (ESE) approach for solvation energy and novel atomic charge schemes (ACP, I-ACP). His work bridges quantum chemistry with practical applications in catalysis and molecular modeling. His recent publications (2021–2023) show a strong trend toward integrating machine learning (particularly dense neural networks) with physical chemistry principles to create fast, accurate models for solvation free energies. These models leverage atomic charges and non-iterative algorithms, enabling high-throughput screening and simulation. His work also includes fundamental studies in confined quantum systems , vibrational theory , and main group element reactivity . He teaches courses including Numerical Methods and Programming, Chemical Kinetics, Nanotechnology, and Physical Chemistry Labs at both undergraduate and master’s levels. Contact Information: Email: sergey.vyboyshchikov@udg.edu Phone: +34-972-418362 Office: C3-106, Edifici Ciències, University of Girona

• Physical Chemistry
• Computational Chemistry
• Reaction Mechanisms
• +۴ مورد دیگر

Sergey Vyboyshchikov Preobrazhenskaya is an Associate Professor in the Department of Chemistry at the University of Girona , affiliated with the Institute of Computational Chemistry and Catalysis (IQCC) and the DiMoCat research group . His work bridges physical chemistry , computational chemistry , and catalysis , with a focus on solvent effects , density functional theory , and machine learning applications. Research interests include solvation theory (developing ESE methods for solvation energy), DFT (confined systems and catalysis), atomic charge schemes (ACP/I-ACP), energy decomposition (bonding analysis), and vibrational theory (proton/hydride motion). Recent publications highlight machine learning integration for solvation energy prediction and DFT-based mechanistic studies in molecular and solid-state systems. Teaching includes Numerical Methods and Programming (University of Barcelona and Girona), Chemical Kinetics , Nanotechnology , and Bonding Analysis courses. Programming expertise spans Fortran , Python , and UNIX shell scripting .

• Computer Science
• Artificial Intelligence
• Robotics
• +۶ مورد دیگر

Javier Montoyo Bojo is a Professor in the Department of Computer Science and Artificial Intelligence at the University of Alicante. He earned his Computer Science degree (1995) and PhD in Computer Science (2015) from the same institution. A member of the Robotics and Three-Dimensional Vision (RoViT) research group, he held the role of Deputy Director of Business Internships at the Higher Polytechnic School (2015-2021). His research spans Artificial Intelligence , 3D Vision , Image Processing , and Educational Technology . Key projects include GPU-accelerated RGB-D camera registration , 3D hand pose estimation , and assistive robotics for acquired brain injury patients. Over the last decade, his work has focused on educational reform under the ECTS system, particularly for Statistics and Software Development courses. Recent publications highlight advancements in gaze tracking (2023) and collaborative educational frameworks for sustainable water technology programs (2019). He has participated in 9 public research projects (2018-2023) as coordinator or collaborator, including initiatives in 3D object recognition and VR/AR assistant systems for autism/Phobias (2023-2026). Teaching contributions include 13 iterations of Hypermedia Programming II and 10 of Statistics for Computer Engineering. He has directed/co-directed 14 undergraduate/master's theses in the last 5 years. Former roles include academic coordination of business internships (2017-2021) and editorial contributions to Statistics education materials.

• Signal Processing
• Digital Communications
• Machine Learning Applications
• +۳ مورد دیگر

Rafael Boloix Tortosa is an Associate Professor in the Department of Signal Theory and Communications at the University of Seville's School of Engineering, where he leads significant research in complex-valued signal processing and its applications. His academic career spans over two decades with continuous involvement in national and international research projects. His research focuses on advancing complex-valued signal processing methodologies, particularly complex Gaussian processes, kernel methods, and widely linear estimation techniques. These theoretical advances are applied to diverse domains including medical imaging (especially burn wound analysis), wireless communications (UWB positioning, OFDM systems), and art conservation. His work bridges fundamental signal processing theory with practical engineering solutions. Professor Boloix Tortosa maintains an active publication record with significant contributions in top IEEE journals, particularly in signal processing and machine learning. His recent work (2025-2019) demonstrates continued innovation in complex-valued machine learning methods, while his earlier publications established foundational work in blind source separation and multiuser detection. He actively participates in multiple research projects including EU-funded initiatives like COURAGEOUS (counter-UAS solutions) and ARIADNE (agricultural drones), alongside national projects in medical imaging and communications. His research group has received consistent funding through the TIC-155 consolidation incentives. His laboratory work centers around the Signal Processing and Communications Research Group, where he develops both theoretical frameworks and practical implementations for signal processing challenges. The group maintains strong connections between theoretical advances and real-world applications across multiple engineering domains.

