جست‌وجوی استاد و پژوهشگر

Jennifer Pestanaمشاهده پروفایل

مدرس ارشد

• Numerical Linear Algebra
• Scientific Computing
• Krylov Subspace Methods
• +۷ مورد دیگر

Dr Jennifer Pestana is a Senior Lecturer in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. She has been a faculty member since 2015 and is actively involved in research and academic leadership. She is a principal investigator on multiple grants and serves as an editor for SIAM publications. Education: DPhil, University of Oxford, 2012 Postdoctoral positions: University of Manchester, University of Oxford Her research centers on numerical linear algebra and its applications in scientific computing . She investigates the convergence of Krylov subspace methods , develops preconditioners for specific applications, and explores the use of tropical algebra for matrix pre-scaling. Current projects involve fast solvers for fractional diffusion and analysis in broadband signal processing . Her work bridges theoretical numerical analysis with practical computational challenges. The recent publications reflect a strong focus on iterative methods, matrix analysis, and signal processing. Common themes include Toeplitz matrices , eigenvalue decomposition , preconditioning , and covariance estimation , demonstrating her expertise in both algorithmic development and application-driven numerical methods. Scientific Awards: Best Student Paper Award (2018) Dr Pestana actively supervises students and leads research projects, including EPSRC-SFI and Strathclyde ISP Joint PhD initiatives. She has secured significant funding for work on Krylov methods and efficient solvers. Her professional service includes organizing major conferences such as the Biennial Conference on Numerical Analysis and serving on program committees, including for the International Linear Algebra Society. She is also an invited speaker and visiting researcher, demonstrating national and international recognition. She is involved with research teams focused on numerical analysis and scientific computing, contributing to collaborative efforts in developing novel preconditioned iterative solvers. Her lab and project groups work on both theoretical and applied aspects of linear algebra in PDEs and signal processing.

Yue Wuمشاهده پروفایل

مدرس

• Stochastic Analysis
• Numerical Analysis
• Stochastic Differential Equations
• +۸ مورد دیگر

Dr. Yue Wu is a Lecturer in the Department of Mathematics and Statistics at the Faculty of Science, University of Strathclyde. She is actively engaged in research and teaching, with a strong focus on stochastic and numerical analysis. She is affiliated with the Alan Turing Institute as a Visiting Researcher and collaborates internationally on advanced mathematical and data science projects. Research Interests: Numerical analysis for stochastic (partial) differential equations (SDEs/SPDEs) Random periodic solutions and their numerical approximation Rough path theory and signature methods Applications in machine learning, data science, and engineering systems Her recent work bridges pure stochastic analysis with practical applications in AI, autonomous systems, and industrial diagnostics. She employs advanced mathematical tools such as log-signatures and randomized numerical schemes to solve complex real-world problems. Publication Trends: Dr. Wu's recent publications (2022–2025) show a strong trend toward integrating stochastic numerics with machine learning. Her work spans theoretical convergence analysis of numerical schemes, feature extraction using rough paths, and PDE-informed deep learning. There is a clear interdisciplinary focus, connecting mathematics with engineering and computer science. Scientific Awards: Strathclyde & TU Braunschweig Joint Collaborative Funding Recipient (2023) ICIAM2023 Financial Support Scheme 2 Recipient (2023) Turing Network Development Award: Trailblazers Competition Recipient (2022) Lower Saxony – Scotland Tandem Fellowship Recipient (2022) Advising and Grants: Dr. Wu is accepting PhD students and offers projects in stochastic numerics and rough path applications. She has secured funding as Principal Investigator (e.g., International Exchanges Round 3) and Co-investigator (e.g., AI-based asteroid navigation project with ESA). Her grants reflect strong international collaboration and interdisciplinary innovation. Labs and Teams: While no specific lab name is mentioned, Dr. Wu is part of active research networks including the Alan Turing Institute and collaborates with teams in aerospace, data science, and applied mathematics. She organizes seminars and workshops, indicating leadership in her research community.

