جست‌وجوی استاد و پژوهشگر

Jeff Borggaardمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Science
• Control Theory
• +۷ مورد دیگر

Jeff Borggaard is a Professor of Mathematics at Virginia Tech, affiliated with the College of Science and the Interdisciplinary Center for Applied Mathematics (ICAM). His research focuses on numerical analysis, computational science, and control theory, with emphasis on optimization and control of systems governed by partial differential equations (PDEs). He specializes in sensitivity analysis, reduced-order modeling, and their applications in fluid dynamics and engineering systems. His work includes developing computational methods for PDE-constrained optimization, control of fluid flows, and uncertainty quantification. Key collaborations involve researchers at institutions like Florida State University and École Polytechnique de Montréal. Borggaard has been funded by agencies including the Air Force Office of Scientific Research (AFOSR) and the National Science Foundation (NSF), supporting projects on model reduction, flow control, and energy-efficient building systems. Research highlights include advancements in proper orthogonal decomposition (POD) for turbulent flows, nonlinear balanced truncation techniques, and applications of reduced-order models in control and optimization. His contributions also extend to thermal energy modeling in buildings and parameter estimation in groundwater flow systems. Borggaard holds positions at both the Department of Mathematics (McBryde Hall) and ICAM (Wright House), and maintains active involvement in professional societies such as the Society for Industrial and Applied Mathematics (SIAM) and the American Mathematical Society (AMS).

Matthias Seissمشاهده پروفایل

پژوهشگر

• Differential Equations
• Differential Algebra
• Algebraic Geometry
• +۶ مورد دیگر

Matthias Seiß is a Researcher at the University of Kassel's Faculty of Mathematics and Computer Science, Department of Algebra and Differential Equations. His research focuses on differential equations, algebraic geometry, and their applications in mathematical biology and systems theory. He has collaborated on topics such as singular initial value problems, chemical reaction network reduction, and differential Galois theory. Seiß has taught courses like Mathematical Biology, Linear Algebra, and Differential Algebra, leading exercise sessions and seminars. His work bridges algebraic methods with computational and applied mathematics, addressing challenges in modeling biological systems and analyzing complex dynamical behaviors. Key contributions include studies on Markov chain models, conservation laws in chemical networks, and numerical methods for singular equations. He is involved in projects like the Symbiont initiative, advancing symbolic computation in differential equations. His research has been published in journals such as SIAM Journal on Applied Dynamical Systems and Communications in Algebra.

• Differential-algebraic Equations
• Systems and Control Theory
• Data-driven Modelling
• +۳ مورد دیگر

Jan Heiland is a Lecturer at TU Ilmenau, focusing on systems and control theory, with affiliations to the Max Planck Institute for Dynamics of Complex Technical Systems (MPI Magdeburg). His work emphasizes differential-algebraic equations, data-driven modeling, and flow control. He holds a PhD in Applied Mathematics from TU Berlin. Education: PhD in Applied Mathematics (TU Berlin). Research Interests: Control theory, numerical methods for PDEs, model reduction, and applications in fluid dynamics. Current courses include Numerical Solutions of PDEs and Scientific Computing. He leads projects on nonlinear controller design (LPV Approximations), scientific computing in MaRDI, and fluid dynamics modeling. Grants & Projects: Projects include LPV Approximations for Nonlinear Controller Design (2023), MaRDI (2023), and MRI Fingerprinting with Philips (2021). His research also involves uncertainty quantification and robust stabilization techniques. Talks/Publications: Recent talks address H-infinity robust control and computational approaches for large-scale systems. Key publications focus on autoencoder-based reduced-order modeling and low-rank solutions for Riccati equations.

