جستجوی استاد راهنمای دانشگاهی

Andrea Ianiro is a Full Professor in the Aerospace Engineering Department at Universidad Carlos III de Madrid (UC3M), where he leads research in fluid dynamics, turbulence, and heat transfer. His work bridges experimental techniques and machine learning applications for flow analysis and control. He serves as Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) and directs the EFM Lab (Experimental Fluid Mechanics Laboratory) at UC3M. Professor Ianiro's research focuses on turbulence characterization, boundary layer flows, and the application of machine learning to fluid mechanics problems. His work spans experimental techniques including Particle Image Velocimetry (PIV), infrared thermography, and advanced data processing methods. Recent research emphasizes data-driven approaches for flow field reconstruction, turbulence control, and heat transfer optimization in wall-bounded flows. His projects often combine theoretical, experimental, and computational approaches to address complex fluid mechanics challenges. The analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional fluid mechanics. His work increasingly focuses on using deep learning techniques (particularly CNNs and GANs) for flow field prediction from limited measurements, developing meshless computational methods for flow analysis, and applying optimization techniques (including genetic algorithms) to heat transfer enhancement. His research maintains a strong experimental foundation while embracing data-driven approaches to tackle turbulence modeling challenges. Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) Professor Ianiro leads multiple significant research projects including SPANDRELS (SParse AND paRsimonious Event-based fLow Sensing, 2025-2030), HumanIC (Human-Centric Indoor Climate for Healthcare Facilities, 2024-2027), and EXCALIBUR (Extraction of machine learning strategies for turbulent flow control, 2023-2026). His work has attracted funding from the European Commission, Spanish National Research Agency, and industry partners including Airbus. He has supervised numerous theses on topics including AI-based sensing of turbulent flows, convective heat transfer control, and turbulent boundary layers. At UC3M, Professor Ianiro directs the Experimental Fluid Mechanics Laboratory (EFM Lab), which focuses on advanced measurement techniques for fluid flow and heat transfer characterization. The lab specializes in PIV/PTV techniques, infrared thermography, and the development of novel experimental approaches for turbulence research. Current research directions include machine learning applications for flow field reconstruction, plasma-based flow control, and heat transfer optimization in complex flow configurations.

• Fluid Dynamics
• Turbulence
• Heat Transfer
• +۶ مورد دیگر

Francisco Manuel Bernal Martínez is an Associate Professor in the Department of Mathematics at Carlos III University of Madrid. His research focuses on numerical methods, partial differential equations, and computational mathematics, with applications in industrial engineering and materials science. He leads projects such as 'Financiación adicional 5º año (2022)' and collaborates on initiatives like 'Clustering Automático de Comportamientos de Invertebrados en Libertad mediante Imagen 3D.' His work emphasizes domain decomposition algorithms, radial basis functions, and stochastic control problems. He has advised at least one PhD thesis and holds grants from regional and national funding bodies. Key research interests include meshless methods, probabilistic domain decomposition, and uncertainty quantification in energy systems. Recent publications highlight advancements in hybrid algorithms for large-scale PDEs and volatility modeling. Bernal Martínez actively participates in academic networks and has presented at international conferences on computational methods and industrial mathematics.

• Numerical Methods
• Partial Differential Equations
• Computational Mathematics
• +۳ مورد دیگر

Santiago Badia is a Full Professor of Computational Science and Engineering at Universitat Politècnica de Catalunya (UPC), holding an adjoint researcher position at the International Center for Numerical Methods in Engineering (CIMNE). He leads the Large Scale Scientific Computing (LSSC) group at CIMNE, focusing on finite element methods, numerical analysis, and high-performance computing. His research emphasizes fluid dynamics, multiphysics problems, and scalable solvers for large-scale systems. Previously, he worked at Politecnico di Milano and Sandia National Labs. He developed the FEMPAR software framework, a parallel finite element tool for PDE simulations, achieving landmark scalability (e.g., 60 billion unknowns on 458,672 cores). FEMPAR is recognized in the High-Q Club of European codes. His expertise includes discontinuous Galerkin methods, XFEM, and domain decomposition preconditioners. Research interests span metal additive manufacturing, superconductor devices, and nuclear engineering applications. Awards include FEMPAR's High-Q Club inclusion. He advises PhD and MSc students (e.g., Jesus Bonilla, Eric Neiva, Marc Olm) and has open positions in postdoc/PhD levels. His team includes researchers like Javier Principe and Alberto Martín. Ongoing projects involve advancing parallel algorithms, multiphysics simulations, and software scalability for exascale computing.

