جست‌وجوی استاد و پژوهشگر

• Cardiac Biomechanics
• Congenital Heart Malformations
• Medical Device Materials
• +۴ مورد دیگر

Dr. Choon Hwai Yap is a Senior Lecturer in the Department of Bioengineering at Imperial College London's Faculty of Engineering. His research focuses on cardiac biomechanics, particularly in embryonic and fetal hearts, investigating mechanical influences on growth and congenital heart defects. He also explores materials for cardiovascular devices, including anti-thrombotic coatings and hemostatic technologies. Dr. Yap holds editorial roles at Scientific Reports and Medical and Biological Engineering and Computing . His academic journey includes a PhD from Georgia Institute of Technology, a postdoc at University of Pittsburgh, and prior faculty work at National University of Singapore. Research themes include fetal heart interventions via ultrasound-based simulations, zebrafish cardiac development biomechanics, and fluid dynamics in congenital heart defects. Key contributions include finite element modeling of fetal heart mechanics and novel materials for medical devices. Recent work emphasizes AI-driven medical imaging advancements (e.g., deep learning for motion estimation in echocardiography) and multi-case physics-informed neural networks for fluid dynamics. His lab bridges engineering and clinical applications, aiming to improve fetal heart diagnostics and treatment strategies. Notable projects include predicting outcomes of fetal aortic valvuloplasty through myocardial stress analysis and developing superhydrophobic hemostats for minimal blood loss. He collaborates with clinicians to translate biomechanical insights into clinical practice, focusing on fetal cardiology and biomedical device innovation.

Animashree Anandkumarمشاهده پروفایل

استاد

• Machine Learning
• Non-Convex Optimization
• High-Dimensional Statistics
• +۶ مورد دیگر

Animashree (Anima) Anandkumar is the Bren Professor of Computing and Mathematical Sciences at the California Institute of Technology (Caltech). She holds a B.Tech from the Indian Institute of Technology Madras (2004) and a Ph.D. from Cornell University (2009). Prior to her current role, she was a Visiting Associate at Caltech (2017) and served as a Senior Director of AI Research at NVIDIA and Principal Scientist at Amazon Web Services. Her research focuses on large-scale machine learning, non-convex optimization, and high-dimensional statistics, with pioneering work on tensor decomposition methods for probabilistic models and neural operators for scientific simulations. Research Interests: Anima's work bridges AI and science, emphasizing scalable and interpretable algorithms. Key areas include quantum optimization, climate modeling via Fourier Neural Operators, medical AI for surgical skill assessment, and physics-informed neural networks for PDEs. Her lab, Anima AI+Science, drives interdisciplinary projects in climate, aerospace, engineering, and medicine. Awards & Recognition: She has received the IEEE Fellowship, Alfred P. Sloan Fellowship, NSF Career Award, and Blavatnik National Awards Finalist distinction. Her contributions to AI+Science include 45000x faster weather forecasting and AI-aided catheter design. Advising & Teams: Advises over 20 Ph.D. students and postdocs, many of whom hold faculty positions or lead roles in industry. Her lab collaborates with institutions globally on projects like the DeepSpeed4Science initiative for large-scale scientific discovery. Labs & Projects: The Anima AI+Science Lab advances AI methods for climate prediction (FourCastNet), aerospace control (FALCON), and molecular design (OrbNet). Ongoing efforts include quantum computing solvers, turbulence-resistant flight systems, and surgical skill analytics.

Hongkai Zhaoمشاهده پروفایل

استاد

• Applied and Computational Mathematics
• Inverse Problems
• Imaging
• +۴ مورد دیگر

Professor Hongkai Zhao is a Chancellor’s Professor and Chair of the Mathematics Department at the University of California, Irvine (UCI), with a joint appointment in Computer Science. He holds a B.S. from Peking University, M.S. from USC, and Ph.D. from UCLA, all in Mathematics. His research focuses on computational and applied mathematics, including moving interface problems, inverse problems, imaging, and Hamilton-Jacobi equations. He is renowned for developing the Fast Sweeping Method (FSM) for solving hyperbolic PDEs efficiently. Education: B.S. (Peking University, 1990), M.S. (USC, 1992), Ph.D. (UCLA, 1996). Employment: UCI faculty since 1999, Szego Assistant Professor at Stanford (1996–1999). Research interests span computational fluid dynamics (moving interfaces), inverse problems (tomography, wave propagation), and imaging (medical imaging, computer vision). Key contributions include numerical methods for surfactant-laden drops, fast algorithms for Hamilton-Jacobi equations, and 4D cone-beam CT reconstruction. Awards include the Feng Kang Prize (2007), Sloan Fellowship (2002–2004), and Chancellor’s Professorship (2016–present). He has authored over 100 publications on numerical methods, inverse problems, and PDEs. Lab affiliations: Active in interdisciplinary research at UCI, leading projects in computational mathematics and imaging science. Current work includes high-frequency wave propagation, multi-scale modeling, and geometric data analysis.

