Find Academic Supervisors

Arnold ReuskenView profile

Professor

• Numerical Analysis
• Finite Element Methods
• Multigrid Methods
• +7 more

Prof. Dr. Arnold Reusken is a full Professor of Numerical Mathematics at RWTH Aachen University, affiliated with the Institute for Geometry and Practical Mathematics (IGPM). He has held the Chair for Numerical Mathematics since 1997 and maintains an active research and academic profile in computational mathematics. Education: Ph.D. in Mathematics, University of Utrecht (1988) M.Sc. in Mathematics, University of Utrecht (1984) His research focuses on the development and analysis of numerical methods for partial differential equations, with particular emphasis on finite element methods, multigrid solvers, and computational techniques for two-phase incompressible flows and PDEs on surfaces. His work bridges theoretical numerical analysis and practical scientific computing applications in fluid dynamics and interfacial phenomena. He has made significant contributions to trace finite element methods, surface Stokes equations, and unfitted discretizations. The recent publication trend shows sustained activity in numerical methods for evolving surfaces, surface fluid dynamics, and preconditioning techniques. His work often involves rigorous error and stability analysis, demonstrating a strong theoretical foundation. Editorial Roles: Associate Editor, Journal of Numerical Mathematics (2015–present) Associate Editor, IMA Journal of Numerical Analysis (2020–present) Former Associate Editor, SIAM Journal on Numerical Analysis (2016–2021) Former Associate Editor, SIAM Journal on Scientific Computing (2002–2008) Former Associate Editor, Computing & Visualization in Science (2010–2021) Member of Advisory Board, Computing (1997–2009) Prof. Reusken has advised numerous students and researchers, though specific names are not listed in the provided text. He has been involved in collaborative research projects and has secured funding for work in numerical simulation and computational fluid dynamics. He co-authored the influential textbook Numerik für Ingenieure und Naturwissenschaftler , now in its third edition, and has contributed to other key publications in the field. He leads a research group at IGPM focused on numerical methods for interface and surface problems, contributing to both fundamental algorithm development and practical implementation in scientific computing. His team works on cutting-edge methods for simulating complex fluid systems with moving boundaries and topological changes.

Constantin BacutaView profile

Professor

• Numerical Methods for PDEs
• Finite Element Methods
• Singularly Perturbed Boundary Value Problems
• +6 more

Constantin Bacuta is a Professor in the Department of Mathematical Sciences at the University of Delaware, College of Arts & Sciences. He holds a Ph.D. in Mathematics from Texas A&M University (2000), an M.S. from Alexandru Ioan Cuza University (Romania), and has held postdoctoral positions at Pennsylvania State University and Lawrence Livermore National Laboratory. His research focuses on numerical methods for PDEs, finite element methods, singularly perturbed problems, iterative methods, and subspace interpolation techniques. His expertise spans advanced numerical discretization techniques, including finite element and finite difference methods for convection-diffusion problems, multigrid methods, and saddle point formulations. He has published extensively in computational mathematics, with over 50 peer-reviewed articles in journals like Computer Methods in Applied Mathematics , SIAM Journal on Numerical Analysis , and Journal of Computational and Applied Mathematics . Bacuta teaches graduate and undergraduate courses in numerical analysis, functional analysis, and computational mathematics, including MATH 817 (Numerical Approximation of PDEs), MATH 611 (Introduction to Numerical Discretization), and MATH 838 (Finite Elements and Boundary Element Methods). His work emphasizes rigorous mathematical foundations alongside computational implementation, with contributions to preconditioning strategies and error analysis in non-conforming discretizations.