Jia Liuمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Linear Algebra
• Sparse Matrix Computations
• +۹ مورد دیگر

Dr. Jia Liu is a Professor and Chair of the Department of Mathematics and Statistics at the University of West Florida, within the Hal Marcus College of Science and Engineering. She has been a key academic figure at UWF since 2006, leading both research and departmental initiatives in computational and applied mathematics. Her educational background includes a Ph.D. in Mathematics from Emory University, funded by the National Science Foundation, focusing on preconditioned Krylov subspace methods for incompressible flow problems. She earned her M.S. and B.A. in Mathematics from Central China Normal University, where her bachelor's thesis received the highest honor. Dr. Liu's research lies at the intersection of numerical linear algebra, scientific computing, and interdisciplinary applications. Her primary interests include: Numerical solvers for large sparse linear systems Krylov subspace iterative methods Preconditioning techniques Applications to Navier-Stokes and optimization problems Geometric and topological analysis of ellipsoids Complex networks and community detection via spectral clustering Machine learning for disease prediction and biological modeling The trends in her publications reflect a consistent focus on robust numerical algorithms with applications across fluid dynamics, network science, and biomedical modeling. Her work emphasizes both theoretical development and practical implementation in high-performance computing environments. Dr. Liu has served as an editor and editorial board member for several peer-reviewed journals and regularly reviews submissions. While specific awards are not listed, her sustained publication record in prestigious venues such as SIAM Journal on Scientific Computing and Journal of Biological Dynamics underscores her scholarly impact. As department chair and professor, she plays a central role in academic advising, curriculum development, and research mentorship. She teaches core courses including Differential Equations, Numerical Analysis, and Real Analysis, contributing significantly to both undergraduate and graduate education. Her leadership extends to managing research grants and fostering collaborations across disciplines. Though no formal lab name is mentioned, her research activities suggest involvement with computational modeling groups, likely associated with applied mathematics and data science initiatives at UWF. Her ongoing work continues to advance numerical methods for complex systems in science and engineering.

Mengying Xiaoمشاهده پروفایل

استادیار

• Computational Mathematics
• Numerical Analysis
• Fluid Dynamics
• +۷ مورد دیگر

Dr. Mengying Xiao is an Assistant Professor in the Department of Mathematics and Statistics at the University of West Florida, part of the Hal Marcus College of Science and Engineering. Her research focuses on computational mathematics and numerical methods for fluid dynamics and related systems. Dr. Xiao earned her Ph.D. in Mathematics from Clemson University, an M.S. in Mathematics from Capital Normal University in China, and a B.S. in Mathematics from the same institution. Her research interests include computational mathematics, numerical analysis of incompressible Navier-Stokes equations, nonlinear solvers, Anderson acceleration, algebraic splitting methods, and magnetohydrodynamics (MHD). She develops and analyzes efficient iterative schemes for complex fluid systems, particularly emphasizing convergence acceleration and stability. The recent publications show a strong trend toward improving convergence of fixed-point and Picard-type iterations using Anderson acceleration, as well as developing and analyzing algebraic splitting techniques for decoupling complex systems like Navier-Stokes and MHD equations. Her work bridges theoretical numerical analysis with practical computational efficiency in fluid dynamics simulations. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: No information available regarding student advising or research grants. Labs and Research Teams: Dr. Xiao is associated with the Computational Science, Data Analytics (CSDA) Lab at UWF, which supports interdisciplinary research in computational modeling and data science.

• Inverse dynamics
• Structural health monitoring
• Linear and nonlinear vibrations
• +۵ مورد دیگر

Dr. Hamed Kalhori is a Lecturer in the School of Mechanical and Mechatronic Engineering at the University of Technology Sydney (UTS), Faculty of Engineering & Information Technology. As a Casual Academic, he contributes to teaching and research in mechanical engineering with a focus on dynamics, vibrations, and structural health monitoring. His work bridges theoretical and practical applications in civil infrastructure, aerospace systems, and composite materials. Dr. Kalhori earned his PhD in Mechanical Engineering from the University of Sydney between March 2013 and July 2017. Dr. Kalhori's research spans multiple domains within mechanical and structural engineering. His primary expertise lies in inverse dynamics problems , particularly impact force identification, where he develops advanced methodologies combining model-based approaches with machine learning techniques. He extensively investigates linear and nonlinear vibrations in various structures, from micro-plates to large-scale bridges. His work in structural health monitoring focuses on innovative approaches for bridge inspection, including drive-by assessment techniques and sensor network optimization. Additionally, he explores smart materials and structures , particularly carbon nanotube-reinforced composites and their mechanical behavior under dynamic loading conditions. Dr. Kalhori's publication record demonstrates a strong trajectory in mechanical engineering research with increasing focus on interdisciplinary approaches. His recent work shows growing integration of machine learning techniques with traditional mechanical engineering methods, particularly for solving inverse problems in structural dynamics. There's a clear progression from fundamental vibration analysis toward practical applications in infrastructure monitoring and composite material characterization. His research maintains strong connections between theoretical modeling and experimental validation across diverse structural systems. Dr. Kalhori has received notable recognition for his academic contributions: 2019 Dean's Award for Excellence in Teaching and Tutoring Multiple research grants in vibration-related fields Dr. Kalhori has secured research funding for projects related to vibration analysis and structural monitoring, including industry collaborations such as the Alstom DMU traincar crash test project (2025). He has led international research collaborations and conducted numerous workshops and short courses both locally and internationally. While the specific details of his advising relationships aren't provided in the available information, his extensive publication record and research leadership suggest active engagement with graduate students and early-career researchers. His research involves collaboration with the Centre for Autonomous Systems at UTS and Data61-CSIRO, where he previously worked as a research associate and research assistant respectively. These collaborations focus on structural health monitoring projects across New South Wales, leveraging advanced sensing technologies and computational methods for infrastructure assessment.

