جست‌وجوی استاد و پژوهشگر

Yongtao Zhangمشاهده پروفایل

استاد

• Computational Physics
• Mathematical and Computational Biology
• Numerical Solution of Differential Equations
• +۳ مورد دیگر

Yongtao Zhang is a Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He holds a PhD from Brown University (2003) and an M.S. from Nankai University (1999). His research focuses on developing high-order numerical methods for partial differential equations (PDEs), including weighted essentially non-oscillatory (WENO) schemes, discontinuous Galerkin (DG) methods, and implicit integration factor (IIF) methods. These methods are applied to computational physics (e.g., shock wave dynamics, fluid flows) and computational biology (e.g., morphogenesis, tissue patterning). Dr. Zhang's work emphasizes numerical solutions for hyperbolic conservation laws, stiff advection-reaction-diffusion equations, and Hamilton-Jacobi equations. He has contributed to sparse-grid implementations, fast sweeping methods, and exponential time differencing schemes to enhance computational efficiency and accuracy. His research bridges theoretical advancements with practical applications in high-dimensional systems and complex geometries. He is an editorial board member for Journal of Scientific Computing and Communications on Applied Mathematics and Computation , reflecting his leadership in the field. Teaching responsibilities include numerical analysis, scientific computing, and applied mathematics courses. His work has been published in leading journals such as Journal of Computational Physics and SIAM Journal on Scientific Computing .

Thomas Abadieمشاهده پروفایل

استادیار

• Fluid Mechanics
• Multiphase Flows
• Interfacial Phenomena
• +۵ مورد دیگر

Thomas Abadie is an Assistant Professor at the University of Birmingham's School of Chemical Engineering. His research focuses on fluid mechanics, multiphase flows, and interfacial phenomena with applications in environmental and industrial challenges. He employs experimental techniques (e.g., Particle Imaging Velocimetry) and numerical methods (e.g., interface capturing/tracking) to study bubble dynamics, complex fluids, and mass transfer processes. His work spans scales from microreactors to industrial aeration systems. Abadie holds a PhD in Fluid Mechanics (2013) and an MSc in Fluid Mechanics, Energy, and Transport Phenomena from the University of Toulouse (2010). He teaches courses on transport phenomena and supervises undergraduate and graduate research projects. His research explores topics such as viscoelastic fluid dynamics, Richtmyer-Meshkov instabilities, and graphene synthesis. Recent studies emphasize machine learning integration with multiphase flow modeling and data-driven approaches to drop size distributions. His work bridges computational methodologies with real-world applications in energy and environmental systems. Abadie serves as a reviewer for journals like Chemical Engineering Science and Journal of Fluid Mechanics , and consults on CFD and multiphase systems. His collaborations span academia and industry, addressing challenges in wastewater treatment and renewable energy systems.

• Computational Astroparticle Physics
• Cosmic-Ray Propagation
• Gamma-Ray Binaries
• +۷ مورد دیگر

Ralf Kissmann is an Associate Professor at the Institute for Astro- and Particle Physics, Faculty of Mathematics, Computer Science and Physics, University of Innsbruck, Austria. He leads the Computational Astroparticle Physics research group and is actively involved in the Cherenkov Telescope Array (CTA) Consortium. His research focuses on computational astroparticle physics , particularly the numerical modeling of cosmic-ray propagation in the Milky Way using the PICARD code, and high-resolution simulations of gamma-ray binaries such as LS 5039 using the CRONOS magnetohydrodynamics code. His work addresses particle acceleration in colliding-wind binaries, anisotropic diffusion, 3D galactic gas distributions, and the interpretation of diffuse gamma-ray emission. He investigates both hadronic and leptonic processes in extreme astrophysical environments. The recent trends in his publications (2021–2025) highlight his central role in CTA science preparation , contributing to sensitivity studies for dark matter, cosmology, PeVatrons, and galactic surveys. His work combines advanced numerical methods with high-energy astrophysical observations, bridging theory and experiment. He has presented at numerous international conferences, including Astronum, TeVPA, and ICRC, often by invitation, and has delivered public outreach lectures on space weather and astrophysics. Associate Professor, University of Innsbruck Research Group: Computational Astroparticle Physics Key Collaborations: Cherenkov Telescope Array (CTA) Consortium Prof. Kissmann mentors students and early-career researchers, supervising work in numerical astrophysics, and has secured research funding for projects on galactic cosmic-ray transport and gas dynamics. He is a key contributor to the development and application of simulation codes that are vital for interpreting current and future gamma-ray observations. He is actively involved in research teams focused on galactic cosmic-ray propagation, gamma-ray binaries, and CTA science working groups. His lab utilizes high-performance computing for large-scale simulations of astrophysical plasmas and particle transport.

