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Gerald KniziaView profile

Associate Professor

• Quantum Chemistry
• Theoretical Chemistry
• Computational Chemistry
• +5 more

Gerald Knizia is an Associate Professor of Chemistry at the Department of Chemistry, Pennsylvania State University. His research focuses on developing computational methods and software tools for quantum chemistry, emphasizing electronic structure theory, orbital localization, and quantum embedding techniques. He leads the Knizia Group, which explores topics such as reaction mechanisms, transition metal catalysis, and high-performance computational methods. Education: Dipl.-Phys., Technische Universität Dresden (2006) Dr.rer.nat. in Chemistry, Universität Stuttgart (2010) Research Interests: Dr. Knizia specializes in bridging first-principles quantum mechanics with chemical concepts, particularly through intrinsic bond orbital (IBO) theory. His work includes advancing density matrix embedding theory (DMET), developing efficient algorithms for molecular integrals, and creating software like IboView to visualize molecular orbitals. His methods aim to elucidate reaction mechanisms, catalytic processes, and electronic structure phenomena. Advising & Grants: Advises graduate students such as Mieke Peels (semi-empirical methods) and Alyssa Bienvenu (machine learning for reactivity). Recipient of the NSF CAREER Award for quantum embedding in heterogeneous catalysis. Labs/Teams: The Knizia Group at PSU integrates theoretical, computational, and software development approaches to address challenges in computational chemistry, with a focus on practical applications for real-world chemical systems.

• Inverse Problems
• Computational Algorithms
• Medical Imaging
• +3 more

Rosemary Renaut is a Professor and Associate Director for Faculty Development at the School of Mathematical and Statistical Sciences, Arizona State University. Her research focuses on computational algorithms for solving inverse problems in medical and geophysical applications, emphasizing regularization techniques and numerical linear algebra. She holds a Ph.D. in Applied Mathematics from the University of Cambridge (1985). Education: Ph.D. in Applied Mathematics, University of Cambridge, UK (1985) Research Interests: Dr. Renaut specializes in developing algorithms for solving ill-posed inverse problems, particularly in medical imaging (e.g., PET, MRI) and geophysics (e.g., gravity/magnetic data inversion). Her work addresses challenges like data noise, model ill-posedness, and parameter regularization. She has contributed to methods like total variation regularization, χ²-based parameter estimation, and iterative solvers (e.g., LSQR). Articles Trends: Recent publications (2020–2025) emphasize large-scale inversion techniques, sparsity-driven reconstruction, and efficient regularization parameter estimation. Key applications include geophysical exploration (e.g., iron oxide deposits, kimberlites) and medical imaging (e.g., EEG source localization). Her work often combines numerical methods with domain-specific challenges, such as joint inversion of multimodal geophysical data. Awards: SIAM Fellow (2022) Technische Universitaet Muenchen Ambassador (2013) J.T. Knight Prize in Mathematics (1982) Grants & Service: Dr. Renaut has led numerous grants (e.g., NSF, DOD) on inverse problems and computational methods. She serves on editorial boards (BIT, SIAM News) and has held roles like Program Director at the NSF. Her service also includes mentoring junior faculty and advancing computational biosciences education. Labs/Teams: Collaborates with interdisciplinary teams in geophysics (e.g., gravity/magnetic data inversion) and biomedical imaging (e.g., PET parametric imaging). Active in conferences like International Conference on Spectral and High Order Methods.

Yuan LiuView profile

Associate Professor

• Numerical Analysis
• Scientific Computing
• Computational Biology
• +7 more

Dr. Yuan Liu is an Associate Professor of Applied Mathematics at Wichita State University, affiliated with the Fairmount College of Liberal Arts and Sciences and the Department of Mathematics, Statistics, and Physics. She holds a PhD in Applied and Computational Mathematics and Statistics from the University of Notre Dame (2012), and M.Sc. and B.Sc. degrees in Mathematics from the University of Science and Technology of China (2007 and 2004). Her research focuses on designing and analyzing high-order numerical methods for solving partial differential equations (PDEs), including discontinuous Galerkin (DG), WENO, and spectral methods. Applications span computational biology (e.g., zebrafish dorsal-ventral patterning, tumor growth modeling) and computational engineering (e.g., fluid-structure interaction, phase-field modeling in additive manufacturing). Dr. Liu has held academic positions at Wichita State University (2019–present), Mississippi State University (2015–2019), and Michigan State University (2012–2015 as Visiting Assistant Professor). Her publications reflect expertise in adaptive multiresolution methods, bound-preserving schemes, and mathematical modeling of biological systems. Recent work emphasizes numerical methods for dispersive waves, nonlinear Schrödinger equations, and tumor growth dynamics. Dr. Liu’s research has been supported by grants focusing on computational mathematics and interdisciplinary applications. She actively contributes to teaching in applied mathematics and supervises graduate students in numerical analysis and computational biology.

