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

Luo Mai is an Assistant Professor at the University of Edinburgh's School of Informatics , with an upcoming promotion to Associate Professor (UK Reader) in August 2025. He leads the Large-Scale Machine Learning Systems Group and co-leads the UK EPSRC Centre for Doctoral Training in Machine Learning Systems and an ARIA Project on Scaling AI Compute by 1000X . PhD in Computer Science (Imperial College London, 2018) MRes in Advanced Computing (Imperial College London, 2012) His research focuses on the intersection of computer systems , machine learning , and data management . Key contributions include award-winning systems like WaferLLM (wafer-scale LLM inference), Tenplex (elastic ML), and ServerlessLLM (serverless LLM serving), published at top venues (OSDI, SOSP, ICML, NeurIPS, JMLR). Recent publications demonstrate trends in GPU-based distributed systems , LLM optimization , and adaptive machine learning . His team has developed groundbreaking open-source projects including TensorLayer , TorchOpt , and ServerlessLLM . Awarded Microsoft Research StarTrack Scholar (2024) , secured ARIA grant (2024) with Imperial College & Cambridge University, and received Google Fellowship during PhD (2012-2016). As an educator, he designed Edinburgh's popular Machine Learning Systems course (150+ students). His group supervises multiple PhD students including Yao Fu (recognized as 2024 Rising Star in ML & Systems) and Leyang Xue .

• Computer Systems
• Machine Learning Systems
• Data Management
• +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

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

Dr. Marcos Loureiro Garcia is an Assistant Professor at the University of Vigo, affiliated with the Department of Mathematics within the Faculty of Education and Sport Sciences. He obtained his PhD in 2020 with a thesis on "Modeling and numerical approach of aortic valve stages: healthy, stenotic and transcatheter replaced (TAVI)". His career demonstrates interdisciplinary expertise combining mathematics, cardiology, and educational innovation. Doctorate in Mathematics Applied to Cardiology (2020) Master's in Industrial Mathematics Master's in Teacher Training Bachelor's Degree in Mathematics His research focuses on applying mathematical methods to cardiology, particularly for simulating aortic valve pathologies and transcatheter aortic valve implantation (TAVI) procedures. He has developed biomechanical models of aortic valves, investigated TAVI outcomes through numerical simulation, and explored educational technology applications for mathematics teaching. His academic work spans both computational cardiology and mathematics education. He has published on topics including deep learning for TAVI characterization, digital valve twins, and augmented reality applications in teacher training. His research addresses clinical challenges through mathematical modeling while contributing to innovative teaching methodologies. Scientific achievements include: Best Poster Prize in Numerical Simulation at ESC Congress (2018) Six-Year Research Career Recognition Grants from Ministry of Science, Xunta de Galicia, GAIN He collaborates with leading institutions like the Czech Technical University in Prague and the Center for Computational Medicine in Cardiology (Lugano), developed his doctoral research at the Galicia Sur Health Research Institute (IISGS), and currently participates in the RITME research group focused on educational innovation and mathematical modeling.

• Mathematics Applied to Cardiology
• Biomechanical Simulation
• Aortic Stenosis
• +۳ مورد دیگر

Antonio Rodríguez-Sánchez is a theoretical particle physicist at the Instituto de Física Corpuscular (IFIC), University of Valencia, specializing in high-precision Standard-Model tests, especially the muon anomalous magnetic moment, hadronic light-by-light scattering, tau-lepton physics, and effective field theories. After his PhD at Valencia and postdocs at SISSA Trieste and IJCLab Orsay, he returned to IFIC in 2024 as a permanent research faculty member. Research Interests: Muon g-2 and its hadronic contributions Hadronic light-by-light scattering and short-distance constraints Standard Model Effective Field Theory (SMEFT) and top-quark physics Tau-lepton decays and flavor physics CP violation and weak-basis invariants His work combines perturbative QCD, dispersion relations, and lattice QCD input to reduce theoretical uncertainties in precision observables. He has co-authored flagship reviews on the Standard-Model prediction of the muon g-2 and is active in global SMEFT analyses. Selected Scientific Output: 42 peer-reviewed articles (2020-2025) with >300 citations on INSPIRE Co-leader of short-distance constraints program for hadronic light-by-light contributions to muon g-2 Contributor to Snowmass 2021 white papers on g-2 and strong coupling extraction No graduate students, grants, or major awards are listed in the available sources; his primary role is research faculty at IFIC.