• Mixed-Integer Nonlinear Programming (MINLP)
• Global Optimization
• Chemical Process Optimization
• +۳ مورد دیگر

Tapio Westerlund is a Professor in Mathematics at Åbo Akademi University's Faculty of Science and Engineering. His research focuses on mixed-integer nonlinear programming (MINLP), convex optimization, and nonsmooth optimization techniques, with applications in chemical process engineering and environmental science. Key Research Areas: MINLP, global optimization, supporting hyperplane methods, and limestone dissolution modeling. Notable Contributions: Development of the Supporting Hyperplane Optimization Toolkit and reformulation frameworks for nonconvex MINLP problems. Collaborations: Active in interdisciplinary research networks with external collaborations across countries. His recent publications emphasize convex MINLP algorithms, hyperplane techniques, and applications to wet flue gas desulfurization. While no specific awards or students are listed in the provided data, his citations and Mendeley readership indicate significant academic impact.

Matthias Maischakمشاهده پروفایل

استاد

• Elliptic Boundary Value Problems
• Transmission Problems
• Signorini Problems
• +۱۰ مورد دیگر

Dr Matthias Maischak is a Reader in the Department of Mathematics at Brunel University London within the College of Engineering, Design and Physical Sciences . His research focuses on elliptic boundary value problems , transmission problems , Signorini problems , and variational inequalities , with emphasis on boundary and finite element methods , fast solvers , and adaptive algorithms . He is also a key developer of the MaiProgs software package, which implements advanced numerical techniques for engineering applications. Research Areas : Boundary Element/FEM Methods, Contact Mechanics, Adaptive Algorithms, High-Performance Computing, Retarded Potential Analysis Supervision : Ph.D. and MSc students in computational mathematics, including projects on shape analysis, discontinuous Galerkin methods, and boundary integral equations. Publications span topics like dual-dual formulations , FE-BE coupling , hp-adaptive methods , and error estimation in linear elasticity and acoustic fluid-structure interaction . He has co-authored works on nonlinear interface conditions and time-domain boundary element methods , with applications to microstructure analysis , elastoplastic contact , and singular perturbations .

Alessandro Tasoraمشاهده پروفایل

استاد

• Theoretical Mechanics
• Numerical Methods in Multibody Dynamics
• Robotics
• +۷ مورد دیگر

Alessandro Tasora is a Full Professor at the University of Parma in the Department of Industrial Engineering and Department of Engineering and Architecture . He directs the Digital Dynamics Lab and serves as Scientific Director of the Smart Production Lab 4.0 . His work spans theoretical mechanics, robotics, and high-performance computing. Director, Digital Dynamics Lab (2019–present) Scientific Director, Smart Production Lab 4.0 (2017–present) National Scientific Qualification for Full Professor (2016) Honorary Associate, University of Wisconsin-Madison (2009–present) Research focuses on non-smooth multibody dynamics , GPU-accelerated simulation , and industrial robotics . He developed the Chrono::Engine software for multibody physics and HyperOCTANT for NLCP problems. His work addresses granular flows in nuclear reactors, vehicle mobility on deformable terrain, and historical structural analysis. Key article trends include GPU-based HPC for large-scale simulations, cone complementarity in contact dynamics, and isogeometric beam formulations for flexible bodies. Collaborations with Argonne National Laboratory and Fraunhofer ITWM highlight his international impact. Top-SNIP Paper (2017) International CAE Conference Poster Award (2015) Best Paper, Asian Conference on Multibody Dynamics (2010) TOP4 Paper, RAAD Robotics Workshop (2011) He has supervised over 40 theses in automation, tribology, and robotics. Grants include FFABR-MIUR , US Army RIF , and CNR projects . Projects involve seismic protection, autonomous AGVs, and Industry 4.0 consulting.