• Underwater Acoustics
• Bioacoustics
• Marine Conservation
• +۵ مورد دیگر

Professor Paul Lepper is a faculty member in the Department of Electronic and Electrical Engineering at Loughborough University, specializing in underwater acoustics and bioacoustics. His research focuses on mitigating anthropogenic noise impacts on marine fauna, including studies on offshore windfarm construction, marine species hearing, and acoustic deterrent systems. He holds a PhD and BSc and has extensive experience in underwater technology development, such as parametric sonar systems and IoT-based environmental monitoring networks. Education: Paul Lepper earned his PhD and BSc, though specific institutions are not detailed. His career includes roles as a Senior Research Fellow and postdoctoral researcher at institutions like the Scripps Institution of Oceanography. Research Interests: His work spans underwater noise assessment, marine mammal behavior, renewable energy noise mitigation, and acoustic monitoring technologies. He collaborates on global projects addressing UXO disposal, tidal energy impacts, and flood detection systems. Key areas include noise source characterization, sound field modeling, and marine species exposure analysis. Publications: Recent work highlights advancements in UXO disposal methods, noise mitigation strategies, and UAV applications for environmental monitoring. His research emphasizes interdisciplinary approaches to address global challenges in marine conservation and renewable energy integration. Labs/Teams: He contributes to the Signal Processing & Networks Research Group and collaborates internationally on projects like the Billia Croo Wave Energy Test Site and acoustic impact assessments for offshore wind farms.

Urban Faselمشاهده پروفایل

مدرس

• Machine Learning
• Autonomous Systems
• Aeroelasticity
• +۲ مورد دیگر

Urban Fasel is a Lecturer in Data-Driven Aerospace Engineering at Imperial College London's Department of Aeronautics. His research integrates machine learning with aerospace systems for applications in autonomous flight, aeroelastic control, and composite structures. Research spans co-design optimization, adaptive structures, and data-driven control methods, with particular focus on morphing wings for airborne energy systems and flapping-wing micro aerial vehicles. Recent work explores sparse modeling techniques for nonlinear dynamics discovery and robust control under uncertainty. Publications emphasize computational efficiency in aeroelastic modeling, reinforcement learning for flow control, and Bayesian approaches for system identification. Emerging themes include interpretable machine learning for distributed systems and morphing wing optimization for eVTOL applications.

Stefania Frescaمشاهده پروفایل

استادیار

• Scientific Machine Learning
• Reduced Order Modeling
• Deep Learning
• +۳ مورد دیگر

Stefania Fresca is an Assistant Professor in Numerical Analysis at the MOX Laboratory (Modeling and Scientific Computing) within the Department of Mathematics at Politecnico di Milano, Italy. She is part of the Future Artificial Intelligence Research (FAIR) Project. Previously, she held a postdoctoral position at MOX and conducted research at the University of Cambridge as a Visiting Researcher (2023–present) and during a visit to the Isaac Newton Institute (March–April 2023). Her research focuses on scientific machine learning, reduced order modeling (ROM), and numerical approximation of PDEs, with applications in engineering, biomechanics, and fluid dynamics. She develops deep learning-based ROM frameworks to accelerate simulations of complex systems, emphasizing theoretical foundations and practical efficiency. Education: PhD in Mathematical Models and Methods in Engineering (2021, Politecnico di Milano), MSc in Mathematical Engineering (2017, Politecnico di Milano & Sorbonne Universités), BSc in Mathematical Engineering (2014, Politecnico di Milano). She has organized multiple mini-symposia at conferences like ECCOMAS 2024 and SIAM UQ24, focusing on deep learning and ROM interactions. Upcoming engagements include a lecture at the Data-Driven & Reduced Order Models in Biomechanics Summer School (April 2025). Her work bridges theoretical advancements in neural networks and practical applications, addressing challenges in computational efficiency and model interpretability.

