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Henry BoatengView profile

Associate Professor

• Scientific Computing
• Computational Chemistry
• Materials Science
• +4 more

Henry Boateng is an Associate Professor in the Department of Mathematics at San Francisco State University (SFSU), affiliated with the College of Science & Engineering. His research focuses on scientific computing, computational chemistry, and numerical analysis with applications in particle methods, materials science, and randomized linear algebra. He develops advanced algorithms for electrostatic interactions, treecode methods, and hierarchical clustering. Education details are not explicitly stated but inferred from his academic role. His work integrates computational mathematics with interdisciplinary applications, supported by grants from NSF and the U.S. Department of Energy. He has developed multiple software tools in C++, Fortran, and Python for treecode implementations, including the Periodic Coulomb Tree Method and tricubic interpolation-based algorithms. Research interests include mesh-free methods, multipolar electrostatics, and high-performance computing. His publications emphasize algorithm design for efficiency and accuracy in large-scale particle systems. He teaches courses in linear algebra, numerical analysis, and computational mathematics at SFSU and previously at Bates College.

Nadine AubryView profile

Professor

• Fluid Dynamics
• Turbulence Modeling
• Microfluidics
• +5 more

Nadine Aubry is Professor and Senior Advisor to the Dean of Engineering at Tufts University School of Engineering, Department of Mechanical Engineering. An internationally recognized scholar, she previously served as Provost and Senior Vice President at Tufts (2019-2021), Dean of Engineering at Northeastern University (2012-2019), and Department Head at Carnegie Mellon University. Her leadership spans multiple institutions with global impact. Education: Ph.D. in Mechanical and Aerospace Engineering, Cornell University (1987) M.S. in Mechanical Engineering, Université Scientifique et Médicale de Grenoble B.S., Grenoble - Institut National Polytechnique Research Focus: Dr. Aubry pioneers computational approaches in fluid dynamics, specializing in turbulence modeling, microfluidics, and electrohydrodynamics. Her work integrates dynamical systems theory with nanoparticle manipulation and biofluid applications. Recent research emphasizes machine learning-enhanced methods for flow prediction, convective heat transfer optimization, and aerodynamic design using physics-informed neural networks. Publication Trends: Recent articles demonstrate strong emphasis on machine learning applications in fluid mechanics and thermal systems. Dominant themes include physics-informed neural networks for flow prediction, deep reinforcement learning for active flow control, convolutional networks for aerodynamic optimization, and hybrid AI methods for multiphysics problems across aerospace, electronics cooling, and biomedical domains. Awards & Honors: Elected Member: U.S. National Academy of Engineering Fellow: American Academy of Arts & Sciences, American Physical Society, ASME, AAAS, AIAA G.I. Taylor Medal for Fluid Mechanics Research National Academy of Inventors Fellow C.C. Mei Distinguished Lecturer Grants & Leadership: Secured USDA funding for aging research (2014-2019). Chaired International Union of Theoretical and Applied Mechanics assemblies globally. Serves on National Academy of Engineering prize committees and governance boards.

Yue LingView profile

Associate Professor

• Multiphase Flows
• Atomization & Sprays
• Computational Fluid Dynamics
• +3 more

Yue (Stanley) Ling is an Associate Professor in Mechanical and Aerospace Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. He directs the Computational Thermo-Fluids Laboratory, where his team develops advanced numerical methods for high-fidelity simulation of multiphase flows and heat transfer processes. Research focuses on atomization mechanisms, droplet/bubble dynamics with phase change, and fluid-surface interactions, using adaptive mesh refinement and massively parallel computing. Applications span fuel injection, thermal management, aerodynamics, and environmental processes. Dr. Ling's NSF CAREER Award (2020) supports fundamental research on spray formation mechanisms under turbulent conditions. Education: Ph.D. in Mechanical Engineering, University of Florida (2010) B.S. in Engineering Mechanics, Beihang University (2004) Recent publications develop conservative volume-of-fluid methods for compressible multiphase flows and characterize turbulence-interface interactions. Awards include ASME's Best Video Presentation (2021) and AIAA's Flow Visualization honor (2019). Dr. Ling leads multiple NSF-funded projects and collaborates internationally through the PARIS (Parallel, Robust, Interface Simulator) consortium.