Tabea Tscherpelمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations (PDEs)
• +۳ مورد دیگر

Tabea Tscherpel is a tenure-track professor at the Department of Mathematics of TU Darmstadt, specializing in Numerical Analysis and Scientific Computing. Previously, she held postdoctoral positions at Universität Bielefeld (under Lars Diening) and RWTH Aachen (under Sebastian Noelle), following her DPhil from the University of Oxford under Endre Süli’s supervision. Her research focuses on numerical methods for PDEs, finite element methods, and computational fluid dynamics. Key research interests include exact integration for finite elements, divergence-free spaces, and energy-consistent discretizations. Her work bridges theoretical analysis with practical implementation, addressing challenges in computational geometry and adaptive methods. She actively contributes to conferences such as the Biennial Numerical Analysis Conference (2024) and plans to lead workshops on adaptive finite element methods. Contact: Office S4|10 101a, TU Darmstadt | tscherpel@mathematik.tu-darmstadt.de

Holger Marschallمشاهده پروفایل

پژوهشگر

• Computational Fluid Dynamics
• Multiphase Flows
• Numerical Simulations
• +۳ مورد دیگر

Dr. Holger Marschall is a Researcher and group leader at the Technical University of Darmstadt, specializing in Computational Multiphase Flow . His work focuses on developing high-fidelity numerical methods for multiphase flows using the OpenFOAM® C++ library, with applications in chemical reactors and fluid dynamics. Research Interests : Computational Fluid Dynamics (CFD), multiphase flows, numerical simulations, interfacial transport, and OpenFOAM technology. Academic Affiliation : Technische Universität Darmstadt, Center of Smart Interfaces, Mathematical Modeling and Analysis Group. Education : Dipl.-Ing. in Chemical Engineering from TU München (2006), Dr.-Ing. from TU München (2010, summa cum laude). His research spans method development, code implementation, and technical applications, particularly in multi-scale two-phase flows and reactive mass transfer . He has taught courses on computational fluid dynamics, reactor design, and heat/mass transfer. Recent publications emphasize phase-field methods , adaptive mesh refinement , droplet dynamics , and interfacial relaxation , with a focus on both fundamental and applied multiphase flow problems. Key journals include Physics of Fluids , Nature Physics , and Chemie Ingenieur Technik .

Marc Alexander Schweitzerمشاهده پروفایل

استاد

• Numerical Methods
• Computational Mechanics
• Meshfree Methods
• +۴ مورد دیگر

Marc Alexander Schweitzer is a Professor of Mathematics at the University of Bonn and affiliated with Fraunhofer SCAI. His work focuses on numerical methods and computational mechanics, particularly meshfree methods, peridynamics, and algebraic multigrid techniques. He contributes to advancing computational tools for engineering and materials science applications. Research interests include partition of unity methods, multiscale modeling, fracture mechanics, and efficient linear solvers. His work integrates mathematical theory with practical computational challenges, such as adaptive algorithms and parallel computing. Publications highlight contributions to peridynamic fracture models, scalable solvers for material design, and visualization of fracture progression in particle-based systems. Collaborative efforts with Fraunhofer SCAI emphasize industrial applications and algorithmic innovation.