• Theoretical Particle Physics
• Muon g-2
• Hadronic Light-by-Light Scattering
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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

Dr. Juan Jose Gude Prego is a Professor at the University of Deusto's Faculty of Engineering within the Department of Computing, Electronics and Communication Technologies. He maintains an active research profile with numerous publications and projects focused on advanced control systems. His primary research interests include: Fractional-order control systems Process control and system identification PID controllers and sliding mode control Industrial automation and real-time control systems Control engineering education and development of teaching tools Dr. Gude Prego's recent publications (2022-2025) demonstrate a strong focus on fractional-order control methodologies, particularly their application to chemical processes and industrial systems. His work bridges theoretical developments with practical implementations, often incorporating modern computing approaches like edge computing and AI-based optimization. He has developed novel identification techniques based on reaction curves and created specialized hardware architectures for implementing fractional-order controllers. Among his notable achievements is his certification as a National Instruments LabVIEW Associate Developer, reflecting his expertise in practical control system implementation. He has received recognition for his contributions to control engineering education through the development of innovative teaching equipment and remote laboratory systems. As an educator, Dr. Gude Prego has served as Coordinator of Engineering programs and actively participated in curriculum development related to the Bologna Process implementation. His work extends beyond pure research to address practical industrial challenges in process control, energy conservation, and industrial automation.

• Fractional-order control systems
• Process control and system identification
• PID controllers
• +۵ مورد دیگر

Juan Ignacio Arribas is a Full Professor in the Department of Signal and Communications Theory and Telematic Engineering at the School of Telecommunications Engineering, University of Valladolid. He is affiliated with the Center of Artificial Intelligence (Valladolid), Castilla-Leon Institute of Neuroscience (INCyL), and serves on the Section Board of Agriculture (MDPI) and Editorial Board of Scientific Data (Nature). His educational background includes: PhD in Electrical Engineering, University of Valladolid (2001) MSc in Electrical Engineering, University of Valladolid (1996) Dr. Arribas's research focuses on Machine Learning, Pattern Recognition, and Expert Systems applied to Cybersecurity, Computer Aided Diagnosis, Computer Vision, Bioinformatics, and Food Science. He pioneers computer vision and hyperspectral imaging for agricultural applications including weed detection, fruit quality assessment, and plant health monitoring, while also advancing medical diagnostics through EEG-based schizophrenia analysis. Analysis of his 15 most recent publications (2021-2024) reveals dominant themes in agricultural technology (70% of output), particularly non-destructive testing using hyperspectral imaging for crop monitoring and food quality. Cybersecurity applications (20%) feature network intrusion detection via novel neural architectures, with biomedical engineering (10%) represented by EEG analysis for psychiatric diagnosis. His work consistently integrates machine learning with domain-specific sensor data. Dr. Arribas supervises research through the Image Processing Laboratory (LPI) and collaborates with the Center of Artificial Intelligence. His international engagements include Visiting Research Associate roles at the University of Maryland (1998-2009) and Barrow Neurological Institute (2010). He leads the Image Processing Laboratory (LPI) research group and maintains active collaborations with the Castilla-Leon Institute of Neuroscience, driving interdisciplinary projects that bridge agricultural technology, medical diagnostics, and cybersecurity through advanced computational methods.

• Machine Learning
• Pattern Recognition
• Expert Systems
• +۵ مورد دیگر

Dr. Iker Pastor López is a Researcher at the University of Deusto's Faculty of Engineering, where he serves in the Department of Computing, Electronics and Communication Technologies. His academic credentials include a PhD in Information Systems (2013), Master's in Information Security (2010), and Computer Engineering degree (2007), all from the University of Deusto. His research spans: Machine Learning methods for industrial optimization Computer vision applications in manufacturing quality control Edge computing architectures for Industry 4.0 Big Data analytics in financial and healthcare domains Cybersecurity frameworks for emerging technologies He has led numerous competitive grants including: EU H2020 projects (DANTE 2016-2019) ELKARTEK industrial transformation initiatives Quantum computing security research (QUANTEK) Industry collaborations with ETXE-TAR and VIDRALA His publication record shows strong focus on applied AI research with recent work in: Edge computing optimization Financial time series forecasting Industrial defect detection Biomedical sequence analysis He currently supervises graduate research on industrial AI applications and has contributed to institutional initiatives bridging academia and industry through technology challenges.

• Machine Learning
• Computer Vision
• Optimization
• +۵ مورد دیگر

Francisco Javier Cuadrado Aranda is a faculty member in the Department of Naval and Industrial Engineering at the Ferrol Engineering Polytechnic School, University of A Coruña (UDC), Spain. His academic work spans teaching, research, and industrial collaboration, with a focus on mechanical engineering, multibody dynamics, and biomechanics. His research interests include: Multibody System Dynamics Computational Kinematics and Dynamics Biomechanics of Human Motion Gait Analysis and Orthosis Design Machine and Maintenance Engineering Simulation and Numerical Integration His recent publications (2021–2025) reflect a strong trend in applying multibody dynamics to biomechanics and robotics, particularly in human gait simulation, exoskeletons, assistive devices, and industrial applications. The research combines modeling, optimization, real-time simulation, and sensor integration, often with industrial or medical applications in automotive, maritime, and rehabilitation sectors. He has secured significant research funding from national (MINECO, Xunta de Galicia), European (EU), and industrial sources (GKN Driveline, NAVANTIA, Pixee Medical), and has directed multiple final-year and master’s theses. He is also involved in patents and software development through collaborations with Tecnalia and spin-offs like ABLE Human Motion. He leads the Laboratorio de Ingeniería Mecánica, where he supervises student projects and conducts applied research. His teaching includes Machine Design, Maintenance Engineering, Theory of Machines, and Biomechanics across Mechanical, Industrial, and Podiatry programs.

