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Thomas MachView profile

Researcher

• Numerical Linear Algebra
• Data Analysis
• Inverse and Ill-Posed Problems
• +5 more

Dr. Thomas Mach is a Postdoctoral Researcher at the Institute of Mathematics , University of Potsdam, Germany, focusing on Data Assimilation under Prof. Melina Freitag. His work bridges theoretical and applied numerical linear algebra. Research Interests Numerical linear algebra for large/structured matrices Krylov subspace methods and iterative eigenvalue solvers Adaptive cross approximation and regularization techniques Applications to inverse problems and parametric systems Publication Trends show expertise in matrix eigenvalue problems, regularization for ill-posed equations, and algorithm design for structured systems. Co-authored works with leading experts like David S. Watkins demonstrate methodological innovation. Awards : SIAM Outstanding Paper Prize (2015) for polynomial root-finding research Academic Service includes co-organizing workshops, serving as Associate Editor for numerical analysis journals, and active participation in international conferences like ILAS, SIAM, and GAMM.

Ron MorganView profile

Professor

• Numerical Analysis
• Computational Mathematics
• Linear Algebra
• +2 more

Ron Morgan is a Professor of Mathematics at Baylor University and serves as an Undergraduate Advisor. His primary affiliation is with the Department of Mathematics, where he focuses on numerical analysis and computational methods. Research Interests Numerical analysis of large linear systems and eigenvalue problems Development of efficient algorithms for Monster Matrices Applications in computational mathematics and sparse matrix techniques Specialized in Krylov subspace methods like GMRES and Arnoldi Scientific Contributions are centered on iterative solvers, deflation techniques, and multigrid methods for eigenvalue problems. His work spans publications in prestigious journals such as SIAM and Mathematics of Computation. Former Ph.D. Students Zhao Yang (2015) Huy Nguyen (2015) Quan Liu (2014, Co-Advised) Dywayne Nicely (2008) Dean Darnell (2006, Co-Advised) Min Zeng (1996) Contact: Ronald_Morgan@baylor.edu | (254) 710-6575

Tobias DammView profile

Professor

• Control Theory
• Numerical Analysis
• Linear Systems
• +3 more

Prof. Dr. Tobias Damm is a leading academic in the Department of Mathematics at RPTU Kaiserslautern-Landau, heading the Technomathematics Group. His work focuses on control theory, numerical analysis, and stochastic systems, with significant contributions to model reduction, linear systems, and optimization. He holds a professorship and has authored influential publications on topics such as balanced truncation, stochastic control, and numerical methods for large-scale systems. Research Interests: Tobias Damm’s research emphasizes the interplay between theoretical mathematics and practical engineering challenges. Key areas include model order reduction techniques for complex systems, optimal control strategies, and the numerical solution of Lyapunov equations. His work bridges stochastic systems, linear algebra, and control engineering, with applications in energy networks and power systems. Recent Articles: His recent publications explore distributed model predictive control for power systems, complexity reduction of stochastic systems, and Hadamard powers in kernel methods. These contributions highlight advancements in both theoretical frameworks and applied methodologies. Advising & Grants: Damm’s research is supported by grants focusing on energy systems and control theory. His work often collaborates with interdisciplinary teams, addressing challenges in district heating networks and hyperbolic systems reduction. Labs/Teams: He leads the Technomathematics Group, fostering collaborations on cutting-edge problems in control systems and numerical analysis.

Bruno CarpentieriView profile

Associate Professor

• Numerical Linear Algebra
• High-Performance Computing
• Computational Electromagnetics
• +5 more

Bruno Carpentieri is an Associate Professor in Applied Mathematics at the Faculty of Computer Science , Free University of Bozen-Bolzano , Italy. His work focuses on Numerical Linear Algebra , High-Performance Computing , and Preconditioning Techniques for large-scale scientific problems. Education : Laurea in Applied Mathematics (1997, University of Bari Aldo Moro); PhD in Computer Science (National Polytechnic Institute Toulouse). Research Interests : Sparse linear systems, Krylov subspace methods, computational electromagnetics, PageRank problems, and fractional calculus applications in nonlinear engineering. His publications (1998–2025) emphasize efficient solvers for multi-shifted systems, parallel computing, and preconditioning strategies. Recent works (2025) explore fractional-order methods and multilayer network centrality . He has collaborated on ITER tokamak simulations (F4E/MIUR grants) and biomedical finite element models . Grants : F4E-2008-OPE-06-06-11 (ITER analysis); MIUR PRIN 2010SPS9B3. Key Contributions : VBARMS preconditioner (2014), hybrid block GMRES variants (2018), and chaos-enhanced fractional solvers (2025).