• Computational Modelling
• Solid Mechanics
• Material Interfaces
• +5 more

Sonia Fernández-Méndez is a Professor (Catedrática de Universidad) at Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Civil and Environmental Engineering (DECA) and the Escola Tècnica Superior d'Enginyeria de Camins, Canals i Ports de Barcelona (ETSECCPB) . She also holds a lecturer position at the Facultat de Matemàtiques i Estadística (FME). Her research focuses on computational modeling in solid mechanics, material interfaces, electroactive materials, 4th-order PDEs, and advanced discretization methods like HDG and X-FEM. She leads the Laboratori de Càlcul Numèric (LaCàN) , a top research group in applied numerical methods. Education: Licenciatura en Matemàtiques (UPC, 1996), PhD in Applied Mathematics (UPC, 2001) under Antonio Huerta. Her awards include the Jacques Louis Lions Award 2010 and Juan Carlos Simó Award 2009. She has supervised 7 PhD students, including Rubén Sevilla and Alba Muixí. Her work bridges high-order methods with industrial applications in energy and materials science. Key contributions: Pioneered hybridizable discontinuous Galerkin (HDG) methods for fracture mechanics and flexoelectric metamaterials. Published extensively in Computer Methods in Applied Mechanics and Engineering , Journal of Scientific Computing , and International Journal for Numerical Methods in Engineering . Active in EU-funded projects on computational mechanics and energy materials.

Onkar SahniView profile

Professor

• Fluid Mechanics
• Computational Fluid Dynamics
• Turbulence Simulations
• +2 more

Onkar Sahni is a Professor in the Department of Mechanical, Aerospace, and Nuclear Engineering at Rensselaer Polytechnic Institute (RPI). He is affiliated with the Center for Computational Innovations (CCI) and the Scientific Computation Research Center (SCOREC). His research focuses on Fluid Mechanics, Computational Fluid Dynamics (CFD), Turbulence Simulations, and High-Performance Computing. His work spans plasma-material interactions in fusion energy systems, shock processing in aerodynamics, and anisotropic mesh adaptation for complex geometries. Key research areas include development of GPU-accelerated particle-in-cell (PIC) codes (e.g., hPIC2), global impurity transport modeling (GITRm), and robust shock detection algorithms. Sahni’s contributions advance computational tools for fusion reactors, hypersonic flows, and unsteady aerodynamics. His methodologies emphasize parallel computing, unstructured mesh techniques, and machine learning integration. His publications from 2022–2025 reflect a focus on fusion energy system simulations, hypersonic flow adaptation, and plasma dynamics. Recent work addresses tokamak impurity transport, meshing challenges in multi-scale PIC simulations, and AI-driven shock processing. Sahni collaborates on exascale computing projects and integrates open-source frameworks like SCOREC’s PUMI library. Awards and grants are not explicitly listed in the provided materials. His advising and grants involve computational infrastructure development for industrial and academic partners. Sahni’s lab specializes in high-performance CFD workflows, with applications in aerospace engineering, renewable energy systems, and plasma physics.

William HenshawView profile

Professor

• Numerical Methods for Partial Differential Equations
• Overlapping Grids
• Adaptive Mesh Refinement
• +5 more