Minghao Rostamiمشاهده پروفایل

دانشیار

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

Minghao Rostami is an Associate Professor in the Department of Mathematics and Statistics at Binghamton University. His research focuses on interdisciplinary problems at the intersection of applied mathematics, computational fluid dynamics, and biophysics, often leveraging machine learning and numerical methods. His recent work spans topics such as mixed noise removal in signal processing, data-driven fluid trajectory prediction, and multi-scale modeling of actin polymer dynamics. Publications highlight collaborations between mathematical theory and biological applications, particularly in biofluid mechanics. The trends in his publications from 2012 to 2025 emphasize scalable numerical algorithms for dense/sparse matrices, stability analysis in incompressible flows, and hybrid modeling frameworks combining machine learning with physical simulations. Specific subfields include multigrid methods, kernel regularization, and bio-inspired fluid pumping optimization.

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• Computational Structural Dynamics
• +۱۲ مورد دیگر

Dr.-Ing. Norbert Hosters is a Research Associate and Chief Engineer at the Chair for Computational Analysis of Technical Systems (CATS), Faculty of Mechanical Engineering, RWTH Aachen University. He has been active since 2020 and serves as General Secretary of the German Association for Computational Mechanics (GACM). His work bridges advanced computational methods with engineering applications. His research focuses on numerical methods for fluid-structure interaction , computational fluid and structural dynamics , isogeometric analysis , and aerothermoelasticity . He also explores applied quantum methods and physics-informed neural networks for solving complex PDEs and optimizing industrial processes. His interdisciplinary work spans mechanical, biomedical, and computational engineering. The recent publications (2023–2025) demonstrate a strong trend toward integrating machine learning with traditional simulation techniques, particularly in partitioned FSI , multiphase flow , shape optimization , and biomedical simulations such as LVAD modeling. His work appears in high-impact journals like Scientific Reports , Computers & Fluids , and International Journal for Numerical Methods in Engineering , as well as major conferences including GACM and CMBE. He is actively involved in teaching courses on Numerical Methods for Fluid-Structure Interaction , Isogeometric Analysis , and Simulation Methods in Mechanical Engineering . He offers student projects and supervises research, though no named advisees are listed. He has no listed scientific awards or fellowships. His research is conducted within the CATS chair, a leading group in computational mechanics, contributing to both fundamental methods and industrial applications. He plays a key role in academic service through GACM leadership.

• Numerical Analysis
• Iterative Methods
• Nonlinear Equations
• +۷ مورد دیگر

Vicente Fco Candela Pomares is an Associate Professor in the Department of Mathematics at the Faculty of Mathematics, University of Valencia, Spain. His academic career has been centered on numerical analysis and computational mathematics, with a focus on iterative methods for nonlinear equations and multiresolution techniques. His research interests lie primarily in Numerical Analysis , especially iterative root-finding methods such as Halley, Chebyshev, and Steffensen-type algorithms. He has contributed significantly to the convergence analysis of these methods, particularly in Banach spaces and for ill-conditioned problems. His work extends to multiresolution analysis , wavelets , and image restoration , where he applies fractional regularization and nonlinear approximation frameworks. The trends in his recent publications show a sustained focus on derivative-free iterative methods , convergence theory , and applications in image processing . His work often bridges theoretical numerical analysis with practical computational challenges. He earned his PhD from the University of Valencia in 1988 under the supervision of Dr. Antonio Marquina Vila, with a thesis on a priori error estimators for iterative methods. He has collaborated extensively with researchers including Sergio Amat, Sonia Busquier, and Rosa Peris. Notable co-authors include Pantaleón D. Romero and Francesc Aràndiga. His publications appear in high-quality journals such as Journal of Computational and Applied Mathematics , Applied Mathematics and Computation , and SIAM journals. He is actively affiliated with the University of Valencia, as evidenced by his institutional email and ongoing publications. There is no indication of part-time status, retirement, or awards in the available data.