Yunhui HeView profile

Assistant Professor

• Numerical Analysis
• Scientific Computing
• Finite Element Methods
• +5 more

Yunhui He is an Assistant Professor in the Department of Mathematics at the University of Houston. His research focuses on numerical analysis and scientific computing, with expertise in finite element methods, multigrid methods, and local Fourier analysis. He has held postdoctoral positions at institutions like the University of British Columbia and the University of Waterloo. His work includes contributions to preconditioning techniques, acceleration methods, and the numerical solution of partial differential equations. Education: PhD in Mathematics (2018), Memorial University of Newfoundland MSc in Computational Mathematics (2015), Chinese Academy of Sciences BSc in Mathematics and Applied Mathematics (2012), Capital Normal University Research Interests: Dr. He’s research emphasizes numerical methods for PDEs, multigrid algorithms, and iterative solvers. He explores topics like finite element methods, preconditioning strategies, and local Fourier analysis to enhance computational efficiency in fluid dynamics and optimal control problems. His work bridges theoretical analysis and practical implementation, with applications in engineering and physics. Articles Trends: Recent publications highlight advancements in multigrid relaxation schemes, Anderson acceleration for nonlinear PDEs, and preconditioners for coupled flow systems. His work often integrates local Fourier analysis to optimize solver performance, with applications to Stokes-Darcy equations and optimal control problems. Grants & Awards: He co-organized the 2025 NSF-funded CBMS Conference on Applied Mathematics and Machine Learning (DMS-2430460), demonstrating leadership in academic collaboration. Teaching: Taught courses including Linear Algebra, Partial Differential Equations, and Numerical Analysis at the University of Houston and other institutions. His pedagogical focus aligns with computational mathematics and scientific computing. Lab/Team: Hosts Santolo Leveque as a postdoctoral fellow (2024–present), advancing collaborative research in numerical methods and multigrid theory.

Tony F. ChanView profile

Professor

• Mathematical Image Processing
• Computer Vision
• Computational Brain Mapping
• +14 more

Tony F. Chan is currently President and Professor of Mathematics and Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He holds the title of Professor Emeritus in the Department of Mathematics at the University of California, Los Angeles (UCLA), where he previously served as Professor with joint appointments in Computer Science and Bioengineering. He was Dean of the Division of Physical Sciences at UCLA (2001–2006) and Assistant Director at the National Science Foundation (NSF) for Mathematics and Physical Sciences (2006–2009). President, HKUST Professor, Mathematics & Computer Science and Engineering, HKUST Professor Emeritus, Mathematics, UCLA Assistant Director, NSF (2006–2009) Dean, Division of Physical Sciences, UCLA (2001–2006) His research interests are centered around mathematical image processing, computer vision, computational brain mapping, and numerical algorithms. He has made seminal contributions to variational methods, total variation regularization, level set methods, and multiscale computational techniques. His work bridges pure mathematics with applications in biomedical imaging, VLSI design, and scientific computing. His recent publications focus on image segmentation, inpainting, brain surface mapping, and nonlocal filtering. These works demonstrate a strong trend toward geometric and variational models for image analysis, with increasing emphasis on medical and biological applications such as neuron tracking and cortical mapping. One of the most cited mathematicians (ISI Highly Cited) Chan has mentored over 25 PhD students and 15 postdoctoral fellows, contributing significantly to the training of next-generation researchers in applied mathematics and computational science. He has led major research initiatives including the Institute for Pure & Applied Mathematics (IPAM) and has been involved in numerous professional services at national and international levels. His work has been supported by major funding agencies including the NSF. He leads the Image Processing Group at UCLA and has been instrumental in advancing interdisciplinary research at the intersection of mathematics, engineering, and neuroscience.

• Optimization Algorithms
• Network Flows
• Large Sparse Systems
• +5 more

Donald Goldfarb is the Alexander and Hermine Avanessians Professor in the Department of Industrial Engineering and Operations Research at Columbia University. His research spans optimization algorithms, network flows, and large-scale computational methods, with applications in finance, image processing, and machine learning. Research Focus: Goldfarb specializes in developing efficient algorithms for convex and nonconvex optimization problems, including linear, quadratic, semidefinite, and second-order cone programming. His work extends to robust optimization for financial modeling and innovative methods for image restoration using total variation techniques. Publication Trends: His recent research emphasizes optimization in computational imaging (e.g., image decomposition and restoration) and finance (e.g., robust portfolio management). Algorithmic contributions include interior-point methods, Bregman iterations, and large-scale convex optimization techniques.