William Henshaw serves as the Margaret A. Darrin Distinguished Professor in Applied Mathematics at Rensselaer Polytechnic Institute, affiliated with the Department of Mathematical Sciences. He maintains active roles in the Center for Modeling, Optimization and Computational Analysis (MOCA), Center for Computational Innovations (CCI), and Scientific Computation Research Center (SCOREC). His academic credentials include: Ph.D. in Applied Mathematics, California Institute of Technology, 1985 B.Math (Hons) in Applied Math and Computer Science, University of Waterloo, 1985 Dr. Henshaw's research centers on numerical methods for partial differential equations , with pioneering contributions to overlapping grid techniques . His work encompasses adaptive mesh refinement , multigrid algorithms , and high-order accurate methods for fluid dynamics, solid mechanics, and electromagnetics. He has developed robust boundary conditions for conjugate heat transfer and fluid-structure interactions, and is the primary architect of the Overture framework —an object-oriented platform for solving PDEs on complex geometries. Analysis of his 2021-2025 publications reveals sustained innovation in high-order numerical schemes, particularly for wave propagation, Maxwell's equations in nonlinear media, and multi-physics problems. His work consistently addresses stability challenges at material interfaces and develops compatibility conditions for overset grids, demonstrating cross-disciplinary impact from electromagnetics to marine engineering. Dr. Henshaw leads the Overture project, which provides computational infrastructure for multi-domain simulations. His framework enables efficient solutions for problems requiring moving grids, adaptive refinement, and coupled physics, serving as a foundation for both academic research and industrial applications in computational science.

Guillaume CaumonView profile

Professor

• Numerical Geology
• Geomodeling
• Uncertainty Quantification
• +5 more

Guillaume Caumon is a Full Professor in Numerical Geology at the Nancy School of Geology and GeoRessources, Université de Lorraine. He leads the RING Consortium (since 2007) and holds a senior innovation chair at Institut Universitaire de France (2023–2028). His expertise includes numerical methodologies for integrating geological and physical observations, with a focus on uncertainty quantification, geomodeling, geostatistics, and meshing. He has held roles such as postdoctoral scholar at Stanford University (2003–2004) and visiting professor at the Colorado School of Mines (2015). His education includes a MSc and PhD from Nancy School of Geology/INPL, with a Habilitation in Geosciences addressing uncertainty quantification in geomodeling. Research interests span numerical geology, geomodeling workflows, and subsurface uncertainty analysis. His work emphasizes stochastic methods, structural restoration, and applications in reservoir geology. Recent publications focus on 3D metamorphic modeling, fault network stochastic interpretation, and transdimensional inversion. He directs the RING Consortium, fostering collaborative earth modeling initiatives. Key awards include the senior innovation chair (IUF). Teaching involves geomodeling, geostatistics, and C++ programming. His research bridges geology and computational methods, addressing challenges in subsurface characterization and uncertainty management.

• Seismic Wave Propagation
• Homogenization
• Seismic Noise Correlations
• +2 more

Paul Cupillard is an Associate Professor at the University of Lorraine's GeoRessources laboratory. His research focuses on computational geophysics, particularly seismic wave propagation, homogenization techniques, and inverse problems in complex geological media. Research Focus: Dr. Cupillard develops advanced numerical methods to simulate seismic phenomena and interpret geophysical data. His work bridges theoretical seismology with practical applications in subsurface characterization and hazard assessment, employing techniques like homogenization, time-reversal, and transdimensional inversion. Publication Trends: Recent articles (2022-2024) demonstrate consistent innovation in wave simulation methodologies, with applications ranging from basin seismicity studies to archaeological geophysics. Key advancements include adaptive mesh techniques, uncertainty quantification in inversion, and integrated geological-geophysical workflows. No awards, students, or grant information is documented. He collaborates with the RING research consortium on geophysical computational challenges.

Robert WoodView profile

Senior Lecturer

• Computational fluid dynamics
• Adaptive mesh refinement
• Numerical analysis
• +2 more

Dr. Robert Wood is Senior Lecturer in Mathematics and Statistics at Charles Sturt University's School of Computing, Mathematics and Engineering. His research specializes in computational fluid dynamics and numerical methods, with particular expertise in adaptive mesh refinement techniques and accuracy verification. Research highlights include: Development of 2D adaptive mesh refinement methods Analysis of lid-driven cavity flows Error-driven node placement optimization Spectral radius analysis of matrices His publications demonstrate rigorous validation of numerical methods for fluid dynamics applications, with recent focus on unsteady flow simulations and shallow water equation modeling.