Benachir Medjdoubمشاهده پروفایل

استاد

• Digital Architectural Design
• Generative Design Models
• Building Services Design Optimization
• +۷ مورد دیگر

Benachir Medjdoub is a Professor of Digital Architectural Design at the School of Architecture Design and the Built Environment at Nottingham Trent University (NTU). He leads the Creative and Virtual Technologies Research Lab, focusing on securing external funding and advancing digital design education. His career includes roles at Ecole Centrale de Paris, University of Cambridge, and University of Salford, with extensive industry collaboration in architectural technology and energy conservation. Research: Specializes in generative design models, energy-efficient building systems, and constraint-based design methodologies Funding: Secured over £500k in EPSRC grants and industry partnerships Collaborations: Works with Atkins, Foster+Partners, Autodesk, and ETH Zurich His work bridges architectural practice and computational methods, developing tools like ARCHiPLAN and augmented reality systems for building design. He has advised numerous postgraduate students and served as external examiner at multiple UK universities. Key research contributions include thermal infrared thermography for energy audits, BIM optimization, and parametric design systems. His publications span journals like Automation in Construction and Energy Research & Social Science, with over 75 peer-reviewed articles.

Stefania Frescaمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Scientific Computing
• High Performance Computing
• +۳ مورد دیگر

Stefania Fresca is a researcher at Politecnico di Milano, holding a Researcher academic rank. She specializes in AI, scientific computing, and mathematical modeling with applications in cardiac electrophysiology. Her work focuses on developing deep learning-based reduced order models (DL-ROM) for complex systems like cardiac action potential simulations. She has contributed to publications in prestigious journals such as PLoS ONE and Computer Methods in Applied Mechanics and Engineering. Her methodologies improve computational efficiency for parametrized PDEs, impacting both industrial design and clinical decision-making in heart dysfunction analysis. She has also interned at Ernst & Young in risk advisory roles. Education: Mathematical Models and Methods (2017-2021), Mathematical Engineering (2014-2017) at Politecnico di Milano Research interests include: AI-driven model reduction, HPC applications, and biomedical modeling. Her DL-ROM framework outperforms traditional projection-based methods in electrophysiology simulations. She actively publishes in top journals and collaborates on grants related to parametrized systems.

Simone Deparisمشاهده پروفایل

استاد

• Mathematics Education
• Numerical Analysis
• Computational Fluid Dynamics
• +۳ مورد دیگر

Simone Deparis is a Professor and senior scientist in the Mathematics Section at EPFL's School of Basic Sciences (SB). He specializes in numerical analysis, computational fluid dynamics, and mathematics education. Deparis holds a PhD from EPFL and a mathematics degree from ETH Zurich. He pioneered a flipped classroom approach in first-year linear algebra courses, earning the 2018 Credit Suisse Award for Best Teaching. His research focuses on fluid-structure interaction, hemodynamics, and reduced basis methods, with applications in cardiovascular systems and biomedical engineering. Collaborations include EPFL’s Teaching Support Center (CAPE) and development of MOOCs on programming languages like MATLAB/Octave. Research interests span multiscale modeling, numerical methods for PDEs, and interdisciplinary applications in engineering and medicine. Deparis has contributed to open-source tools like the LifeV library, advancing computational hemodynamics simulations. His flipped classroom experiments emphasize student engagement and equity, leveraging innovative pedagogical strategies. Awards reflect his dual excellence in research and teaching. Education : M.Sc. Mathematics (ETH Zurich), PhD (EPFL) Key Projects : Fluid-structure interaction in arteries, numerical methods for cardiovascular simulations, flipped classroom pedagogy Grants/Awards : 2018 Credit Suisse Award for Best Teaching Labs/Teams: Involved in EPFL’s MATHICSE group and collaborations with biomedical engineering teams. Research outputs include over 50 peer-reviewed articles, focusing on computational methods and educational innovation.