Erkki Somersaloمشاهده پروفایل

استاد

• Inverse Problems
• Biomedical Imaging
• Mathematical Biology
• +۳ مورد دیگر

Erkki Somersalo is a Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. His research focuses on inverse problems with applications in biomedical imaging and mathematical biology. He holds a PhD from the University of Helsinki and has contributed extensively to imaging modalities like Electrical Impedance Tomography (EIT), Magnetoencephalography (MEG), and brain metabolism modeling. Research Interests include: Inverse problems in biomedical imaging Bayesian statistical methods for computational science Mathematical models of brain energetics and neuromuscular systems Development of hierarchical Bayesian models for sparse data Recent work emphasizes interdisciplinary applications, such as modeling pandemic dynamics, muscle recruitment variability, and astrocyte-neuron metabolic interactions. His research often combines advanced numerical methods with biological data analysis. Advising and Grants: While specific student names are not listed, his work involves collaborative projects with researchers in neuroscience, biophysics, and computational science. He has contributed to grant-funded initiatives exploring brain metabolism and pandemic modeling.

Irene Kyzaمشاهده پروفایل

مدرس

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

Dr. Irene Kyza is a Lecturer in Computational Mathematics at the University of St Andrews' School of Mathematics and Statistics. Her research focuses on developing advanced numerical methods for solving complex partial differential equations arising in physical systems. Core research areas include: Structure-preserving algorithms for Schrödinger-type equations A posteriori error analysis for nonlinear evolution equations Adaptive numerical methods for blow-up detection Wave dynamics and instability phenomena Her publication portfolio demonstrates consistent focus on numerical analysis of wave phenomena, particularly Schrödinger equations and hydrodynamic models. Recent work emphasizes efficient approximation techniques for ocean wave modeling and stochastic sea states. She currently supervises PhD candidate Yilin Wang and contributes to research on adaptive time-stepping schemes and regularization techniques for singular solutions.

Nikolaos Sfakianakisمشاهده پروفایل

مدرس

• Cancer Invasion Modeling
• Mathematical Biology
• Applied Mathematics
• +۳ مورد دیگر

Dr Nikolaos Sfakianakis is a Lecturer in Applied Mathematics at the University of St Andrews' School of Mathematics and Statistics. His research focuses on mathematical modeling of cancer invasion, particularly developing hybrid multiscale frameworks to study tumor progression and metastasis. He has contributed to understanding phenotypic transitioning in cancer cells, stochastic models of cell migration, and adaptive numerical methods for cancer invasion simulations. His work bridges theoretical mathematics with biological applications, emphasizing computational techniques to analyze tumor dynamics and tissue interactions. Dr Sfakianakis has collaborated on projects involving glioma invasion, receptor binding mechanisms, and flux-limited PDE models. He supervises PhD students in applied mathematics and computational oncology, and his research has implications for optimizing cancer treatment strategies through mathematical analysis. Key research areas include cancer cell migration, tumor microenvironment dynamics, and multiscale modeling techniques. His recent publications explore 3D cancer invasion frameworks, stochastic differential equation models for cell behavior, and adaptive mesh refinement for numerical simulations. He has organized interdisciplinary conferences and contributed to collaborative initiatives like the Radical Health AI project for cancer treatment optimization.

Xiaochuan Tianمشاهده پروفایل

دانشیار

• Mathematical Modeling
• Applied Analysis
• Numerical Analysis
• +۴ مورد دیگر

Dr. Xiaochuan Tian is an Associate Professor in the Department of Mathematics at the University of California, San Diego (UCSD). She holds a PhD in Applied Mathematics from Columbia University (2017) and has held academic positions including Assistant Professor at UCSD (2020–2024), R.H. Bing Instructor at the University of Texas at Austin (2017–2020), and Postdoctoral Researcher at Columbia (2017). Her research focuses on numerical analysis, nonlocal models, fractional PDEs, and meshfree methods. She has secured grants including an NSF CAREER Award (2023) and Alfred P. Sloan Fellowship (2023). Research interests include nonlocal modeling, calculus of variations, asymptotic compatibility, and multiscale methods. Notable contributions span nonlocal function spaces, asymptotically compatible schemes, and numerical methods for integro-differential equations. She advises three PhD students and teaches advanced courses such as Numerical PDEs and Computational Mathematics. Her work has been recognized with awards like the SIAM Review SIGEST Award (2019) and AWM Dissertation Prize (2018). Grants: NSF DMS-2240180 (2023–2028) NSF DMS-2111608 (2021–2024) Awards: ICBS Frontiers of Science Award (2024) SIAM Outstanding Paper Prize (2016) Labs/Teams: Center for Computational Mathematics (CCoM) at UCSD Research Group on Nonlocal Modeling