Costanza AricòView profile

Associate Professor

• Numerical Modeling
• Hydraulic Engineering
• Fluid Dynamics
• +5 more

Costanza Aricò serves as an Associate Professor in the Department of Civil, Environmental, Aerospace and Materials Engineering at the University of Palermo. Her academic career began with postdoctoral research at the University of Palermo and CNR Research Institute (2005-2012), leading to her appointment as Researcher in December 2012 before advancing to her current position. She maintains active teaching responsibilities with scheduled office hours, though recently noted she is temporarily visiting Germany and offers teleconferences for student consultations. Her research focuses on advanced numerical modeling techniques for fluid dynamics and hydraulic engineering applications. Key areas include finite volume/element methods for convection-dominant transport, shallow water modeling for flood forecasting, pollutant transport in aquifers, and innovative turbine design for renewable energy recovery. Her work bridges theoretical numerical methods with practical engineering solutions for water systems and energy harvesting. Analysis of her recent publications (2021-2025) reveals a strong emphasis on hydrokinetic energy systems, particularly optimization of Darrieus, Savonius, and Banki turbines for low-head applications in water networks. Parallel research explores non-isothermal flows, fluid-porous medium interactions, and computational methods for complex geometries. Her numerical approaches frequently address real-world challenges in wastewater energy recovery and flood prediction. Her scientific contributions demonstrate consistent productivity with publications spanning prestigious journals and conference proceedings. Key themes include: Numerical solver development for incompressible flows Hydrokinetic turbine performance optimization Energy recovery in water distribution systems Advanced flood forecasting methodologies Dr. Aricò maintains active research collaborations, evidenced by her current visiting position in Germany. Her work integrates computational modeling with experimental validation, particularly in turbine hydrodynamics and flow phenomena in complex systems. She advises students through thesis supervision and contributes to academic governance within her department.

Matthias K. GobbertView profile

Professor

• Scientific Computing
• Parallel Algorithms
• Machine Learning
• +5 more

Matthias K. Gobbert is a Professor of Mathematics at the University of Maryland, Baltimore County (UMBC) , affiliated with the Department of Mathematics and Statistics . His research focuses on scientific computing , parallel algorithms , machine learning , and numerical methods for applications in medicine, engineering, and biology. He has received the University System of Maryland Board of Regents' Faculty Award for Excellence in Mentoring in 2010. Education : Ph.D. in Mathematics, Arizona State University (1996) Postdoctoral Associate at the University of Minnesota’s Institute for Mathematics and its Applications (1996–1997) Research Interests : Parallel computing for large-scale simulations Multiscale modeling of chemically reactive flows Numerical methods for partial differential equations Applications in medical physics (e.g., proton therapy, prompt gamma imaging) Machine learning for big data in science and engineering Teaching : Develops courses using flipped classrooms, active learning, and technology integration (e.g., tablet laptops, Blackboard) Teaches Math 441 (Numerical Analysis) , Math 447/627 (Parallel Computing) , and software-focused courses like Math 426 (MATLAB) . Incorporates entrepreneurship training into curricula, linking computational methods to commercialization opportunities. Grants & Collaborations : Co-director of NSF-funded CyberTraining: Big Data + HPC + Atmospheric Physics . International collaborations with the University of Kassel (Germany) on parallel computing and HPC. Advisor to REU programs in big data analytics and high-performance computing. Labs & Teams : UMBC High Performance Computing Facility (HPCF) Center for Interdisciplinary Research and Consulting (CIRC)

Erwin GeorgeView profile

Senior Lecturer

• Computational Applied Mathematics
• Fluid Dynamics
• Numerical Analysis
• +3 more