• Computational Science and Engineering
• Numerical Analysis
• Finite Element Methods
• +۳ مورد دیگر

Alexandre Santos Francisco is Professor of Fluid Mechanics at Fluminense Federal University (Universidade Federal Fluminense) in Brazil, holding a PhD in Nuclear Engineering. He will soon serve as Visiting Professor at the ERC Advanced Grant project DyCon under Prof. Enrique Zuazua (FAU, University of Deusto, and Universidad Autónoma de Madrid), focusing on computational mathematics and porous media research. His academic credentials include: PhD in Nuclear Engineering (1994–2000), Federal University of Rio de Janeiro Master's Degree in Nuclear Engineering (1990–1993), Federal University of Rio de Janeiro Bachelor's Degree in Mechanical Engineering (1984–1990), Federal University of Rio de Janeiro Internship at Petrobras (1988) Francisco's research centers on porous media, computational mathematics, and computational fluid mechanics, with emphasis on numerical methods for heterogeneous media flows. His work bridges nuclear engineering safety, environmental contaminant transport, and industrial process optimization through advanced simulation techniques. Analysis of his 15 most recent publications reveals dominant trends in multiscale modeling for porous media, parallel computing implementations, and reliability assessment in nuclear systems. Key application areas include waste disposal simulation, steam generator integrity, and multiphase industrial processes, consistently integrating mathematical rigor with engineering challenges. No scientific awards are documented in the provided materials. While student advisement details are absent, his upcoming ERC Advanced Grant project DyCon involvement signifies active grant-funded research. The project will expand his work on mathematical control theory for complex systems under Prof. Zuazua's supervision. He collaborates internationally through the ERC DyCon project, leveraging expertise in numerical methods to address multiscale dynamics in porous media, with future work likely advancing computational techniques for energy and environmental applications.

• porous media
• computational mathematics
• computational fluid mechanics
• +۳ مورد دیگر

Elisabet Golobardes Ribé is a Full Professor at La Salle Digital Engineering School, Ramon Llull University, where she is affiliated with the Engineering Department. She leads and participates in the Research Group on Smart Society and is actively involved in major international research collaborations, particularly with CERN's LHCb experiment. Her research spans artificial intelligence, machine learning, data science, and high-energy physics, focusing on analyzing particle physics data and detecting disinformation in digital societies. She applies computational techniques to fundamental physics problems, including B-meson decays and searches for physics beyond the Standard Model. The body of her recent publications demonstrates a strong trend in experimental particle physics, with numerous contributions to high-impact journals such as the Journal of High Energy Physics and Physical Review D. These works involve amplitude analyses, angular distributions, branching fraction measurements, and constraints on new physics, primarily through the LHCb and ATLAS collaborations. She has secured competitive research funding, including EU grants such as FLAIR and national grants from AGAUR, and serves as Principal Investigator on projects related to AI readiness and disinformation detection. Her work bridges engineering, computer science, and fundamental physics. Notable research projects include: FLAIR (Fostering Learners' AI Readiness) – European Commission-funded project (2024–2026) LHCb-RTA-URL – CERN data challenges and new physics searches (2023–2026) Rapid response for detecting disinformation flows – Internal grant (2021) Research Group on Smart Society – AGAUR-funded network (2022–2025) She is a member of the LHCb collaboration and contributes to large-scale data analysis in high-energy physics experiments. Her work emphasizes computational methods in science and societal applications of AI.

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

Matteo Giacomini is an Associate Professor of Computational Engineering at Universitat Politècnica de Catalunya (UPC), affiliated with the Laboratori de Càlcul Numèric (LaCàN). He is also an affiliated researcher at CIMNE (Severo Ochoa Excellence Centre) and affiliated faculty at IMTech (Institute of Mathematics of UPC-BarcelonaTech). His research focuses on numerical methods for PDEs, including high-order and low-order methods, error estimation, and reduced-order modeling. Applications span computational fluid dynamics, solid mechanics, image segmentation, and industrial sustainability. Education : PhD in Applied Mathematics, École Polytechnique (2016) MSc & BSc in Mathematical Engineering, Politecnico di Milano (2013 & 2010) Research Interests : High-order methods: finite element, discontinuous Galerkin Error & adaptivity: a posteriori estimates, mesh adaptation Dimensionality reduction: reduced order models, scientific ML PDE-constrained optimization: topology/shape optimization Software development: open-source CSE tools Recent Work Trends : Recent articles emphasize multi-fidelity surrogate modeling, domain decomposition for parametric PDEs, and robust finite volume methods for incompressible/compressible flows. He also contributes to HDG method implementations (e.g., HDGlab) and industrial applications of CFD. Labs & Affiliations : LaCàN - UPC CIMNE - Innovative Algorithms & Credible Data-Driven Models groups IMTech - Mathematical Modelling research line