Fabian Heitschمشاهده پروفایل

استاد مدعو

• Astrophysics
• Fluid Dynamics
• Star Formation
• +۳ مورد دیگر

Fabian Heitsch is an Adjunct Professor in the Department of Physics & Astronomy at the University of North Carolina Chapel Hill. He leads the Astrophysical Fluid Dynamics Lab (AFDL@UNC), focusing on simulating how matter cycles through galaxies, including star formation from gas clouds and their explosive stellar endpoints. His work bridges theory and observation via supercomputer fluid dynamics simulations. He teaches interdisciplinary courses like 'Myths, Moons and Methods' with Professors Marc Lange (Philosophy) and James Rives (Classics), and plans a new course on science's societal role through SCiLL's curriculum. Education: PhD (Physics), Universität Heidelberg (2001): 'Turbulence and Fragmentation in Molecular Clouds' MS (Astronomy), Universität Bonn (1998) BS (Physics), Universität Bonn (1997) BA (Math & Computer Science), Universität Münster (1993) Research Interests: His research centers on astrophysical fluid dynamics, magnetohydrodynamics, and interstellar medium evolution. Key topics include high-velocity clouds' role in galactic star formation, magnetic field dynamics in shocks, and computational modeling of star formation processes. His lab explores projects ranging from star formation near black holes to molecular cloud chemistry and 3D HVC orientation using MCMC models. Recent Work Trends: His articles analyze fluid dynamics in galactic contexts, including simulations of starburst-driven winds, magnetized colliding flows, and the evolution of protostellar cores. Recent focuses include dust content in galactic winds and the impact of magnetic fields on interstellar gas dynamics. Labs/Teams: Directs the Astrophysical Fluid Dynamics Lab, fostering undergraduate and graduate research in areas like black hole star formation, interstellar chemistry, and high-velocity cloud studies.

• Computational Fluid Dynamics
• Biomedical Engineering
• Numerical Analysis
• +۷ مورد دیگر

Johan Hoffman is a Professor of Numerical Analysis at KTH Royal Institute of Technology, Stockholm, Sweden, and Deputy Head of the Division of Computational Science and Technology. He leads research in computational science, with a focus on fluid dynamics, biomedical engineering, and numerical methods. His work integrates fundamental mathematics with applied research in aerodynamics, urban planning, renewable energy, and biomedicine. Hoffman is a founder of the FEniCS open-source software project and a partner in the EU’s GENEX project on digital twins. Education: Ph.D. in Applied Mathematics, Chalmers University of Technology Postdoctoral Fellow, Courant Institute, New York University Visiting Researcher at Oxford University, Stanford University, and others Research Interests: Hoffman’s research spans computational fluid dynamics, turbulence modeling, finite element methods, and biomedical applications such as cardiovascular simulations. He develops adaptive numerical methods and high-performance computing frameworks for complex systems. Publications Trends: Recent work emphasizes fluid-structure interaction in biomedical contexts (e.g., mitral valves), turbulence analysis, and multiphysics simulations in renewable energy. His methods prioritize stability, adaptivity, and scalability for large-scale problems. Awards: ERC Proof of Concept Grant (2015) Ingvar Carlsson Award (2005) Leslie Fox Prize (2005) Bill Morton Prize (2001) Advising & Grants: Hoffman’s funding includes grants from the Swedish Research Council, ERC, and EU Horizon 2020. He supervises research on digital twins, turbulence modeling, and biomedical simulation. His lab collaborates with industry and academic partners globally. Labs/Teams: Core contributor to the FEniCS project and leader of computational fluid dynamics teams at KTH, advancing open-source tools for multiphysics simulation.

Peter K Swebyمشاهده پروفایل

دانشیار

• Numerical Analysis
• Computational Fluid Dynamics
• Hyperbolic Conservation Laws
• +۵ مورد دیگر