Dr. Erwin George is a Senior Lecturer in the School of Computing and Mathematical Sciences at the University of Greenwich. He holds a PhD in Computational Applied Mathematics from Stony Brook University (2003) and has held academic positions at SUNY Stony Brook, the University of Warwick, and the University of Edinburgh. His research focuses on fluid dynamics, numerical analysis, and mathematical biology, with notable contributions to turbulent mixing, inverse problems in biomedical imaging, and computational methods for complex systems. He is a Fellow of the Institute of Mathematics and its Applications (FIMA) and serves on the Mathematics Today Editorial Board. Education includes: B.A. Honours in Mathematics, University of Guelph (Canada) M.A. in Mathematics, Pennsylvania State University (USA) PhD in Computational Applied Mathematics, SUNY Stony Brook (USA) Research interests span fluid dynamics phenomena, mathematical modeling of biological systems, and numerical methods for solving transport and reaction equations. His work bridges theoretical analysis and practical applications in engineering and life sciences. Recent publications emphasize stochastic processes in structural dynamics and biomedical modeling. Awards include Fellowship of the IMA, reflecting his contributions to advancing mathematical sciences. Teaching expertise spans pure/applied mathematics, scientific computing, and statistics at undergraduate and postgraduate levels.

• Computational Mechanics
• Structural Analysis
• Finite Element Methods
• +2 more

Francesco Parrinello is Associate Professor in Structural Engineering at the University of Palermo. His research develops computational methods for fracture mechanics, including hybrid equilibrium elements for cohesive crack propagation and delamination analysis. Teaching responsibilities include Computational Mechanics of Structures and Science of Construction courses for Civil and Energy Engineering students. Recent publications focus on adaptive numerical formulations for complex material failure under dynamic loading. Laboratory work emphasizes experimental validation of computational models.

Xuemin TuView profile

Professor

• Scientific Computing
• Numerical Analysis
• Applied Mathematics
• +3 more

Xuemin Tu is a Professor in the Department of Mathematics at the University of Kansas, serving as the Scheduling Officer. His research focuses on scientific computing, numerical analysis, and applied mathematics, emphasizing domain decomposition methods, BDDC algorithms, and data assimilation. He is affiliated with the College of Liberal Arts & Sciences and actively contributes to interdisciplinary computational science. Research interests include iterative methods for partial differential equations, numerical solutions of fluid dynamics problems, and the development of efficient algorithms for large-scale simulations. His work bridges theoretical analysis and practical applications in engineering and geophysics. Key trends in his recent articles (2018–2025) highlight advancements in BDDC algorithms for fluid and porous media systems, implicit sampling techniques for data assimilation, and machine learning applications in image recognition. He also explores stochastic models for parameter estimation and uncertainty quantification. No scientific awards or grants are explicitly listed in the provided text. Advising roles or student mentorship details are not included, though his research areas suggest contributions to graduate training in computational mathematics. Labs or collaborative teams are not detailed here, but his affiliations imply involvement with computational science initiatives at the University of Kansas.

Reza AbediView profile

Associate Professor

• Applied Mathematics
• Computational Mechanics
• Solid Mechanics
• +4 more

Reza Abedi is an Associate Professor in the Department of Mechanical, Aerospace and Biomedical Engineering at the University of Tennessee, Space Institute (UTSI), part of the Tickle College of Engineering. He holds a Ph.D. in Theoretical and Applied Mechanics from the University of Illinois at Urbana-Champaign (2010) and has expertise in computational mechanics, fracture mechanics, and discontinuous Galerkin methods. Education: B.S. Civil Engineering, Sharif University of Technology, 1999 M.S. Structural Engineering, Sharif University of Technology, 2001 M.S. Mathematics, University of Illinois at Urbana-Champaign, 2006 Ph.D. Theoretical and Applied Mechanics, University of Illinois at Urbana-Champaign, 2010 Research Interests: Focus on spacetime discontinuous Galerkin methods, fracture modeling, computational electromagnetics, and structural health monitoring. His work emphasizes adaptive meshing, multiscale modeling, and dynamic contact/fracture processes. Publications: Over 30 peer-reviewed articles, including studies on radiative transfer, metamaterials, and fracture mechanics. Recent work explores dynamic homogenization and machine learning in fragmentation analysis. Awards: Henry J. Langhaar Award (2006) Phi Kappa Phi Honor Society Membership (2004–present) Firdawsi Science Fellowship (2004) Labs/Teams: Leads research in SDGFEM (spacetime discontinuous Galerkin finite element methods) and collaborates on projects involving computational fracture mechanics and energy storage materials.