Chi-Wang Shuمشاهده پروفایل

استاد

• Numerical Solutions of Conservation Laws
• Computational Fluid Dynamics (CFD)
• Discontinuous Galerkin Methods
• +۵ مورد دیگر

Chi-Wang Shu is the Stowell University Professor of Applied Mathematics and former Chair of the Division of Applied Mathematics at Brown University. His research focuses on high-order numerical methods for convection-dominated partial differential equations, including ENO/WENO schemes, discontinuous Galerkin methods, and spectral methods. He has contributed extensively to computational fluid dynamics, semiconductor modeling, and Hamilton-Jacobi equations. Shu has held academic positions at Brown since 1987 and has received accolades such as the SIAM/ACM Prize in Computational Science (2007) and SIAM/AMS Fellowships (2009, 2012). His work bridges theoretical development and practical applications in fluid dynamics, astrophysics, and materials science. Education: B.S., University of Science and Technology of China (1982); Ph.D., UCLA (1986). Affiliations: Brown University (1987–present), Chair of Applied Mathematics (1999–2005). Research interests span numerical methods for conservation laws, with emphasis on shock-capturing algorithms and their application to multiphysics problems. Notable contributions include the development of WENO schemes and DG methods for complex flows. He has authored over 400 publications and advised numerous students in computational mathematics and engineering. Key awards: NASA Public Service Group Achievement Award (1992), Feng Kang Prize (1995), ISI Highly Cited Author (2004).

Tong Wuمشاهده پروفایل

استادیار

• Applied Mathematics
• Numerical Analysis
• Computational Mathematics
• +۹ مورد دیگر

Tong Wu is an Assistant Professor in the Department of Mathematics at The University of Texas at San Antonio (UTSA), part of the College of Sciences. He specializes in applied and computational mathematics, with a focus on developing robust numerical methods for nonlinear partial differential equations and data assimilation. Ph.D. in Mathematics, Tulane University B.S. in Mathematics and Applied Mathematics, Xi'an Jiaotong University His research lies at the intersection of numerical analysis, fluid dynamics, and mathematical biology. He develops innovative numerical schemes—such as adaptive moving mesh central-upwind and upwind methods—for solving hyperbolic systems, shallow water equations, chemotaxis models, and stiff ordinary differential equations. His work emphasizes properties like well-balancing, positivity preservation, and high resolution in capturing shocks and complex structures. His publications reveal a consistent focus on improving numerical accuracy and efficiency in modeling physical and biological systems. Key themes include the development of algorithms for shallow water models with wet/dry fronts and friction, moving-water equilibria preservation, and adaptive mesh refinement. He also contributes to computational epidemiology by designing bipartite network generation algorithms that preserve joint degree distributions, enabling realistic modeling of disease spread. Tong Wu has not received any explicitly mentioned scientific awards or fellowships in the provided text. He is actively involved in teaching core mathematics courses such as Calculus I–III, Linear Algebra, and Scientific Computing. While no formal advising roles or grant funding are listed, his collaborative research with leading figures in applied mathematics indicates an active academic trajectory. He does not appear to lead a formal lab or research team, but his work is deeply collaborative and computational in nature.