• Quantum Chemistry
• Materials Science
• Mathematics
• +12 more

Michael F. Herbst is a tenure-track Assistant Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) with joint appointments in Mathematics and Materials Science. He leads the Mathematics for Materials Modelling (MatMat) research group, focusing on algorithm development for quantum-chemical simulations of solids and error control in computational modeling. His work bridges mathematics, solid-state physics, and computer science through interdisciplinary research. His research interests include: Density-functional theory (DFT) and Kohn-Sham equations Error propagation in materials property predictions High-throughput screening algorithms Julia programming for scientific computing Tensor networks and reduced basis modeling Self-consistent field convergence methods Recent publications highlight his contributions to: Efficient response property calculations in DFT Rotationally equivariant machine learning operators GPU-accelerated electronic structure methods Polarizable continuum solvation models Robust black-box quantum chemistry algorithms Open-source software development Teaching activities span mathematics, computer science, and chemistry curricula, including interdisciplinary workshops on electronic structure numerics and Julia programming for materials science. He has mentored PhD students Bruno Ploumhans and Niklas Frederik Schmitz.

King-Wah ChuView profile

Research Fellow

• Numerical Analysis
• Numerical Linear Algebra
• Control Theory
• +3 more

King-Wah Chu is an Adjunct Senior Research Fellow at the School of Mathematics, Monash University. He has been working in Melbourne, Australia for 30 years. His research focuses on numerical analysis, numerical linear algebra, eigenvalue problems, control theory, and matrix equations. He serves as an Associate Editor for the SIAM Journal on Matrix Analysis and Applications since 2015. Education: Hong Kong and England Affiliations: Monash University, SIAM Journal His research interests include perturbation and numerical solutions of eigenvalue problems, inverse eigenvalue problems, Riccati equations, and large-scale structured computations. He has contributed to the development of algorithms for control systems and tensor computations. Key Projects: Research visit to the International Symposium on Mathematical Theory of Networks & Systems (2000) Advising & Grants: Chu has advised on multiple projects but no specific students are listed. His work involves collaborations on large-scale algebraic Riccati equations and numerical methods. Labs/Teams: Active in the School of Mathematics at Monash University, focusing on numerical linear algebra and control systems.

• Numerical Linear Algebra
• Ill-posed Problems
• Krylov Subspace Methods
• +2 more

Kirk Soodhalter is the Ussher Assistant Professor in Numerical Analysis at the School of Mathematics, Trinity College Dublin. His research focuses on numerical linear algebra, ill-posed problems, Krylov subspace methods, and high-performance computing applications. He can be reached at ksoodha@maths.tcd.ie and is based in Office 244 of the Llyod Building. His work integrates advanced computational techniques with applications in astrophysics, fluid dynamics, and quantum computing. Research Interests: His primary areas include developing efficient iterative methods for large-scale linear systems, particularly those arising in PDEs and quantum algorithms. He explores Krylov subspace recycling, preconditioning strategies, and sparse grid sampling to address challenges in numerical stability and computational efficiency. Publications: Recent work demonstrates contributions to quantum super-Krylov methods for ground state estimation, fast solutions for Sylvester-structured systems in astrophysics, and hybrid iterative techniques for ill-posed problems. His articles often emphasize high-performance computing frameworks and their practical implementations. Awards: No scientific awards explicitly mentioned in the provided text. Advising & Grants: No student advisees or grant details listed. His research is primarily conducted through Trinity College Dublin's School of Mathematics infrastructure.

Patrick KürschnerView profile

Professor

• Numerical and applied multilinear algebra
• Large-scale eigenvalue problems
• Systems of equations
• +11 more

Prof. Dr. rer. nat. Patrick Kürschner is a faculty member at the Mathematisch-Naturwissensch. Zentrum of Leipzig University of Applied Sciences (HTWK Leipzig) . He teaches mathematics and programming for engineering programs including Civil Engineers, BIM, INB, INM, and BIK. His office hours are on Tuesdays from 2:00-3:00 p.m. and by appointment. Research Focus : His work centers on Numerical and applied multilinear algebra with applications to large-scale eigenvalue problems, systems of equations, matrix equations, and matrix functions. He specializes in preconditioning techniques and low-rank approaches for matrix/tensor methods, solution methods for polynomial equations, mathematical systems and control theory (particularly model order reduction and linear-quadratic control), numerical optimization, digital signal processing, data science, and machine learning. Publication Trends : Dr. Kürschner's research spans Numerical Linear Algebra , Control Theory , and Scientific Computing . His work addresses problems in Large-scale Matrix Equations , Model Order Reduction , Polynomial System Solutions , and Low-rank Approximation for applications in Engineering and Data Science . Recent publications focus on efficient numerical methods for Lyapunov and Sylvester equations, time-limited balanced truncation, and connectome regression techniques.