• Computer Architecture
• Compilers
• Programming Languages
• +4 more

Andreas Krall is an Associate Professor in the Department of Software Technology at TU Wien's Faculty of Informatics. He holds roles as Curriculum Coordinator for the Bachelor Informatics and Bachelor Software and Information Engineering programs, and serves as a Substitute Member of the Curriculum Commission for Informatics. His research focuses on compiler design, architecture description languages (e.g., VADL), and formal methods for ensuring correctness in compiler-processor co-design. Key research areas include computer architecture, compiler verification, embedded systems, and the development of tools for processor simulation and optimization. Krall has led projects such as 'Correct Compilers for Correct Processors' (2010–2015) and contributed to the CACAO JVM project. He has published extensively on topics like instruction selection, abstract state machines (CASM), and SSA-based optimizations. Notable awards include the Heinz Zemanek Preis (1987). His work spans over 30 years, with contributions to both academic research and industry-relevant tools like the VADL architecture description framework. Krall has advised numerous students in topics ranging from compiler backends to garbage collection algorithms.

• Fluid Dynamics
• Computational Fluid Dynamics
• Turbulence
• +3 more

Jørgen Røysland Aarnes is a Research Fellow at the Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU). His research spans fluid dynamics, computational methods, and philosophy of science, with particular focus on turbulence, multiphase flows, and structural realism in Ernst Cassirer's philosophy. He completed his PhD in 2018 with a thesis on particle-laden flows impinging on cylinders. Research Interests: Computational fluid dynamics (CFD), including high-order methods and overset grids Turbulent flows and free-surface vortex structures Particle-laden flow dynamics and impaction mechanisms Philosophy of science, structural realism, and symbolic forms Recent Contributions: His 2025 work on free-surface vortex patterns and 2024 philosophical papers on Cassirer's symbolic forms demonstrate interdisciplinary expertise. He has developed computational tools like the Pencil Code for fluid simulations. Awards: No scientific awards explicitly listed, though his work has been presented at major conferences including the European Turbulence Conference and European Geosciences Union meetings. Advising & Grants: No explicit grants or advisees listed, though his PhD supervision experience is implied through his postdoctoral role. Labs/Teams: Collaborates with the Pencil Code Collaboration and NTNU's energy engineering research groups.

• Computational Electromagnetics
• Finite Element Methods
• Inverse Problems
• +3 more

Dr Paul Ledger is an Honorary Research Fellow at Swansea University's Faculty of Science and Engineering and a Professor of Computational Mathematics at Keele University. His expertise spans computational electromagnetics, finite element methods, and inverse problems. He specializes in high-order finite element simulations for Maxwell's equations, preconditioning techniques, and coupled magneto-mechanical systems in MRI scanner design. Research interests include hp-finite element software development, error estimation, reduced order modeling, and applications in biomedical imaging and metal detection. He has supervised PhD research on MRI scanner vibrations and contributed to industrial projects on electromagnetic field modeling. His work bridges applied mathematics, engineering, and data science, with recent focus on probabilistic methods for inverse problems. Publications emphasize magnetic polarizability tensors, eddy current simulations, and MRI system optimization. He maintains active collaborations in multi-physics computational modeling and has presented widely at international conferences such as ICOSAHOM and MAFELAP. Teaching roles include modules in problem-solving, data science statistics, and computational engineering methods.

Ralf ZimmermannView profile

Associate Professor

• Numerical Linear Algebra
• Matrix Analysis
• Scientific Computing
• +4 more

Ralf Zimmermann is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark. His research focuses on Numerical Linear Algebra, Matrix Analysis, and Scientific Computing, with a particular emphasis on Reduced Order Modelling and Manifold Geometry. He has contributed significantly to the development of efficient algorithms for dynamical systems and geometric optimization on matrix manifolds such as the Stiefel and Grassmann manifolds. His work bridges computational mathematics and engineering applications, including aerodynamics and model reduction techniques. Key research interests include the theoretical foundations of manifold geometry, algorithm design for high-dimensional data, and the application of these methods to real-world problems. He has published extensively on topics like Riemannian metrics, injectivity radii, and curvature analysis. His recent work explores adaptive probabilistic reduced-order models and gradient-enhanced Kriging methods for high-dimensional systems. Recipient of the Best Paper Award (2021) and DFG Scholarship (2014) Organized workshops such as the Nordic Numerical Linear Algebra Meeting (2024) Contributed to projects like Optimal Structure-Preserving Model Reduction and the Danish Data Science Academy His teaching includes courses on numerical analysis, computational physics, and differential equations. Zimmermann actively collaborates internationally, contributing to conferences and peer review.