Peter Thomasمشاهده پروفایل

دانشیار

• Flight Dynamics
• Autonomous Flight Control Systems
• Biomimetic Flight
• +۴ مورد دیگر

Dr Peter Thomas serves as head of the aerospace group and principal lecturer in aerospace engineering at the University of Hertfordshire's School of Physics, Engineering and Computer Science. He is an active member of the Centre for Engineering Research and teaches flight mechanics, stability and control systems, aeroelasticity, and aerodynamics. His academic qualifications: MEng Mechanical Engineering, University of Warwick PhD Aerospace Engineering, Cranfield University Research focuses on flight dynamics, autonomous flight control systems, and biomimetic flight. Expertise includes UAVs, air-to-air refuelling, vortex lattice methods, and experimental testing. He possesses over a decade of programming experience in MATLAB/Simulink for simulation and control implementation. Recent publications reveal strong trends in supersonic aerodynamics (using shock cone modeling and compressibility corrections), UAV motion planning with geofencing algorithms, and biomimetic wing morphing structures. His work integrates computational fluid dynamics with experimental validation to advance autonomous systems and sustainable aviation solutions. Current research projects include decarbonising London Elstree Aerodrome (2024-2025), origami-based debris capture for CubeSat satellites (2022), and lattice structures for aerospace components (2022-2023). He previously collaborated with Cobham Mission Equipment on autonomous air-to-air refuelling and served as Honorary Research Associate at the University of Bristol (2014-2016).

Will Paznerمشاهده پروفایل

استادیار

• Numerical Analysis
• Scientific Computing
• High-Order Finite Element Methods
• +۵ مورد دیگر

Will Pazner is an Assistant Professor in the Fariborz Maseeh Department of Mathematics and Statistics at Portland State University, with research affiliations at Lawrence Berkeley National Laboratory. His work bridges applied mathematics and high-performance computing, focusing on developing advanced numerical methods for scientific applications. Dr. Pazner's research interests center on numerical analysis and scientific computing, with particular expertise in high-order finite element methods, discontinuous Galerkin methods, fast solvers, and GPU-based supercomputing. His work addresses fundamental challenges in computational fluid dynamics and related fields, developing methods that balance accuracy, efficiency, and robustness for extreme-scale computing environments. His recent publications reveal strong trends in developing preconditioners for high-order methods, advancing GPU-accelerated solvers, and creating novel techniques for conservation laws and multiphysics problems. The research spans applications from fluid dynamics to optimal transport, with increasing focus on heterogeneous computing architectures and exascale-ready algorithms. Sigma Xi Columbia-Willamette Outstanding Faculty Researcher Award (2024) $1M NSF regional cyberinfrastructure award (2024) Ralph E. Powe Junior Faculty Enhancement Award (2023) Sidney Fernbach Postdoctoral Fellowship (2018) First Place, AIAA Student Paper Competition (2017) Dr. Pazner maintains active collaborations with national laboratories, particularly through the Exascale Computing Project, and has secured significant research funding. His teaching portfolio includes advanced numerical analysis courses that integrate cutting-edge research with computational practice. At Lawrence Berkeley National Laboratory, he contributes to the Mathematics Group's efforts in developing next-generation computational algorithms.

Sivakumaran Nadarajahمشاهده پروفایل

استاد

• Aerodynamics
• Fluid Mechanics
• Computational Fluid Dynamics
• +۵ مورد دیگر

Sivakumaran Nadarajah is a Professor at the Department of Mechanical Engineering within McGill University's Faculty of Engineering . Holding a Ph.D. and M.S. from Stanford University and a B.Sc. from the University of Kansas, he directs the Computational Aerodynamics Group. Ph.D., Stanford University M.S., Stanford University B.Sc., University of Kansas His research focuses on advanced numerical techniques for computational fluid dynamics (CFD) , mathematical methods in aerodynamic shape optimization , and high-performance parallel computing . He teaches courses like MECH 289: Design Graphics , MECH 331: Fluid Mechanics 1 , and MECH 539: Computational Aerodynamics . Current projects include Remote Inverse Adjoint Algorithms for Sonic Boom Reduction and multidisciplinary aircraft optimization. Publications highlight expertise in adjoint-based optimization , high-order numerical schemes , and unsteady flow modeling applied to helicopter rotors, wind turbines, and engine blades. He contributes to both fundamental and applied research in aerospace systems. Labs & Groups : Computational Aerodynamics Group