• Applied Mathematics
• Numerical Relativity
• Computational Astrophysics
• +۶ مورد دیگر

Isabel Cordero Carrión is an Associate Professor in the Department of Mathematics at the Faculty of Mathematics, Universitat de València. She is affiliated with two research groups: CompAC (Computational Astrophysics and Cosmology Group) and ANIMS (Numerical Analysis, Images, Multiresolution and Simulation). Institution: Universitat de València School: Faculty of Mathematics Department: Department of Mathematics Research Groups: CompAC, ANIMS Her research focuses on applied mathematics, particularly in numerical relativity and computational astrophysics. She works on the numerical evolution of Einstein's equations, relativistic hydrodynamics, and the development of stable and accurate numerical schemes for general relativistic systems. Her work bridges differential geometry, partial differential equations, and high-performance computing. The recent articles show a consistent trend in advancing numerical methods for general relativity, including formulations of the Einstein and hydrodynamic equations, implicit time integration, and spherical coordinate implementations. Her work emphasizes stability, constraint preservation, and computational efficiency in simulating relativistic systems. No scientific awards are listed in the provided text. She completed her Ph.D. in 2009 at the Universitat de València under the supervision of Dr. José María Ibáñez. While no formal students are listed, her collaborative work includes co-authorship with researchers such as Pablo Cerdá-Durán, Miguel-Ángel Aloy, and Jérôme Novak. She has not received any mentioned grants in the text. She is a member of the CompAC and ANIMS research groups, which focus on computational astrophysics, numerical analysis, and simulation of physical systems using advanced mathematical methods.

• Astrophysics
• Relativistic Hydrodynamics
• Computational Astrophysics
• +۷ مورد دیگر

Miguel Ángel Aloy Torás is a Professor in the Department of Astronomy and Astrophysics at the Faculty of Physics, University of Valencia. He is a leading researcher in theoretical and computational astrophysics, focusing on high-energy phenomena such as relativistic jets, gamma-ray bursts, and core-collapse supernovae. His research interests span astrophysics, relativistic hydrodynamics, computational physics, and numerical analysis. He specializes in modeling extreme astrophysical environments using advanced numerical simulations, particularly in general relativistic magnetohydrodynamics (GRMHD) and high-performance computing. His work contributes to understanding gravitational wave sources and transient cosmic events. The publications highlight a strong trend in developing and applying numerical methods to solve complex systems in relativistic astrophysics. Early work focused on relativistic hydrodynamics and Riemann solvers, while more recent contributions include algorithmic improvements in iterative solvers like the Scheduled Relaxation Jacobi method, demonstrating a sustained focus on computational innovation for physical modeling. He is the principal investigator of the CAMAP (Computer Aided Modeling of Astrophysical Plasma) research group, which develops simulation codes for astrophysical plasmas. His collaborative network includes prominent researchers in computational astrophysics across Spain and Europe.

Flavio SARTORETTOمشاهده پروفایل

دانشیار

• Numerical Analysis
• Computational Science
• Medical Signal Processing
• +۴ مورد دیگر

Flavio SARTORETTO is an Associate Professor in Scientific Computing at Ca' Foscari University of Venice. He holds a Mathematics degree from the University of Padua and has held academic positions at University of Padua (1982-1992) and Sapienza University of Rome (1992-1993) before joining Ca' Foscari in 1993. His research focuses on numerical analysis, computational methods, and interdisciplinary applications including environmental modeling, cognitive processes, and assistive technologies. Key research areas include numerical solutions of PDEs, meshless methods, EEG signal analysis, and e-learning tools for impaired individuals. He has participated in major research projects such as EC Network (1992-1995), PRIN initiatives (1997-2010), and contributed to software development for geomechanical models and air quality systems. His work spans computational fluid dynamics, robotics applications, and cognitive studies. Recent publications highlight advancements in mesh refinement strategies, robotic assistive devices, and spatial cognition research. He has reviewed for prestigious journals and served in academic committees for state examinations and international conferences. He maintains active involvement in academic service, including roles in evaluation committees and contributions to professional societies like SIAM and CICAP.

Alex Ferrer Ferreمشاهده پروفایل

دانشیار

• Materials Design
• Aeronautical Engineering
• Computational Mechanics
• +۴ مورد دیگر

Àlex Ferrer Ferre is a Serra-Hunter Associate Professor at the Department of Physics, Universitat Politècnica de Catalunya (UPC) in Barcelona, and an associate researcher at the International Center for Numerical Methods in Engineering (CIMNE). His research focuses on topology optimization, computational mechanics, and advanced material design. He holds a PhD in Computational Mechanics from UPC (2017) and has conducted postdoctoral research at the University of Oxford and École Polytechnique. Key roles include teaching courses like Flight Mechanics and leading the Swan project for topology optimization software. His work integrates academic and industrial collaborations with entities like ESA and INEGI. Research interests span multiscale design, topology optimization for additive manufacturing, and fracture mechanics. Major projects include the TopSat CubeSat chassis project (ESA-funded) and the Flexion project on flexible material design. Awards include the Marie Curie Fellowship (2019) and an Excellent Cum Laude PhD. His labs and teams collaborate globally, emphasizing open-source tools and collaborative research.