Hakan I. TarmanView profile

Professor

• Computational Mechanics
• Hydrodynamic Stability
• Spectral Methods
• +3 more

Dr. Hakan I. Tarman is a Professor at the Mechanical Engineering Department of Middle East Technical University (METU) , Ankara, Turkey. He is also affiliated with the Department of Engineering Sciences and interdisciplinary graduate programs in Biomedical Engineering (Biomechanics Group member since 2006, Program Coordinator 2015-2017) and Earth System Sciences. B.Sc. in Mechanical Engineering and Mathematics (Boğaziçi University, 1982) M.Sc. in Mechanical Engineering (Boğaziçi University, 1984) Ph.D. in Applied Mathematics (Brown University, 1989) His research focuses on Computational Mechanics , Hydrodynamic Stability , and Spectral Methods , including applications to thermal convection under magnetic fields, fluid-structure interactions, and biomedical flows. Recent publications highlight spectral solenoidal-Galerkin methods for convection and pipe flows, drag reduction via phase randomization, and photonic nanojet analysis. Key scientific contributions include projects funded by TÜBİTAK (e.g., 109M435) and METU-BAP (e.g., 2004-03-10-01). He has supervised numerous M.S. and Ph.D. theses on topics like Uncertainty Quantification , Shell Optimization , and Lattice Boltzmann Modeling . Courses taught : Numerical Methods, Spectral Methods, Hydrodynamic Instability, PDE Numerical Methods Labs/teams : Collaborates with Biomechanics Group; integrates spectral methods with biomedical and geophysical applications

XENOPHONTOS CHRISTOSView profile

Professor

• Numerical Analysis
• Numerical Solution of Partial Differential Equations
• Finite Element Methods
• +1 more

Christos Xenophontos is a Professor in the Department of Mathematics and Statistics at the University of Cyprus (UCY), where he has served as an academic since 2004. He currently holds the position of Director of the Teaching and Learning Center (KE.DI.MA.) since 2022. His academic journey includes a B.Sc. (High Honors) in Mathematics from the University of William & Mary (1991), an M.Sc. (1993), and a Ph.D. in Applied Mathematics (1996) from the University of Maryland, Baltimore County. He has held positions at Ohio State University, Clarkson University, Loyola University, and UCY before his promotion to Professor in 2017. His research focuses on Numerical Analysis and Scientific Computing, with a particular emphasis on Finite Element Methods (FEM), singularly perturbed problems, partial differential equations, and computational mechanics. Notable contributions include advancements in hp-FEM for boundary value problems, isogeometric analysis, and numerical solutions to reaction-diffusion systems. His work bridges theoretical analysis and practical computational methods, addressing challenges in multi-scale and high-order systems. Xenophontos’ scholarly output includes over 60 peer-reviewed articles, with recent trends focusing on hp-FEM for singularly perturbed systems, neural network applications in numerical methods, and adaptive algorithms for complex boundary value problems. His methodologies emphasize robustness, exponential convergence, and error estimation in non-standard domains. He actively engages in international collaborations and contributes to academic leadership through his directorship at KE.DI.MA.