• Computational Imaging
• Inverse Problems
• Numerical Analysis
• +۲ مورد دیگر

Greg Buzzard is Professor of Mathematics and Director of the Center for Computational & Applied Mathematics (CCAM) at Purdue University's College of Science. His research develops mathematical foundations for computational imaging systems through collaborative work with electrical engineering and materials science researchers. Dr. Buzzard leads a substantial research group focusing on inverse problems in imaging science. His work combines theoretical advances in optimization with practical applications in electron microscopy, neutron tomography, and medical imaging. Key research areas include: Consensus Equilibrium frameworks for model integration Dynamic sampling algorithms for computational imaging Plug-and-play methods for image reconstruction Multi-agent systems for large-scale inverse problems Recent publications demonstrate leadership in computational tomography innovations, particularly neutron imaging techniques for materials science and spectral CT reconstruction. His team's methods enable faster, higher-resolution imaging across scientific domains from biomedical applications to aerospace materials characterization. The 2020 SIAM Imaging Sciences Best Paper Prize recognized fundamental contributions to Plug-and-Play reconstruction methods. Professor Buzzard maintains an active mentoring program with 9 current graduate students and postdocs. His interdisciplinary collaborations include sustained partnerships with the Purdue School of Electrical and Computer Engineering and materials science research groups.

• Numerical Analysis
• Computational Fluid Dynamics
• Partial Differential Equations
• +۳ مورد دیگر

Aleksey Sikstel is a part-time faculty member at RWTH Aachen University , holding the title of Vertr.-Prof. (Part-Time Lecturer). His research focuses on advanced numerical methods for hyperbolic conservation laws, including Discontinuous Galerkin schemes, stochastic Galerkin formulations, and error estimation techniques. Key research areas: Numerical Analysis, Computational Fluid Dynamics, and Applied Mathematics Primary affiliation: Faculty 10 Institutions at RWTH Aachen University Contact: aleksey.sikstel@rwth-aachen.de His work emphasizes coupling hyperbolic systems, entropy-stable discretizations, and adaptive grid methods. Recent publications analyze Baer-Nunziato-type models, multiresolution strategies, and boundary control for hyperbolic equations.

• Computational Fluid Dynamics
• Marine Propulsion
• Magnetohydrodynamics
• +۷ مورد دیگر

Chunlei Liang is a Professor in the Department of Mechanical & Aerospace Engineering at Clarkson University, affiliated with The Center for Advanced Materials Processing (CAMP). He holds editorial roles at the ASME Journal of Fluids Engineering and Computers & Fluids. His expertise spans Computational Fluid Dynamics (CFD), Magnetohydrodynamics (MHD), and fluid-structure interaction, with a focus on high-order numerical methods for unstructured grids. Dr. Liang has led NSF/AFOSR-funded projects developing solvers like CHORUS-MHD for solar convection simulations and the Selig20 ducted turbine. Education: Ph.D., University of London, 2005 Postdoc, Stanford University, 2007–2010 Research Interests: Dr. Liang's work integrates advanced numerical techniques with engineering applications: High-order spectral difference methods Magnetohydrodynamic simulations Turbomachinery and marine propulsion Unsteady flow modeling (e.g., ducted wind turbines) Algorithm development for complex geometries Key Publications: Recent work includes high-order solvers for MHD equations, turbulence modeling in ducted systems, and astro-physical convection studies. His methods emphasize divergence cleaning and adaptive meshing for unstructured grids. Awards: Presidential Early Career Award for Scientists and Engineers (2019) NSF CAREER Award (2016) ONR Young Investigator Award (2014) Advising & Grants: Supervised 13 PhD students and 10+ master's candidates. Active grants include AFOSR support for MHD modeling and NSF funding for solar convection simulations. Collaborates with institutions like Lawrence Livermore National Lab and Dassault Systèmes. Labs & Teams: Leads the CHORUS-MHD research group, with ongoing projects on fluid-structure interaction in marine systems and high-order CFD for aerospace applications.

Dave Marcumمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Mesh Generation
• High-Performance Computing
• +۱ مورد دیگر

Dave Marcum is the ExxonMobil Professor and Chief Scientist in Computational Fluid Dynamics at Mississippi State University's Center for Advanced Vehicular Systems (CAVS). With a Ph.D. from Purdue University, he leads research in unstructured mesh generation and high-performance computing. His work integrates computational methods with aerospace and automotive engineering. Research interests include adaptive mesh refinement, viscous flow simulations, and parallel algorithm design. His publications emphasize CFD optimization for industrial applications, such as missile aerodynamics and propeller design. Awards include ExxonMobil Endowed Professorship, Inria International Chair, and multiple outstanding researcher recognitions. He collaborates globally with institutions like MIT and INRIA. Key labs include CAVS and HPC², focusing on scalable CFD solutions for DoD, NASA, and aerospace partners.