• Multibody System Dynamics
• Computational Kinematics
• Biomechanics of Human Motion
• +۹ مورد دیگر

Oscar Flores Arias is a Full Professor in the Department of Aerospace Engineering at the University of Carlos III of Madrid, affiliated with the Gregorio Millán Barbany University Institute for Modeling and Simulation in Fluodynamics, Nanoscience, and Industrial Mathematics. His research spans aerodynamics, biomedical fluid dynamics, and computational modeling. Director of the Aerospace Engineering Research Group Expert in Direct Numerical Simulation (DNS) and turbulent flow analysis His recent work focuses on machine learning integration with Navier-Stokes equations for cardiac flow mapping, patient-specific simulations of thrombogenesis, and bioinspired aerodynamic designs. Collaborations include biomedical institutions and engineering firms, emphasizing interdisciplinary applications. He leads multiple funded projects from Spanish national agencies and private sector partnerships, including: AEI-funded micro-turbine design (2022-2025) Cardiac flow uncertainty quantification (AEI, 2020-2024) MINETUR-funded micro-air vehicle aerodynamics (2016-2020) Supervised theses cover topics from coagulation modeling to bioinspired fluid-structure interaction. Current projects investigate turbulence control, blood flow dynamics, and bioinspired propulsion systems.

• Aeronautics
• Biomedicine
• Electronics
• +۷ مورد دیگر

Amine Ammar is a full Professor at Arts et Métiers University , where he serves as Deputy Director of the LAMPA laboratory and Scientific Director of the Doctorate at ENSAM. His academic career spans multiple institutions including Joseph Fourier University of Grenoble and ENS de Cachan. PhD in Mechanical Engineering (ENS de Cachan, 2001) Authorization to Direct Research (University Joseph Fourier, 2006) DEA in Solids & Structures (Paris VI, 1998) Engineering Degree (ENSAM Lille/Paris, 1998) A leading expert in multiscale computational modeling , his research focuses on rheological flows , dimensional reduction techniques , and digital twin technologies for polymer processing and composite materials. He pioneered the application of Proper Generalized Decomposition (PGD) in viscoelastic flow simulations and microstructure modeling. His scientific contributions have been recognized with the ESAFORM 2009 Scientific Award and Jean Mandel Prize . With an H-index of 28 (Scopus), he has supervised key research projects on polymer crystallization and fiber suspension dynamics. Co-author of Springer monograph on Stream-Tube Method (2021) Editor of Publibook on PGD for Materials Modeling (2012) As National Facilitator for Disciplinary Teaching at ENSAM, he has developed innovative courses on computational mechanics and rheology, mentoring students like S. Montésino and E. Prulière in their thesis work on polymer microstructuring.

• Multiscale Modeling
• Rheology
• Numerical Simulation
• +۴ مورد دیگر

Filippo Terragni is an Associate Professor in the Department of Mathematics at Carlos III University of Madrid. His interdisciplinary research spans applied mathematics, computational physics, and biomathematics, with focus areas in reduced order modeling, plasma dynamics, and angiogenesis. Dr. Terragni develops advanced numerical methods including LUPOD-based collocation techniques and adaptive POD frameworks for efficient simulation of complex systems. Applications range from space propulsion technologies to tumor-induced angiogenesis models and epidemic spread prediction. His recent work includes statistical theory of angiogenesis equations, soliton-driven biological processes, and plasma instabilities in magnetic nozzles.

• Applied Mathematics
• Computational Physics
• Reduced Order Modeling
• +۳ مورد دیگر

Antonio Huerta is a Professor of Applied Mathematics at the Universitat Politècnica de Catalunya (UPC) and Director of ICREA. He leads the Laboratori de Càlcul Numèric (LaCàN) , focusing on numerical methods for scientific and engineering problems governed by mechanics and interdisciplinary principles. His research spans applied mathematics, computational science, and fluid dynamics. Education : Ph.D. from Northwestern University (1987), preceded by studies at the Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos de Barcelona (1983). His work emphasizes numerical methods , particularly hybridizable discontinuous Galerkin (HDG), finite volume methods, and proper generalized decomposition (PGD), applied to problems in incompressible/compressible flow simulation , fluid-structure interaction , and magneto-mechanical coupling . Recent projects include data-driven design for automotive battery optimization ( GREEN ) and MATLAB-based open-source tools. Key collaborations include co-authoring the textbook Finite Element Methods for Flow Problems with Jean Donea and developing error estimation frameworks for finite element solutions. His publications highlight interdisciplinary applications in mechanics, mathematics, and computer science.

• Numerical Methods
• Mechanics
• Applied Mathematics
• +۴ مورد دیگر