Alexander PoppView profile

Professor

• Computational Contact Dynamics
• Computational Solid and Structural Dynamics
• Fluid-Structure Interaction
• +7 more

Univ.-Prof. Dr.-Ing. Alexander Popp is a Full Professor of Computer-Based Simulation at the University of the Bundeswehr Munich, Germany, where he also serves as Vice Dean of the Department of Civil Engineering and Environmental Sciences. Additionally, he is the Vice Director of the DLR Institute for the Protection of Terrestrial Infrastructures in Sankt Augustin, Germany. His academic career spans prestigious institutions including TU Munich, Columbia University, and The University of Tokyo, demonstrating his international recognition in computational mechanics. Professor Popp's research spans the entire field of computational mechanics and numerical analysis, with a particular focus on computational contact dynamics, solid and structural dynamics, and multiphysics problems. His work encompasses fluid-structure interaction, non-conforming discretization methods, tribology, and computational plasticity. More recently, he has expanded his research into digital twins for critical infrastructure protection and biomedical applications, particularly in cardiovascular mechanics and stent graft modeling for endovascular repair. His group develops advanced finite element formulations for beams and shells, isogeometric analysis, and integrates machine learning techniques with traditional computational methods. Professor Popp has received numerous prestigious awards including the O.C. Zienkiewicz Award for Young Scientists from ECCOMAS (2018), the Top Teaching Trophy of the Munich School of Engineering (2018), and the ZD.B Junior Research Group Award (2017). His research is supported by significant funding from the Deutsche Forschungsgemeinschaft (DFG), the German Federal Ministry of Education and Research, and industry partners. He leads several major research projects including "AutoStent - An autonomous design assistant for aneurysm repair" and "Combination of data- and physics-based methods for hybrid digital twins" through the dtec.bw research center. As a leader in the computational mechanics community, Professor Popp serves on multiple editorial boards including Scientific Reports (Springer Nature) and Advanced Modeling and Simulation in Engineering Sciences. He is an Associate Editor for the Journal of Theoretical, Computational and Applied Mechanics and will serve as Co-Chairman for the 17th World Congress on Computational Mechanics (WCCM) and ECCOMAS Congress in 2026. His laboratory, the Institute for Mechanics of Components and Systems (IMCS), focuses on bridging fundamental research with real-world applications in civil engineering, infrastructure protection, and biomedical engineering.

Michael InnerbergerView profile

Researcher

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +3 more

Michael Innerberger is a researcher at TU Wien's Faculty of Mathematics and Geoinformation , affiliated with the Research Group Numerics of PDEs . He holds a Dr.techn. (PhD) and Dipl.-Ing. in engineering. His work focuses on adaptive finite element methods (FEM) , numerical analysis , and multigrid solvers for partial differential equations (PDEs), with an emphasis on achieving optimal computational complexity and robust error estimation. Recent contributions include the development of hp-robust multigrid solvers and the MooAFEM MATLAB framework for higher-order adaptive FEM. His research addresses both symmetric and nonsymmetric PDEs, including semilinear and nonlinear cases. Education: Dipl.-Ing. (TU Wien), Dr.techn. (2022, TU Wien) Key Areas: Adaptive mesh refinement, goal-oriented error control, iterative linearization, and numerical software development. Publications highlight advancements in rate-optimal and cost-optimal adaptive algorithms for elliptic PDEs, demonstrating rigorous complexity analysis and practical implementation strategies. Collaborations include Dirk Praetorius, Roland Becker, and Jens Markus Melenk. Labs/Teams: Core member of the Numerics of PDEs research group, contributing to theoretical and computational advancements in scientific computing.