• Computational Engineering
• Numerical Methods for PDEs
• High-order Methods
• +۶ مورد دیگر

María Concepción Bermúdez Edo serves as a State Researcher at the Polytechnic University of Cartagena's Faculty of Sciences, maintaining an active research career from 1999 to 2025. Her work centers on advancing computational mathematics through rigorous numerical analysis methodologies. Her primary research explores iterative solution techniques for nonlinear systems, with specialization in Newton-type methods, wavelet applications for Maxwell's equations, and convergence analysis of high-order algorithms. This includes significant contributions to k-step iterative schemes, damped parameter optimization, and non-Fréchet differentiable operator handling. Analysis of her 13 journal publications reveals consistent focus on algorithmic efficiency and mathematical foundations across computational electromagnetics and differential equations. The 2017 wavelets overview demonstrates cross-disciplinary integration of numerical analysis with electromagnetic theory, while her 2009 mentoring publication highlights engagement in faculty development initiatives within the Faculty of Sciences.

• Numerical Analysis
• Computational Mathematics
• Iterative Methods
• +۳ مورد دیگر

Stefano Discetti is a Full Professor in the Department of Aerospace Engineering at Universidad Carlos III de Madrid, leading the Aerospace Engineering Research Group. His work bridges experimental fluid dynamics, machine learning, and flow control. Key research themes include turbulence characterization, heat transfer enhancement, and advanced measurement techniques like PIV/PTV. He has developed data-driven methods for flow estimation, sensor placement optimization, and physics-informed neural networks. Research Trends from his publications show a focus on: Machine learning for turbulent flow reconstruction (GANs, CNNs, KNN) Non-intrusive sensing from wall measurements Manifold learning and reduced-order modeling Heat transfer control via plasma actuators and passive structures Time-resolved diagnostics using hybrid experimental/numerical approaches Projects include principal roles in EU and national grants like NEXTFLOW (2021-2026) and EXCALIBUR (2023-2026), with industry collaborations at Airbus and TU Delft.

• Aerodynamics
• Turbulent Flow Analysis
• Particle Image Velocimetry (PIV)
• +۹ مورد دیگر

Francesc Arandiga Llau is a Professor in the Department of Mathematics at the Faculty of Mathematics, Universitat de València, Spain. He is affiliated with the ANIMS (Numerical Analysis, Images, Multiresolution and Simulation) research group, where he conducts research in applied mathematics with a focus on numerical methods and their applications. Education: PhD from Universitat de València (1992), thesis on operator approximation and spectral radius continuity, supervised by Dr. Vicent Caselles Costa. His research interests center on Numerical Analysis , Approximation Theory , and Multiresolution Methods , with significant contributions to WENO schemes , nonlinear interpolation , and image and signal compression . His work often bridges theoretical developments with practical implementations in computational mathematics and engineering. He has made notable advances in the stability, accuracy, and adaptability of reconstruction techniques for piecewise smooth and discontinuous functions. The analysis of his recent publications reveals a consistent focus on high-order numerical methods, particularly in the context of image processing and data compression . His work leverages multiresolution analysis , radial basis functions , and adaptive interpolation to improve accuracy and efficiency. Themes across his articles include monotonicity preservation, error control, and the design of nonlinear schemes that avoid spurious oscillations near discontinuities. There are no scientific awards explicitly mentioned in the provided text. Francesc Arandiga has extensive collaborative research, particularly with scholars such as Rosa Donat, Dionisio F. Yáñez, Pep Mulet, and Antonio Baeza. His work has been supported through various research projects, though specific grants are not detailed in the text. He has advised students, including those who have completed theses under his supervision, although a full list is not provided. He is a key member of the ANIMS research group, which focuses on Numerical Analysis, Images, Multiresolution, and Simulation. This team works on developing and analyzing advanced computational methods for scientific and engineering applications, particularly in the areas of data representation, image processing, and numerical solutions to differential equations.