• Numerical Analysis
• Ordinary Differential Equations (ODEs)
• Differential-Algebraic Equations (DAEs)
• +5 more

Dr. Helmut Podhaisky is a researcher at the Institute of Mathematics , Martin Luther University Halle-Wittenberg , within the Faculty of Natural Sciences II. His work focuses on numerical methods for differential equations, particularly stiff ODEs, DAEs, and Volterra equations. Email: podhaisky@mathematik.uni-halle.de Research Interests: Numerical methods for stiff and oscillatory differential equations Runge-Kutta and peer methods Parallel computing algorithms Mathematical modeling of calcium waves in biological systems Recent Publications highlight his work on barycentric rational methods, adaptive integrators, and bifurcation analysis in oscillating systems. His research spans both theoretical developments and practical applications in computational mathematics.

Anestis DounavisView profile

Associate Professor

• VLSI Design Automation
• High-Speed Interconnect Modeling
• Signal Integrity Analysis
• +7 more

Anestis Dounavis is an Associate Professor in the Department of Electrical and Computer Engineering at the Faculty of Engineering, University of Western Ontario. He holds a Ph.D. and is a licensed Professional Engineer (P.Eng.). Research Interests Design Automation of VLSI Circuits Modeling and Simulation of High-Speed Interconnects Signal Integrity (Board, Package, and Chip Level) Electromagnetic Interference and Immunity Analysis Mixed Signal Simulation Model-Order Reduction Techniques Simulation of Microelectromechanical (MEMS) and Optoelectronic Systems His work focuses on developing efficient algorithms for transient analysis of power distribution networks, electromagnetic compatibility, and passive macromodeling techniques. He has published extensively in top-tier IEEE journals, with recent articles spanning waveform relaxation methods, parameterized reduction, and sensitivity analysis. Notable scientific awards include: Ontario Ministry of Research and Innovation Early Research Award (2009) Carleton University Medal for Ph.D. Excellence (2004) Ottawa Centre for Research and Innovation Futures Award (2004) INTEL Best Student Paper Award (2003) University Medal at Master’s Level (2000) He has declined prestigious postdoctoral fellowships, including an NSERC Post-doctoral Fellowship (2003). Teaching recognitions include the University Student Council’s Teaching Honour Roll (2009-2010).

Jennifer ErwayView profile

Professor

• Computational Mathematics
• Numerical Optimization
• Computational PDEs
• +3 more

Jennifer Erway is a Professor of Mathematics and Affiliate of Computer Science at Wake Forest University. Her primary research focuses on Computational Mathematics , specializing in Numerical Optimization, Computational PDEs (particularly finite element methods), and Numerical Linear Algebra. She leads research on large-scale optimization algorithms and develops computational methods for scientific applications. Her research interests center on: Trust-region methods and quasi-Newton updates Numerical software development for optimization Applications in machine learning and imaging Sparse recovery and compressed sensing Matrix computations and eigenvalue problems Her publications demonstrate consistent focus on developing efficient algorithms for optimization problems, with recent work expanding into machine learning applications. Research is primarily supported by NSF grants including DMS-08-11106, CMMI-1334042, and IIS-1741264. Awards & Honors: Best Paper Award at World Congress on Engineering (2010) ORAU Ralph E. Powe Junior Faculty Enhancement Award Multiple NSF grants supporting computational mathematics research Research Training: Has supervised numerous graduate and undergraduate students in mathematics and computational research, including PhD candidates and master's students working on optimization algorithms and numerical methods.