Petter E. BjørstadView profile

Professor

• Numerical analysis
• Domain decomposition
• Elliptic PDE
• +4 more

Petter E. Bjørstad is a Professor of Computer Science and Mathematics at the University of Bergen, Norway, where he has served as Head of the Department of Informatics since 2010. Previously, he was Director of the Bergen Center for Computational Science (2000-2010) and Professor in both the Department of Mathematics (2006-2010) and Department of Informatics (1985-2005). He also held a position as Professor of Mathematics at the University of Minnesota during 1996-1997 on unpaid leave from Bergen. Dr. Bjørstad earned his PhD in Computer Science from Stanford University in 1980, following which he completed a postdoctoral fellowship at the Courant Institute of Mathematical Sciences at New York University. Before entering academia, he worked as a Principal Engineer at Det Norske Veritas from 1981-1985. Dr. Bjørstad's research focuses on numerical analysis and high-performance computing, with particular expertise in domain decomposition methods, parallel algorithms for elliptic partial differential equations, and scientific computing. His work bridges theoretical mathematics with practical computational challenges, especially in industrial applications. He has led significant research projects including 'Multiscale Domain Decomposition: Algorithms and Analysis' (2010-2014), funded by the Research Council of Norway and the University of Bergen. His publication record demonstrates consistent contributions to the field of numerical methods and parallel computing over several decades. The research trends show a strong focus on developing efficient algorithms for solving partial differential equations through domain decomposition techniques, with applications spanning structural analysis, reservoir simulation, semiconductor device modeling, and other industrial contexts. His work increasingly emphasizes practical implementation on modern parallel architectures including SIMD and MIMD systems. Large number of research grants from Norway and the European Union Dr. Bjørstad has been actively involved in mentoring students and collaborating with industry through projects like the Europort effort, where industrial codes were ported to parallel computing platforms. His laboratory for parallel computing (Parallab) has been instrumental in advancing practical applications of high-performance computing since its establishment in 1985 with Europe's first 64-processor Intel hypercube. The lab has evolved to include multiple MIMD machines including an Intel Paragon, Parsytec GC/Power-Plus, and DEC α-cluster. As Head of the Department of Informatics, Dr. Bjørstad leads one of Norway's premier computing research units with expertise spanning theoretical computer science, numerical methods, and practical applications in various scientific domains. His leadership has helped establish Bergen as a significant center for computational science in Europe.

Carlo FiorinaView profile

Associate Professor

• Nuclear Reactor Analysis
• Scientific Computing
• Thermal-Hydraulics
• +8 more

Carlo Fiorina is an Associate Professor of Nuclear Engineering at Texas A&M University, affiliated with the Computational and Data Sciences Applied to National Security and Nuclear Engineering groups. His work focuses on high-fidelity numerical methods, advanced reactor design, and open-source software development for nuclear systems. Fiorina holds a Ph.D. in Energy and Nuclear Science from Politecnico di Milano, alongside M.S. and B.S. degrees in Nuclear and Electrical Engineering. Research Interests Fiorina's research spans thermal-hydraulics simulation, neutronics modeling, and uncertainty quantification for fusion and fission systems. He specializes in multiphysics coupling frameworks (e.g., GeN-Foam, MOOSE) and has pioneered dynamic mesh deformation techniques for fuel performance analysis. His work integrates machine learning for reduced-order modeling, accelerating simulations of complex phenomena like sodium boiling in fast reactors. Awards Early Career Reactor Physicist Award (American Nuclear Society, 2023) Most Cited Paper in Progress in Nuclear Energy (2016) Best Technical Paper in Progress in Nuclear Energy (2013) Research Contributions His publications emphasize reactor safety analysis (e.g., PCMI effects, NECTAR benchmarking) and fusion chamber dynamics. Fiorina has led collaborations on open-source tools like GeN-ROM for molten salt reactors and contributed to international initiatives like IAEA's fast reactor thermal-hydraulics studies. His recent work explores AI's role in nuclear engineering, including surrogate modeling for transient simulations. Labs/Teams Fiorina directs the Computational Nuclear Engineering Lab at Texas A&M, focusing on multiphysics software development and validation against experimental data from facilities like the CROCUS reactor.

Tom GustafssonView profile

Lecturer

• Computational Mechanics
• Numerical Analysis
• Finite Element Methods
• +3 more

Tom Gustafsson is a University Lecturer in the Department of Energy and Mechanical Engineering at Aalto University. His research focuses on computational mechanics, numerical analysis, and finite element methods, with a particular emphasis on solving complex problems in solid and fluid mechanics, including obstacle problems, contact mechanics, and elastoplastic torsion. His work integrates advanced numerical techniques such as Nitsche's method, stabilized finite elements, and adaptive mesh refinement to address challenges in engineering applications. Key research areas include finite element methods for nonlinear problems, such as Stokes flow with slip conditions, Bingham fluid dynamics, and Tresca friction modeling. He has contributed to the development of open-source software like scikit-fem and published extensively on topics ranging from model order reduction to error analysis in mortar methods. Prizes: Article of the year award 2013-2014. Projects: Principal investigator of the project 'Adaptive high-order mortar methods for computational contact mechanics' (2021–2024). His research has been featured in media, including a report on 'Mortaring for Linear Elasticity Using Mixed and Stabilized Finite Elements' by Aalto University in 2023. Collaborations span computational mechanics, applied mathematics, and software development.