• Numerical Analysis
• Approximation Theory
• Multiresolution Analysis
• +۷ مورد دیگر

Cedric Martinez Campos is an Assistant Professor in the Department of Mathematics at the Faculty of Mathematics, University of Valencia. He is a member of the Geometric Analysis Group U.V. (GAGUV) and specializes in Geometry and Topology. His academic work is deeply rooted in geometric mechanics and mathematical physics. Research Interests: Geometric methods in classical and field theories Differential geometry and topology Variational calculus and optimal control Geometric numerical integration (variational integrators) Hamilton-Jacobi theory Constrained and dissipative systems His recent publications reveal a strong focus on applying advanced geometric structures—such as contact, multisymplectic, and k-contact geometries—to formulate and analyze mechanical and field theories, especially those involving dissipation and constraints. He develops high-order numerical methods grounded in variational principles, contributing to both theoretical and computational mechanics. Scientific Contributions: Over 16 publications in major mathematical physics journals Significant work on higher-order field theories and Lagrangian submanifolds Development of geometric integrators for optimal control Contributions to the Hamilton-Jacobi formalism in field theory He has collaborated extensively with leading figures such as Manuel de León, David Martín de Diego, and J. M. Sanz-Serna. His research has been cited over 90 times, reflecting its relevance in geometric mechanics. He advises no listed students yet but is actively involved in high-level research. He is part of ongoing efforts to extend geometric frameworks to non-conservative and singular systems, indicating a forward-looking research agenda in mathematical physics.

• Geometry and Topology
• Geometric Analysis
• Differential Geometry
• +۷ مورد دیگر

Adeline De Montlaur is an Associate Professor at the Universitat Politècnica de Catalunya (UPC) within the School of Telecommunications and Aerospace Engineering of Castelldefels (EETAC), affiliated with the Department of Physics. She leads research in numerical methods for fluid dynamics and their applications in renewable energy systems and air traffic management. Her academic career includes a PhD in Aerospace Science and Technology (UPC, 2009), an M.S. in Mechanical and Aerospace Engineering (SUNY Buffalo, 2003), and an Engineering degree from ENSICA (France, 2002). Roles: Academic Staff, Researcher at LACÀN Center for Numerical Methods. Research Interests: High-order CFD methods, wind energy optimization, multiphase flows, air traffic flow management, and planetary atmospheric dynamics. Her work bridges computational methodologies with practical applications, such as optimizing wind turbine efficiency using flow control techniques and analyzing thermally-driven winds on Mars. She has contributed to over 50 publications and projects like the European-funded WOW-CONTROL initiative for turbine optimization. Awards include the 2023 Best Paper Award in Atmospheric Sciences. Key projects include wind resource assessment near hydroelectric dams, delay management strategies in air traffic control, and analytical models for aviation emissions. Adeline collaborates with global institutions and industry partners to advance sustainable energy and transport systems.

• Computational Fluid Dynamics (CFD)
• Wind Energy
• Air Traffic Management
• +۵ مورد دیگر

Anna Martinez-Gavara is an Associate Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, Universitat de València. She is a member of the OPTIMATH research group, focusing on Optimization and Mathematical Modelling. Her research spans two major domains: applied numerical analysis for partial differential equations—particularly in developing high-resolution, well-balanced, and adaptive schemes for hyperbolic conservation laws and shallow water flows—and combinatorial optimization , where she applies metaheuristic methods such as tabu search, GRASP, and scatter search to problems like graph drawing, clustering, and diversity maximization. Her work integrates theoretical stability analysis with practical algorithmic design. The 15 most recent publications reflect a dual trajectory: early works (2008–2012) emphasize numerical methods for fluid dynamics and conservation laws, while more recent papers (2015–2023) pivot toward discrete optimization and metaheuristics. This evolution highlights her interdisciplinary approach, bridging numerical PDEs and operational research. Her collaborations include prominent researchers such as Rafael Martí, Rosa Donat, and Manuel Laguna, resulting in publications in high-impact journals including Journal of Computational Physics , Applied Numerical Mathematics , and European Journal of Operational Research . While no scientific awards are mentioned in the provided texts, her sustained publication record and active research up to 2023 indicate a strong and ongoing academic contribution. She has advised no publicly listed students in the data, and there is no mention of external grants or leadership of a lab or research team.