Ludmila Prikazchikovaمشاهده پروفایل

مدرس

• Wave Propagation
• Asymptotic Methods
• Non-local Elasticity
• +۲ مورد دیگر

Ludmila Prikazchikova is a Lecturer in Applied Mathematics at Keele University's School of Computer Science and Mathematics. She holds an MSc in Applied Mathematics from Saratov State University and a PhD from the University of Salford focused on dispersion of elastic waves in pre-stressed compressible layers. Her research explores wave propagation phenomena, asymptotic methods for elastic structures, and non-local elasticity theory, with applications in nano-material modeling and composite structures. She develops mathematical frameworks for analyzing dynamic behavior of inhomogeneous materials and fluid-structure interactions. Publications primarily focus on elastic wave dynamics in complex material systems, featuring advanced asymptotic techniques and non-local continuum formulations. Recent articles analyze vibration behavior in layered composites, seismic metasurfaces, and fluid-loaded structures. Teaching: Dynamics and Differential Equations courses Administrative: Deputy Director of Recruitment for Mathematics

Bernd Simeonمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Mathematics
• Coupled Systems (Differential-Algebraic and PDEs)
• +۸ مورد دیگر

Bernd Simeon is a Professor of Mathematics at Technische Universität Kaiserslautern (TUK), leading the Differential-Algebraic Systems group within the Department of Mathematics. His research focuses on applied and numerical mathematics, with particular emphasis on coupled systems of differential-algebraic equations (DAEs) and partial differential equations (PDEs). Key areas of expertise include computational mechanics (especially multibody dynamics), interdisciplinary collaborations in materials science and fluid mechanics, isogeometric finite elements, and biomechanical applications such as hemodynamics and musculoskeletal systems. Before joining TUK in 2010, he held professorships at TU München (2002–2010) and the University of Karlsruhe (2000–2002). He obtained his habilitation in mathematics from Karlsruhe in 2000 and his PhD from TU München in 1994. His academic career includes acting professorships and research roles across multiple German universities. Research Interests: Applications of DAEs and PDEs in engineering and biology Isogeometric analysis for structural and fluid-structure interaction problems Model reduction techniques for dynamic systems Numerical methods for nonlinear dynamics and biomechanics Publications reflect his contributions to computational mechanics, including influential works on differential-algebraic systems (e.g., multibody dynamics) and isogeometric methods. Recent trends emphasize interdisciplinary applications in biomechanics and material science. His work bridges theoretical mathematics with practical engineering challenges, with a focus on advancing numerical simulation techniques. Current projects explore fiber-based muscle modeling and mean-field particle systems. Labs/Teams: Head of the AG Differential-Algebraic Systems research group at TUK.

Papadakis Nikolaosمشاهده پروفایل

دانشیار

• Databases
• Artificial Intelligence
• Software Engineering
• +۶ مورد دیگر

Nikolaos Papadakis is an Associate Professor at the Department of Electrical and Computer Engineering of the Hellenic Mediterranean University (HMU). He holds additional roles including Vice President of the Department of Social Work at HMU and Head of the Telecommunication and Informatic Technology team at the same department. His academic journey includes a BSc in Computer Science from the University of Cyprus (1997), an MSc from the University of Crete (1999), and a PhD in Computer Science from the University of Crete (2004). Prior to HMU, he held visiting professorships at the University of Crete, Technical University of Crete, and Technological Educational Institute of Crete, alongside research contributions at FORTH from 1997-2006. His research focuses on Databases and Knowledge Representation Artificial Intelligence Applications Semantic Web Technologies Software Engineering Methodologies Distributed Algorithms and Communication Protocols Renewable Energy Systems (wind, solar, energy efficiency) Notable projects include the SAVE initiative for net-zero sports facilities and studies on wind turbine blade optimization. His work bridges theoretical computer science with applied engineering solutions. Recent publications emphasize sustainable energy systems, composite material analysis, and quantum optics technologies. He maintains active collaborations in interdisciplinary areas such as epidemiological data modeling and space optical systems development. His career trajectory includes tenure at the Technological Educational Institute of Crete (2009-2019), reflecting a consistent commitment to academic innovation and applied research. Current research continues to explore emerging trends in renewable energy integration and advanced material science.