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

José Ramón Cano de Amo is a Professor in the Department of Computer Science at the University of Jaén, with affiliations to the Andalusian Interuniversity Institute for Data Science and Computational Intelligence and the research group Intelligent Systems and Data Mining. Education: PhD in Computer Science from the University of Granada (2004), thesis: 'Data reduction based on evolutionary instance selection for data mining' His research focuses on constrained clustering , data mining , and evolutionary algorithms , with emphasis on integrating monotonicity constraints , semi-supervised methods , and label distribution learning . Recent work explores hybrid optimization frameworks and multiobjective decomposition-based approaches. His publications (2012–2025) highlight a sustained focus on instance selection , prototype optimization , and monotonic classification , with applications in psychiatric emergency decision support and community sentiment analysis. Collaborative projects include the 2009 'Modelo predictivo colaborativo' for psychiatric diagnosis. Research Affiliations: Andalusian Interuniversity Institute for Data Science and Computational Intelligence Intelligent Systems and Data Mining research group

Sancho Salcedo-Sanzمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Wireless Communications
• +۴ مورد دیگر

Sancho Salcedo-Sanz is a Full Professor in the Department of Signal Theory and Communications at the University of Alcalá's Higher Polytechnic School. He serves as the Deputy Director of his department and is the Director of the GHEODE Research Group at the University of Alcalá (UAH). With extensive experience in academia and research, he has made significant contributions to the fields of signal processing, machine learning, and optimization algorithms. His educational background includes a Full Professor Accreditation (2013), Associate Professor Accreditation (2005), a Ph.D. in Telecommunication Engineering from Universidad Carlos III de Madrid (2002), and a Bachelor's degree in Physics from Universidad Complutense de Madrid (1998). Professor Salcedo-Sanz's research spans multiple domains within computational intelligence and signal processing. His work focuses on developing innovative optimization algorithms like the Coral Reefs Optimization algorithm, with applications in renewable energy forecasting, wireless communications, and environmental modeling. He has pioneered approaches that combine machine learning techniques with evolutionary computation to solve complex real-world problems across various domains including energy, telecommunications, and environmental science. His recent publications demonstrate a strong focus on practical applications of computational intelligence, with particular emphasis on solar radiation forecasting, wind power prediction, structural vibration control, and optimization of smart grid systems. The interdisciplinary nature of his work bridges engineering, computer science, and environmental applications. VI Premio Accenture en Economía y Gestión de la Innovación, Finalists, 2015 Premio Sacyr Hacemos lo Imposible, in the category of Proyectos Ciudadanos, 2011 Premio a la mejor noticia científica in the I Certamen de Divulgación Científica de la Universidad de Alcalá, 2011 Premio del Consejo Social de la Universidad de Alcalá, 2011 Premio Fundación 3M a la Innovación, in the category of Medio Ambiente, 2010 Premio Jóvenes Investigadores de la Universidad de Alcalá en CC. Experimentales, 2009 Professor Salcedo-Sanz leads the GHEODE Research Group at UAH, which focuses on Geospatial and High-dimensional Data Optimization and Engineering. His team works on cutting-edge research in computational intelligence, with particular emphasis on the development and application of novel metaheuristic algorithms to solve complex optimization problems in various domains including renewable energy, telecommunications, and environmental modeling. The group has secured numerous research projects and maintains strong collaborations with international institutions.

Álvaro Morenoمشاهده پروفایل

پژوهشگر

• Fluid Mechanics
• Aerodynamics
• Machine Learning
• +۵ مورد دیگر

Álvaro Moreno is a postdoctoral researcher at Universidad Carlos III de Madrid (UC3M), following a postdoctoral position at Massachusetts Institute of Technology (MIT) and a PhD in Physics of Fluids at Universiteit Twente. His academic training includes a Bachelor’s and Master’s in Aerospace Engineering from Universidad de Sevilla and Technische Universität München, respectively.

• Operations Research
• Intelligent Optimization
• Business Analytics
• +۶ مورد دیگر

Helena Ramalhinho-Lourenço is a Full Professor at Universitat Pompeu Fabra, specializing in Operations Research and Logistics. Her research focuses on metaheuristics, combinatorial optimization, and supply chain management. She holds a PhD from Cornell University. Education: PhD in Operations Research from Cornell University Research Interests: Dr. Ramalhinho-Lourenço explores advanced optimization techniques such as iterated local search and metaheuristics, applying them to logistics, scheduling, and supply chain challenges. Her work bridges theoretical frameworks with practical business analytics solutions. Publications: Her recent work emphasizes foundational contributions to metaheuristics (e.g., iterated local search frameworks) and stochastic models in e-commerce supply chains. She co-authored a chapter on heuristic methods in supply chain management for the Handbook of Heuristics . Advising & Grants: While no specific advisees or grants are listed, her academic role suggests active research leadership in optimization and logistics.