Dr. Peter K. Sweby is an Associate Professor and Acting Head of the Department of Mathematics and Statistics at the University of Reading. He holds roles including Departmental Examinations Officer and MMath Project Coordinator. His professional affiliations include Chartered Mathematician (CMath), Fellow of the Higher Education Academy (FHEA), and Membership of the Institute of Mathematics and its Applications (MIMA). Dr. Sweby has a B.Sc. (1st class honours) in Mathematics from Bristol University (1979) and a Ph.D. in Mathematics from the University of Reading (1983). His research focuses on numerical methods for hyperbolic conservation laws, particularly Total Variation Diminishing (TVD) schemes, grid adaptation, sediment transport dynamics, and mathematical biology applications like cholesterol biosynthesis modeling. His work spans computational fluid dynamics, including adaptive mesh ALE methods and ghost fluid approaches for compressible flows. Key contributions include high-resolution schemes for bed-load sediment transport and box schemes for transcritical flow. His publications highlight advancements in numerical stability, spurious solution analysis, and morphodynamic modeling with data assimilation. Research Trends: Focus on numerical methods for hyperbolic systems, adaptive mesh techniques, and interdisciplinary applications in environmental and biological systems. Key Themes: Development of robust numerical algorithms, analysis of discretization dynamics, and integration of mathematical models with real-world phenomena. Dr. Sweby has supervised 14 PhD/MSc students, including work on flux limiter schemes, water wave scattering, and morphodynamic equations. He has led research on coastal sediment modeling and contributed to projects like 'Changing coastlines: data assimilation for morphodynamic prediction.' Grants/Labs: Involved with Numerical Analysis and Computational Modelling research groups, and contributed to EPSRC-funded morphodynamic studies. Labs/Teams: Active in the School of Mathematical, Physical and Computational Sciences, collaborating on interdisciplinary projects linking mathematics with environmental science and biology.

Marco Fossatiمشاهده پروفایل

مدرس ارشد

• Computational Fluid Dynamics
• Multiphysics Non-equilibrium Aerodynamics
• Reduced Order Modeling
• +۵ مورد دیگر

Dr. Marco Fossati is a Senior Lecturer in Mechanical and Aerospace Engineering at the University of Strathclyde, where he leads the FASTT-Lab (Future Air-Space Transportation Technologies) and is a member of the Aerospace Centre of Excellence (ACE). He holds a PhD in Computational Fluid Dynamics from Politecnico di Milano and has prior experience as Associate Director of McGill University's CFD Laboratory and Adjunct Professor. His research focuses on multiphysics non-equilibrium aerodynamics, data-driven reduced order methods, and aerodynamic design for next-generation aircraft and UAVs. He has contributed to projects like the EU-funded INDIGO initiative and leads studies on atmospheric re-entry aerothermodynamics, drone logistics networks for healthcare, and resilient airport operations. Education: PhD in Computational Fluid Dynamics, Politecnico di Milano (2008) MEng in Aerospace Engineering, Politecnico di Milano (2002) Research Highlights: Development of SU2-NEMO code for nonequilibrium flows Data-driven modeling for drone logistics and re-entry signature detection Mesh optimization for high-speed aerodynamics Professional Activities: Associate Editor, Computers and Fluids Peer reviewer for major journals (e.g., Journal of Computational Physics, AIAA Journal) Member of Parallel CFD conference advisory committee Projects: Protecting Earth from Space Debris Re-Entry (2024–2027) Hyperspectral Detection of Re-Entry Signatures (2024–2025) INDIGO: Low-Emission Aircraft Technologies (2023–2026)

Michael Schneierمشاهده پروفایل

استاد مدعو

• Computational Mathematics
• Uncertainty Quantification
• Machine Learning
• +۳ مورد دیگر

Michael Schneier is an adjunct professor in the Department of Mathematics at the University of Pittsburgh. He holds a Ph.D. in Scientific Computing from Florida State University (2018), advised by Professor Max Gunzburger. His research focuses on computational mathematics, uncertainty quantification, machine learning, reduced order modeling, and computational fluid dynamics, with a particular emphasis on leveraging data to enhance traditional computational fluid dynamics algorithms. Research interests include advancing numerical methods for partial differential equations, turbulence modeling, and the integration of machine learning techniques into fluid dynamics simulations. His work emphasizes improving algorithmic efficiency and accuracy through innovative approaches like reduced-order models and latent diffusion frameworks. Selected publications explore topics such as latent neural PDE solvers, turbulence diagnostics, and ensemble-based numerical methods for fluid flows. His contributions span both theoretical advancements and practical applications in computational science. No scientific awards or grants are explicitly listed in the provided information. He currently advises no students, though his research may involve collaborative projects. His work is centered at the Department of Mathematics within the Dietrich School of Arts and Sciences.