Bedřich SousedíkView profile

Associate Professor

• Applied and Computational Mathematics
• Numerical Analysis
• Scientific Computing
• +5 more

Bedřich Sousedík is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). He joined UMBC in 2014, transitioning from roles as a Research Associate at the University of Southern California and University of Maryland, College Park. His academic journey includes a Ph.D. in Applied Mathematics (2010, University of Colorado Denver) and a Ph.D. in Civil Engineering (2008, Czech Technical University in Prague). Education: Ph.D. in Applied Mathematics, University of Colorado Denver (2010) Ph.D. in Civil Engineering, Czech Technical University (2008) M.Eng. in Mechanical Engineering, Czech Technical University (2001) Research Interests: Focuses on applied and computational mathematics, including numerical analysis, scientific computing, uncertainty quantification, stochastic finite element methods, and domain decomposition techniques. His work emphasizes scalable algorithms for complex systems with parametric uncertainty. Professional Contributions: Author of ~43 refereed publications (24 journals, 12 conferences) with an h-index of 15 (Google Scholar) Principal Investigator on NSF grants totaling $219,999 for multilevel methods research Recipient of prestigious awards including the 2010 Prof. Babuška Prize and 2020 Early Career Excellence Award Advising & Teaching: Supervised 8 Ph.D. and Master’s students since 2014 Mentored undergraduate research projects in computational fluid dynamics and epidemiological modeling

• Numerical Analysis
• Partial Differential Equations
• Computational Mathematics
• +3 more

Prof. Dr. Derviş Subası is a faculty member in the Department of Mathematics at Eastern Mediterranean University's Faculty of Arts and Sciences. His research focuses on numerical analysis, partial differential equations, and computational mathematics, with significant contributions to inverse problems and domain decomposition methods. He has supervised multiple master's and PhD students and is actively involved in academic research. Education: PhD and MS in Mathematics from Eastern Mediterranean University (1997, 1992), BS in Electrical and Electronics Engineering (1989). His research interests include numerical solutions of partial differential equations, preconditioning techniques, and iterative methods for linear systems. He has contributed to advancements in fractional calculus-based algorithms, block-grid methods for singular boundary conditions, and parallel computing for parabolic problems. Prof. Subası has co-supervised ongoing PhD research and mentored numerous master's students, with theses focusing on parabolic inverse problems, diffusion coefficient determination, and finite difference schemes. His work often intersects applied mathematics and computational modeling. Contact details: Office AS140, Phone +90 392 630 1138, Email dervis.subasi@emu.edu.tr .

Gerhard StarkeView profile

Professor

• Numerical Analysis
• Finite Element Methods
• Computational Mechanics
• +7 more

Gerhard Starke is a Professor of Numerical Mathematics at the Faculty of Mathematics, University of Duisburg-Essen. He leads the Numerical Mathematics workgroup, focusing on the development and analysis of finite element methods for solving complex mathematical and mechanical problems. His research spans multiple areas of computational mathematics with applications in solid mechanics and optimization. Professor Starke's research interests center around numerical analysis of finite element methods , reconstruction-based a posteriori error estimators , and mixed finite element methods . His work has significant applications in shape optimization , solid mechanics , hyperelasticity , plasticity , and frictional contact problems . His research combines theoretical mathematical analysis with practical computational approaches to solve challenging engineering problems. Analysis of Professor Starke's recent publications (2017-2025) reveals a strong focus on stress-based methods, shape optimization, and advanced finite element formulations. His work demonstrates a consistent progression from fundamental numerical analysis to increasingly complex applications in solid mechanics. Key trends include the development of constrained first-order system least mean approximation for shape optimization, superconvergent DPG approximations in linear elasticity, and stress-based methods for variational inequalities in contact mechanics. Professor Starke has secured significant third-party funding for his research, including current projects funded by the German Research Foundation (DFG) within Priority Programs 1748 and 1962. These projects focus on stress approximation in hyperelastic material models and stress-based methods for variational inequalities in solid mechanics. His research collaborations span multiple institutions including Humboldt University Berlin, University of Duisburg-Essen's Institute of Mechanics, and international partners in Switzerland. Head of Numerical Mathematics workgroup at University of Duisburg-Essen Principal investigator for multiple DFG-funded projects since 1999 Supervisor for numerous doctoral students including Laura Hetzel, Henrik Schneider, and Kemal Suntay Extensive international collaborations with researchers across Europe The Numerical Mathematics workgroup led by Professor Starke maintains active collaborations with mechanics researchers at the University of Duisburg-Essen and international institutions. The group has participated in significant research initiatives including the DFG Research Training Group 615 and multiple Priority Programs. Current work focuses on applying advanced numerical methods to challenging problems in computational solid mechanics and shape optimization.