JOSE CARLOS CABALEIRO DOMINGUEZمشاهده پروفایل

استاد

• High Performance Computing
• Parallel Systems Architecture
• Parallel Algorithms
• +۷ مورد دیگر

Prof. José Carlos Cabaleiro Domínguez is a Full Professor at the Department of Electronics and Computing, University of Santiago de Compostela. He holds a BS (1989) and PhD (1994) in Physics from the same university. His academic journey includes roles as an associate professor at the University of A Coruña (1990–1994) and associate professor at US圣地亚哥 de Compostela until 2022. He is affiliated with the Center for Research in Intelligent Technologies (CiTIUS) and leads the ARQCOMP research group in Computer Architecture. His research focuses on high-performance computing, parallel systems architecture, LiDAR data processing, and cloud computing. Notable contributions include optimizing parallel algorithms for sparse matrices, developing frameworks like BigOPERA for big data, and advancing LiDAR-based applications in infrastructure mapping and autonomous systems. He has led over 20 national/international projects, including HiPerHC2DA (2023–2026) and TLIX2 (2017–2018). Prof. Cabaleiro’s work spans 150+ peer-reviewed publications, with recent emphases on digital forensics (e.g., Android private browsing analysis), graph-based LiDAR segmentation, and energy-efficient thread migration strategies for NUMA systems. His research integrates hardware performance analysis, parallel algorithm design, and real-world applications in geomatics, bioinformatics, and cybersecurity.

• High-performance computing
• Decimal arithmetic
• Parallel programming
• +۴ مورد دیگر

Alvaro Vazquez Alvarez is an Assistant Professor at the Higher Technical School of Engineering, University of Santiago de Compostela. He holds a PhD in Computer Engineering (2009) from the same institution, focusing on High-performance decimal floating point units under the supervision of Dr. Elisardo Antelo Suárez. His research interests span high-performance computing, decimal arithmetic hardware design, parallel programming models, and LiDAR data processing. His work emphasizes FPGA implementation of arithmetic units, numerical algorithms optimization, and compiler/runtime systems for parallel environments. He leads the CograDe Research Group (Computer Graphics and Data Engineering), exploring advanced topics in parallel computing and data engineering applications. Key contributions include universal MPI binding frameworks for new languages and efficient LiDAR point cloud processing algorithms. Notable technical expertise includes: digit-by-digit algorithms, redundant number systems, CORDIC-based computations, and multi-operand decimal arithmetic implementations. His publications bridge theoretical numerical methods with practical hardware/software co-design solutions. Current research focuses on extending parallel programming interfaces, improving decimal arithmetic precision in modern architectures, and applying advanced geometric processing techniques to 3D data challenges.

Ramón Martínez Rodríguez-Osorioمشاهده پروفایل

استاد

• Satellite Communications
• Antenna Systems
• Signal Processing
• +۴ مورد دیگر

Ramón Martínez Rodríguez-Osorio is a Full Professor at the Polytechnic University of Madrid's School of Telecommunications Engineering, Department of Signals, Systems and Radiocommunications. Born in Madrid (1975), he holds a Telecommunications Engineering degree (1999) and completed doctoral studies at UPM. He has been actively involved in research since 1999, focusing on satellite communications, antenna array systems, and machine learning applications in communication networks. Education: Telecommunications Engineering Degree, Polytechnic University of Madrid (1999) PhD in Signals, Systems, and Radiocommunications (UPM) Research Interests: His work centers on satellite communication systems, including adaptive beamforming, antenna array calibration, and interference mitigation. He also explores machine learning for resource management in 5G/6G networks and hybrid beamforming algorithms for VHTS systems. Recent projects include constellation design for non-terrestrial networks and rain attenuation prediction using LSTM networks. Key Projects: European COST project and national/international grants (CICYT, FEDER, INDRA) FUTURE-RADIO, PROMETEO, STAR, and FAST initiatives Development of the HispaSim satellite link optimization tool Awards: Fundación Telefónica Award (2000) for best final degree project Teaching & Innovation: Contributed to hands-on educational projects like antenna design for CubeSats and MIMO-Testbed systems. Co-authored books on modeling/simulation for communication systems and 3G UMTS technologies. Supervised multiple academic activities including thesis and MOOC development. Labs & Teams: Leads research in the LEHA (Laboratorio de Enlace Hiperfrecuencia y Antenas) lab, focusing on satellite link analysis, antenna measurement systems, and 5G terminal testing. Collaborates with industry partners like INDRA on applied research.