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +4 more

Dr. Hendrik Kleikamp is a Researcher at the Institute for Analysis and Numerical Analysis within the Department of Mathematics and Computer Science at the University of Münster. His research focuses on numerical analysis, machine learning, and scientific computing, with a particular emphasis on nonlinear model order reduction, optimal control of dynamical systems, and scientific machine learning techniques such as neural networks and kernel methods. He is actively involved in international conferences and workshops, including SIAM CSE and MATHMOD, where he has presented talks on topics like adaptive model hierarchies and certified machine learning approaches for parameterized problems. Kleikamp collaborates with institutions globally and contributes to open-source tools like pyMOR. His work bridges theoretical advancements and practical applications in computational science. His research interests include reducing computational complexity in transport-dominated problems, developing efficient algorithms for optimal control scenarios, and integrating machine learning into model reduction frameworks. Recent contributions involve certified algorithms for parametrized systems and knowledge graph development for applied mathematics models. He maintains an active publication record in journals such as ESAIM: Mathematical Modelling and Numerical Analysis and SIAM Journal on Scientific Computing. Kleikamp’s affiliations include Mathematics Münster and the ON-DEM COST Action, where he has conducted workshops on model order reduction. His technical contributions span software development, conference organization, and collaborative research on interdisciplinary computational challenges. Contact: hendrik.kleikamp@uni-muenster.de, Room 120.007.

Dongbin XiuView profile

Professor

• Stochastic Computations
• Uncertainty Quantification
• Numerical Methods
• +3 more

Professor Dongbin Xiu is a faculty member at The Ohio State University's Department of Mathematics, holding the position of Ohio Eminent Scholar. Previously, he has served at institutions such as the University of Utah and Purdue University. His research focuses on stochastic computations, uncertainty quantification, high-order numerical methods, and data assimilation. Dr. Xiu earned his Ph.D. in Applied Mathematics from Brown University, followed by M.S. and B.S. degrees in Mechanical Engineering and Mechanics. His work is supported by grants from NSF, AFOSR, DOE, and DARPA. He has authored a notable book on numerical methods for stochastic computations and holds several awards, including the NSF CAREER Award and Purdue University's Teaching for Tomorrow Award. His publications emphasize innovative approaches in uncertainty quantification, including stochastic collocation methods, polynomial chaos, and machine learning integration. He serves on editorial boards of journals such as the SIAM Journal on Scientific Computing and the International Journal for Uncertainty Quantification.

• Digital signal processing and simulation
• Dynamical systems
• Nonlinear vibrations
• +9 more

Pol D. Spanos is the Lewis B. Ryon Professor of Mechanical and Civil Engineering and Professor of Materials Science and NanoEngineering at Rice University, USA. He is an elected member of the National Academy of Engineering (USA) and foreign member of a dozen prestigious academies worldwide. Education Diploma in Mechanical Engineering and Engineering Science, National Technical University of Athens, Greece M.S. in Civil Engineering, California Institute of Technology (Caltech), USA Ph.D. in Applied Mechanics (with minors in Applied Mathematics and Business Economics & Management), Caltech, USA Research Interests Professor Spanos’ research integrates digital signal processing , nonlinear and stochastic dynamics , and advanced computational mechanics . His group develops analytical, numerical, and Monte-Carlo methods to study: Nonlinear vibrations of structures and mechanical systems under random loads Seismic risk assessment and spectrum estimation Fatigue and fracture of advanced nanocomposite materials Signal processing of biomedical data (ECG, EEG, bone mechanics) Flow-induced vibrations of offshore platforms, risers and pipelines Directional drilling dynamics and certification of space-station payloads The mathematical frameworks involve deterministic and stochastic differential equations (integer or fractional order), finite-element formulations, wavelet/chirplet transforms, and high-performance computing. Scientific Awards & Honors 2024 Blaise-Pascal Medal in Engineering 2021 ASME Gold Medal (highest ASME award) 2020 T.K. Caughey ASME Medal for Lifetime Contributions to Nonlinear Dynamics 2017 International Scientific and Technological Cooperation Award of the People’s Republic of China Member or Foreign Member of 11 national academies and fellow of multiple professional societies ASCE Theodore von Kármán Medal (2003), Newmark Medal, Freudenthal Medal, Huber Prize, etc. Advising & Funding He has supervised over 75 M.S. theses and 60 Ph.D. dissertations , hosted numerous visiting scholars, and secured funding from NSF, NASA, DOE and a broad range of industrial partners. Professional Service & Outreach Editor-in-Chief, International Journal of Non-Linear Mechanics Editor, Journal of Probabilistic Engineering Mechanics Former Chair, ASCE Engineering Mechanics Division and ASME Applied Mechanics Division ASME Distinguished Lecturer (1997-2003) Registered Professional Engineer (Texas) and Licensed Engineer in Greece Active in diversity-enhancing initiatives and pro-bono engineering consulting