Jan S Hesthavenمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Analysis
• Discontinuous Galerkin Methods
• +۷ مورد دیگر

Jan S Hesthaven is a Professor of Mathematics and Dean of the School of Basic Sciences at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He holds adjunct professorships at Brown University and the Technical University of Denmark. His research focuses on computational methods for partial differential equations, model order reduction, and high-order numerical methods like discontinuous Galerkin schemes. He leads the Chair of Computational Mathematics and Simulation Science (MCSS) and founded SCITAS, EPFL's Scientific IT and Application Support unit. Education: M.Sc. (1991) and Ph.D. (1995) in Numerical Analysis from Technical University of Denmark, with postdoctoral training at Brown University and NASA Langley. He earned a Dr.Techn. (2009) for contributions to nodal discontinuous Galerkin methods. Research interests include computational fluid dynamics, seismic monitoring of porous media, and machine learning integration with PDE solvers. Notable contributions include physics-informed neural networks, reduced basis methods for acoustics, and surrogate models for gravitational waves. He has received prestigious awards like the SIAM Fellowship (2014) and the Philip J. Bray Teaching Award (2004). Leadership roles: Dean of SB (2017–present), Director of CCV at Brown (2006–2013), and Deputy Director of ICERM (2010–2013). His work bridges fundamental mathematics with applications in engineering, physics, and environmental science. Grants and collaborations include NSF funding for model reduction and NASA partnerships. He supervises interdisciplinary projects on seismic data analysis and acoustic simulations. SCITAS, under his leadership, provides high-performance computing support across EPFL.

John McPheeمشاهده پروفایل

استاد

• Biomechanical Engineering
• Robotics
• Control Systems
• +۷ مورد دیگر

John McPhee is a Professor in the Department of Systems Design Engineering at the University of Waterloo, with a cross-appointment in other academic groups. His research focuses on biomechanical systems, robotics, control systems, and musculoskeletal modeling with applications in wearable devices, surgical simulation, and sports biomechanics. He leads the Motion Research Group, developing advanced technologies for exoskeletons, prostheses, and human motion analysis. Key research themes include optimizing implant positioning for joint replacements, energy-efficient exoskeleton design, and predictive multibody dynamics modeling. His work integrates machine learning with physics-based simulations for applications in healthcare, transportation, and sports engineering. Recent studies emphasize environment-aware control systems for wearable robots and data-driven approaches for muscle-torque estimation using sEMG signals. McPhee collaborates on interdisciplinary projects involving robotics, biomechanics, and computational modeling. His research outputs highlight advancements in exoskeleton safety standards, automated anatomical landmark detection, and optimal control strategies for autonomous systems. The work demonstrates a strong focus on real-world applications in rehabilitation, surgical planning, and performance enhancement.

Konstantinos Vlachasمشاهده پروفایل

پژوهشگر

• Structural Engineering
• Model Order Reduction
• Nonlinear Systems
• +۳ مورد دیگر

Dr. Konstantinos Vlachas is part of the Professorship for Structural Mechanics and Monitoring at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. His research focuses on parametric reduced-order modeling (pROMs) for virtualization of nonlinear dynamical systems, digital twin development, and fusion of monitoring data with numerical models. Education: National Technical University of Athens (NTUA): Diploma in Civil Engineering (2016) ETH Zurich: M.Sc. in Structural and Transport Systems Engineering (2019) ETH Zurich: Ph.D. Candidate in Structural Mechanics and Monitoring (since 2019) Research Highlights: Specializes in physics-based model order reduction, nonlinear substructuring, and application of machine learning techniques for structural identification and damage assessment. Supported by SANDIA National Laboratories. Awards: Culmann-Prize (2019) for Master's Thesis on ROMs for structural identification in damaged structures. Labs/Teams: Conducts research within the Structural Mechanics and Monitoring group led by Prof. Eleni Chatzi, focusing on advancing digital twin technologies for engineering systems.