• Numerical Analysis
• Partial Differential Equations
• Fluid Mechanics
• +۸ مورد دیگر

Christoph Federrath is a Professor and Associate Director HDR at the Research School of Astronomy & Astrophysics, Australian National University (ANU). He holds a PhD from Heidelberg University (2010) and has held postdoctoral fellowships at Monash Centre for Astrophysics, Lyon’s École Normale Supérieure, and Heidelberg’s Institute for Theoretical Astrophysics. His research focuses on star formation in the present-day and early Universe, turbulence, magnetic fields, computational methods, and synthetic observation techniques. Federrath has supervised four PhD students and leads projects on computational astrophysics, turbulence dynamics, and galaxy formation. His work integrates numerical simulations with observational data to advance understanding of cosmic phenomena. Education: PhD in Physics (Heidelberg University, 2010), Physics studies at University of Würzburg (2001–2007) Research Interests: Star-forming molecular clouds, fluid dynamics, magnetic fields, numerical methods, and observational methods development His research spans theoretical and computational studies, including turbulence-driven dynamo effects, radiation hydrodynamics, and the interplay between magnetic fields and galactic structures. Federrath collaborates widely, contributing to international projects like the POSSUM survey and developing simulation frameworks such as AHKASH. His work bridges fundamental physics with observational astronomy, addressing key questions about star and galaxy formation across cosmic time.

• Star Formation
• Astrophysics
• Fluid Dynamics
• +۴ مورد دیگر

Pablo Cerda Duran is an Associate Professor at the Universitat de València, affiliated with the Faculty of Physics, Department of Astronomy and Astrophysics. He is a member of the Computational Astrophysics and Cosmology (CompAC) research group and previously contributed to the Computer Aided Modeling of Astrophysical Plasma (CAMAP) group. He earned his PhD in 2006 with a thesis on the general relativistic collapse of rotating stellar cores under the supervision of Dr. José Antonio Font Roda. His research lies at the intersection of numerical relativity, computational astrophysics, and high-performance computing. He specializes in developing and applying numerical methods to simulate extreme astrophysical phenomena, particularly involving compact objects such as neutron stars and black holes. His work includes general relativistic hydrodynamics and magnetohydrodynamics, Einstein’s equations, and instabilities in rotating stars. The body of his recent publications reveals a strong focus on algorithmic innovation in numerical relativity—such as implicit time integration schemes, constrained evolution formulations, and iterative solvers—applied to problems in relativistic astrophysics. His simulations span core collapse, neutron star instabilities, and boson star dynamics, often in the context of gravitational wave sources. Email: pablo.cerda@uv.es His scientific contributions appear in leading journals like Classical and Quantum Gravity , Journal of Computational Physics , and Astronomy & Astrophysics . Although no formal awards are listed, his sustained publication record and collaborative network indicate significant recognition in the field. He has co-authored works with prominent figures such as José Antonio Font, Miguel-Ángel Aloy, and Eric Gourgoulhon. Pablo Cerda Duran leads and contributes to advanced computational modeling efforts, advancing the simulation capabilities of relativistic systems. His work continues to support the theoretical interpretation of gravitational wave signals and high-energy astrophysical events.

• Numerical Relativity
• General Relativistic Hydrodynamics
• Computational Astrophysics
• +۷ مورد دیگر

Daniel Millán serves as Professor of Strength of Materials at the Mechanical Engineering Department, School of Industry-Applied Sciences, National University of Cuyo. He concurrently holds leadership positions as Director of the Institute I CAI CONICET-UNCuyo, Director of the Mechanical Engineering Department, Independent Researcher at CONICET, and Head of the MoCCAI research group. His research spans computational modelling and advanced numerical methods for engineering, with core expertise in: High-order phase-field modeling of fracture mechanics in thin-shells Thin-shell flexoelectric metamaterials Automatic data-driven collective variables identification Multiscale mechanobiological modeling of cerebral arteries Cerebral aneurysm morphometry and rupture risk assessment Ultrasonic analysis for additive manufacturing evaluation Analysis of his 2019-2024 publications reveals dominant themes in cerebral aneurysm biomechanics (using Kirchhoff-Love shell theory for patient-specific modeling) and fracture mechanics in thin structures. His work bridges theoretical numerical methods like phase-field modeling with practical applications in biomedical engineering and material science, particularly in ultrasonic characterization of ice composites. As leader of the MoCCAI research group, he directs computational mechanics initiatives focused on developing advanced numerical techniques for solving complex engineering and biomedical problems through interdisciplinary collaboration.

• Computational Modelling
• Numerical Methods
• Fracture Mechanics
• +۳ مورد دیگر