Ming ZhouView profile

Senior Lecturer

• Numerical Linear Algebra
• Eigenvalue Problems
• Adaptive Finite Elements

Ming Zhou is a Senior Lecturer at the Institute of Mathematics, University of Rostock, Germany. His research focuses on numerical linear algebra, eigenvalue problems, and adaptive finite elements. He is known for contributions to convergence theory of preconditioned eigensolvers and iterative methods. Zhou co-developed the AMP Eigensolver, a software tool for solving elliptic partial differential operator eigenvalue problems in 2D domains. His work emphasizes robust bounds for Ritz values, block preconditioned gradient methods, and restarted Krylov subspace iterations. Collaborations with researchers like K. Neymeyr and A.V. Knyazev have produced influential studies in numerical analysis. Zhou’s recent publications (2023–2024) address cluster-robust estimates and angle-free Ritz value bounds, advancing eigensolver algorithms. He is based at the Institute of Mathematics in Rostock, with office 332. His email is ming.zhou@uni-rostock.de . Office hours are by appointment.

Johannes KrausView profile

Professor

• Numerical Solution of Partial Differential Equations
• Finite Element Methods
• Isogeometric Analysis
• +8 more

Prof. Dr. Johannes Kraus is a faculty member at the University of Duisburg-Essen , affiliated with the Faculty of Mathematics . His research focuses on advanced numerical methods for partial differential equations and their applications across multiple disciplines. University: University of Duisburg-Essen Faculty: Faculty of Mathematics Contact: Thea-Leymann-Str. 9, 45127 Essen, Germany Dr. Kraus specializes in numerical solution of partial differential equations , discretization techniques (including finite element and isogeometric analysis), numerical linear algebra , and subspace correction methods like domain decomposition and multigrid. His work extends to high-performance computing and machine learning applications in medicine, engineering, and sciences. Recent publications highlight collaborations on nonlinear poroelasticity , space-time finite element methods , and preconditioning techniques for complex systems. Articles span domains such as biomechanics , biomolecular electrostatics , and multiscale modeling , demonstrating interdisciplinary impact. Teaching responsibilities include Numerical Mathematics II (Summer 2025) and Numerical Mathematics I , with seminars on multiphysics finite element software . He leads the Numerical Mathematics group and has secured third-party funding from DFG and FWF grants.

Eran TreisterView profile

Assistant Professor

• Computational Science
• Scientific Computing
• Numerical Methods
• +8 more

Eran Treister is an Assistant Professor at the Ben Gurion University of the Negev in the Department of Computer Science. He completed his postdoctoral fellowship at the University of British Columbia (2014-2016) and earned his PhD from the Technion in 2014 under Prof. Irad Yavneh. His research spans computational science, numerical methods, and machine learning, with a focus on: Scalable algorithms for inverse problems Graph Neural Networks (GNNs) optimization Seismic and optical imaging via PDE solvers Low-precision deep learning acceleration Multilevel preconditioning techniques Recent work explores: Graph neural networks for PDEs with adaptive meshes Deep learning approaches to Helmholtz equation modeling 3D shape reconstruction via parametric level sets He serves on editorial boards: SIAM Journal on Scientific Computing (2024-) Copper Mountain Conference on Multigrid Methods (2025) International Conference on Machine Learning (ICML) as Area Chair (2025) Current teaching: Optimization Methods for Data Science (Spring 2025) Deep Learning Mini-Project (Winter 2024/5) Advanced Numerical Optimization (Spring 2025)