• Partial differential equations
• Numerical methods for PDEs
• Finite element approximation
• +۳ مورد دیگر

Professor Jean-Luc Guermond holds the Mobil Chair in Computational Science at Texas A&M University. His research focuses on numerical methods for partial differential equations (PDEs), computational fluid dynamics, and finite element approximation. He has made significant contributions to invariant-domain preserving schemes, high-order time integration methods, and the development of robust numerical techniques for hyperbolic systems and conservation laws. His work bridges theoretical advancements with practical applications in fluid dynamics, electromagnetics, and magnetohydrodynamics. Key research areas include the analysis of finite element methods, discontinuous Galerkin discretizations, and the design of algorithms that preserve physical invariants such as mass, momentum, and energy. Guermond’s publications often address challenges in simulating complex phenomena like vortex reconnection, turbulent swirling flows, and dynamo mechanisms in geophysical contexts. He has also contributed to the development of preconditioning strategies for Navier-Stokes equations and high-order Runge-Kutta schemes. His work emphasizes both mathematical rigor and computational efficiency, with applications ranging from engineering simulations to environmental modeling. Despite extensive contributions to the field, no specific awards are mentioned in the provided texts. Collaborative efforts with experimental teams highlight his interdisciplinary approach to solving real-world fluid dynamics problems.

Gregor Gassnerمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Numerical Analysis
• High-Performance Computing
• +۷ مورد دیگر

Gregor Gassner is a Professor at the University of Cologne's Mathematical Institute, specializing in numerical methods for fluid dynamics and high-performance computing. He leads research in entropy-stable discontinuous Galerkin (DG) methods, focusing on robust and efficient simulations of multi-scale problems governed by conservation laws. His work includes adaptive algorithms for compressible flows, magnetohydrodynamics (MHD), and oceanography, supported by an ERC Starting Grant targeting 'un-crashable' solvers. Key research areas: Non-linear multi-scale simulations High-order DG methods Entropy stability and robustness Exascale computing (e.g., SCALEXA project) Adaptive mesh refinement Subcell limiting techniques Publications emphasize advancements in DG schemes, split-form discretizations, and applications to aerodynamics, plasma physics, and geophysical flows. His ERC grant aims to unify efficacy and robustness in numerical methods. Grants/Awards: ERC Starting Grant for developing robust numerical solvers. Labs/Teams: Core scientist at the Center for Data and Simulation Science, leading the NumSim group.

Samuel Grauerمشاهده پروفایل

استادیار

• Optical Diagnostics
• Fluid Dynamics
• Uncertainty Quantification
• +۴ مورد دیگر

Samuel Grauer is an Assistant Professor in the Department of Mechanical Engineering at Pennsylvania State University. His research focuses on inverse problems in optical diagnostics, with applications to fluid dynamics, combustion, and uncertainty quantification. He earned a PhD from the University of Waterloo (2018) and a BSc from the University of Manitoba (2014), followed by a postdoctoral fellowship at Georgia Tech's Ben T. Zinn Combustion Laboratory (2018-2020). His research group develops advanced optical diagnostics techniques such as particle image velocimetry (PIV/PTV), background-oriented schlieren (BOS), and hyperspectral absorption tomography. These methods are applied to study high-speed turbulent flows, detonations, and heterogeneous materials. Key themes include differentiable programming, Bayesian inference, and scientific machine learning for flow reconstruction and uncertainty quantification. Current students include PhD candidates Joseph P Molnar, Ke Zhou, and Amit K Singh, along with MS/BS researchers focusing on computational fluid dynamics and experimental diagnostics. His work has been published in leading journals such as Journal of Computational Physics , Combustion and Flame , and Applied Optics . Professional affiliations include Google Scholar and ResearchGate profiles. His lab collaborates internationally, with equipment spanning high-speed imaging systems and advanced laser diagnostics. Prospective students with programming skills and interests in fluid physics are encouraged to contact him via sgrauer@psu.edu.