Federico Thomasمشاهده پروفایل

استاد پژوهش

• Robotics
• Kinematics
• Distance Geometry
• +۶ مورد دیگر

Federico Thomas is a Professor of Research at the Spanish National Research Council (CSIC), based at the Institut de Robótica i Informàtica Industrial (IRI), affiliated with the Technical University of Catalonia (UPC), Barcelona, Spain. He has held visiting and invited professorships at the University of Massachusetts, Oxford University, École Centrale de Nantes, and the University of Ferrara. Telecommunications Engineering Degree, 1984, UPC Ph.D. in Computer Science, 1988, UPC NATO Postdoctoral Fellow, University of Massachusetts, 1991 His research lies at the intersection of Geometry and Kinematics , with strong applications in Robotics , Computer Vision , and Computer Graphics . He specializes in Distance Geometry and Clifford Algebras , developing mathematical frameworks for solving complex robotic motion and configuration problems. His work enables precise modeling of mechanisms such as lobster-inspired arms and parallel robots. His recent publications (2022–2025) reveal a deep engagement with geometric computation, focusing on rotation matrices, sphere tangents, ellipse intersections, and singularity analysis in parallel robots. These works consistently apply advanced algebraic and geometric methods to practical robotic challenges, demonstrating a trajectory of theoretical rigor with engineering relevance. His scientific honors include the NATO Postdoctoral Fellowship. He has served as an Associate Editor for IEEE Transactions on Robotics (2005–2009, and currently), ASME Journal of Mechanisms and Robotics (2009–2012, and currently), and The International Journal of Mechanics and Control . Federico Thomas was the former director of IRI. He advises students in robotics and geometry, as indicated by the 'Students' section on his homepage. He has not received major grants explicitly listed, but his sustained publication record and editorial roles suggest continuous research support. His 'Art and Science Tapas' initiative reflects an interdisciplinary outreach effort, blending history, art, and science. He leads research activities within the IRI, particularly in computational robotics and geometric methods. His team likely includes collaborators such as J.M. Porta, S. Sarabandi, and B. Bongardt, with whom he co-authors frequently. The 'Kinematics Virtual Library' and 'Tapas' projects suggest an educational and cultural component to his lab’s mission.

Sebastian Willمشاهده پروفایل

استاد

• Computational RNA structure prediction
• RNA-RNA interactions
• Folding dynamics
• +۳ مورد دیگر

Sebastian Will is a Full Professor of Bioinformatics at École Polytechnique IP Paris since 2020. Previously held positions include: Researcher with Ivo Hofacker at TBI, University of Vienna Researcher with Peter Stadler at Bioinformatics group, University of Leipzig Akademischer Rat at Bioinformatics group, University of Freiburg (2005-2012) Postdoc with Bonnie Berger at MIT's CSAIL (2010-2011) His research focuses on: RNA structure prediction algorithms Pseudoknot modeling RNA-RNA interaction analysis Structure design with Infrared framework Multi-target RNA design Integration of experimental data Ensemble-based sparsification Publication trends show concentrated research in: RNA structure prediction techniques Algorithm development Dynamic programming optimization Tree decomposition applications Stochastic sampling methods Multi-objective design Contributed extensively to software development including: LocARNA 2.0 Infrared framework SparseMFEFold BiAlign RNAPOND Pankov LocARNA-P

• Operations Research
• Mathematical Programming
• Project Scheduling
• +۷ مورد دیگر

Francisco Felipe Ballestín González is an Associate Professor in the Department of Mathematics at the Faculty of Economics, University of Valencia. His research is centered on Operations Research, with a focus on resource-constrained project scheduling, optimization models, and heuristic algorithms. University: University of Valencia School: Faculty of Economics Department: Department of Mathematics Email: francisco.ballestin@uv.es His research interests include Operations Research, Mathematical Programming, Project Scheduling, Heuristics, Metaheuristics, and Optimization. He is a member of the OMMEE research group, dedicated to Optimization and Mathematical Models for Economics and Business. The 15 most recent publications analyzed reveal a consistent focus on project scheduling under resource constraints, with applications in production, healthcare, and logistics. His work frequently involves hybrid and evolutionary algorithms, justification techniques, and modeling of preemption and time lags. He has contributed significantly to the theoretical and practical understanding of RCPSP variants. No scientific awards are mentioned in the provided text. There is no information available about student advising or research grants. However, his extensive collaboration with researchers such as Vicente Valls, Sacramento Quintanilla, and Roel Leus indicates active research engagement. He is affiliated with the OMMEE research group, which focuses on developing mathematical models for economic and business applications, particularly in scheduling and optimization.