• Numerical Linear Algebra
• High Performance Computing
• Krylov Subspace Methods
• +4 more

Dr. Hussam Al Daas is a Postdoctoral Research Fellow at the Max Planck Institute for Dynamics of Complex Technical Systems since January 2019, specializing in numerical algorithms for large-scale scientific computing. His work bridges theoretical numerical analysis and high-performance implementation. Education: PhD in Applied Mathematics, Inria-Paris and Sorbonne University (2015-2018), funded by TOTAL Master in Fundamental and Applied Mathematics, Paris-Sud University (2012-2014) His research centers on developing communication-avoiding algorithms for sparse linear systems and tensor computations, with emphasis on robust preconditioners (algebraic two-level Schwarz, domain decomposition), Krylov subspace methods, and low-rank approximations. He addresses critical challenges in parallel scalability through rigorous complexity analysis and memory-efficient implementations, particularly for distributed-memory architectures. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Algebraic multilevel preconditioners achieving robustness for ill-conditioned sparse systems, (2) Theoretical communication lower bounds with optimal algorithm design for tensor/matrix operations, and (3) Novel extensions of Krylov methods for sequences of shifted systems and tensor train decompositions. These contributions target reservoir simulation, PDE-constrained optimization, and high-dimensional data problems. Funded by TOTAL during his PhD and currently by the Max Planck Society, his work shows no evidence of student advisement or external grant leadership. He contributes to the Computational Methods in Systems and Control Theory group, focusing on scalable solvers for complex dynamical systems through collaborative software development and algorithmic innovation.

Sebastian KuckukView profile

Researcher

• High Performance Computing
• GPU Programming
• Parallel Computing
• +7 more

Dr. Sebastian Kuckuk is a researcher and head of training at the Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg. He is affiliated with the Department of Computer Science and contributes to the Chair of System Simulation. His work bridges research, training, and software development in high-performance computing. Education: PhD in Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (2019) His research focuses on enhancing performance portability and programmer productivity using domain-specific languages, code generation, automatic parallelization, and GPU programming. These techniques are applied to develop massively parallel numerical solvers for computational fluid dynamics, particularly for the shallow water equations. He is a core developer of the ExaStencils framework, which enables automated generation of efficient multigrid solvers for structured and patch-structured grids. Analysis of his recent publications (2020–2025) reveals a consistent focus on code generation, GPU acceleration, and solver optimization for fluid dynamics problems. Key themes include heterogeneous computing, block-structured grids, and adaptive methods. His work integrates advanced compiler techniques with numerical mathematics to improve scalability and performance on modern HPC architectures. Scientific Recognition: NVIDIA Deep Learning Institute (DLI) University Ambassador Certified Instructor for DLI courses in GPU programming and CUDA He actively contributes to teaching and training through courses such as Programming Techniques for Supercomputers and High-End Simulation in Practice . He conducts workshops and tutorials on GPU programming and performance optimization. While no formal students are listed, his mentoring role is evident through collaborative research and training activities. He has no recorded grants in the provided text, but his involvement in NHR and KONWIHR projects indicates active participation in funded HPC initiatives. Laboratories and Projects: Lead developer of ExaStencils , a code generation framework for multigrid solvers Contributor to GHODDESS , a module for higher-order discretizations in shallow water modeling Active in NHR@FAU and KONWIHR projects focused on GPU computing and performance optimization