Pawel CaputaView profile

Professor

• Quantum Information
• AdS/CFT Correspondence
• Quantum Complexity
• +4 more

Pawel Caputa is a Professor at the Institute of Theoretical Physics, Faculty of Physics, University of Warsaw. His research focuses on quantum complexity, AdS/CFT correspondence, holography, and quantum information theory, with recent work on Krylov basis applications and path integral optimization. University: University of Warsaw School: Faculty of Physics Department: Institute of Theoretical Physics Academic Rank: Professor His research explores the interplay between quantum mechanics, gravity, and information theory, particularly through frameworks like Krylov complexity and modular Hamiltonian evolution. Recent publications highlight advancements in understanding operator growth, topological phase transitions, and holographic entanglement. Notable contributions include the ERC Consolidator Grant for work on quantum complexity measures and participation in international workshops like the YITP "Quantum Extreme Universe" collaboration. His studies bridge theoretical physics with computational models, emphasizing non-equilibrium dynamics and quantum chaos. Scientific Awards: ERC Consolidator Grant Pawel Caputa's articles from 2022-2025 analyze Krylov complexity, AdS/CFT applications, and quantum information dynamics, establishing connections between abstract quantum mechanics and gravitational phenomena.

Joseph M.L. MaubachView profile

Assistant Professor

• Computational Science and Engineering
• Numerical Analysis
• Mathematical Modeling
• +5 more

Jos M.L. Maubach is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science. He is part of the Centre for Analysis, Scientific Computing, and Applications (CASA), focusing on computational science and engineering. His research emphasizes numerical methods, model order reduction, and applied mathematics for engineering problems. Academically, Maubach holds an MSc (1986) and PhD (1991) in Mathematics from Radboud University Nijmegen, with his doctoral work on iterative methods for nonlinear partial differential equations. He joined TU/e in 1998. Research interests include computational methods for engineering systems, thermal reliability of electronics, and numerical linear algebra. His work spans topics like saddle-point matrices, Krylov subspace methods, and thermal modeling of LED packages. Teaching responsibilities include courses such as Advanced Calculus II, Analysis 1, Programming and Modelling, and Scientific Programming. He has supervised 28 research projects but student names are not listed here. Maubach collaborates with institutions like CASA and has published widely in areas such as model reduction, thermal systems, and preconditioning techniques.

• Numerical Linear Algebra
• High Performance Computing
• Machine Learning
• +5 more

Ren-Cang Li is a Professor of Mathematics at The University of Texas at Arlington, with affiliations including past roles as Chair Professor at Hong Kong Baptist University and positions at University of Kentucky and Oak Ridge National Laboratory. He earned his BS from Xiamen University (1985), MS from Chinese Academy of Sciences (1988), and PhD in Applied Mathematics from UC Berkeley (1995). His research focuses on numerical linear algebra, eigenvalue problems, high-performance computing, and machine learning, with impactful contributions like secular equation solvers in LAPACK and work on matrix equations. Awards include the NSF CAREER Award (1999) and multiple distinguished paper awards. Education: PhD in Applied Mathematics, UC Berkeley, 1995 MS in Computational Mathematics, Chinese Academy of Sciences, 1988 BS in Computational Mathematics, Xiamen University, 1985 Research interests span numerical algorithms, eigenvalue computations, optimization, and applications in machine learning. Notable contributions include methods for large-scale eigenvalue problems, matrix equations, and compressed sensing. His work on the secular equation solver is foundational in scientific computing libraries. Key Awards: Friedman Memorial Prize in Applied Mathematics (1996) NSF CAREER Award (1999) Three Distinguished Paper Awards from ICCM (2018-2020) INFORMS Data Mining Best General Paper (2019) Advising and Grants: Supervised numerous PhD students and led federally funded projects totaling over $5M, including work on probabilistic graph learning and nonlinear matrix equations. Active in editorial roles for journals like SIAM Journal on Matrix Analysis and Mathematical Communications .

Wojciech L. GolikView profile

Professor

• Numerical Analysis
• Computational Mathematics
• Computational Electromagnetics
• +3 more

Dr. Wojciech L. Golik serves as Professor of Mathematics and Assistant Dean for Mathematical and Natural Sciences at Lindenwood University's College of Science, Technology, and Health. With a distinguished academic career spanning over three decades, he holds leadership responsibilities while maintaining active research and teaching commitments. His educational background includes a Master of Science in Mechanical Engineering from Poznan Technological University (1982), followed by an M.S. (1985) and Ph.D. (1988) in Mathematics from New Mexico State University. His doctoral dissertation focused on Convergence of the Boundary Integral Methods in Numerical Solutions of Fourier Problems . Dr. Golik's research centers on advanced computational methodologies, with particular expertise in numerical analysis, computational electromagnetics, and data science applications. His work demonstrates sophisticated integration of wavelet theory with electromagnetic modeling, developing innovative algorithms for solving complex boundary value problems and integral equations. The publication timeline reveals sustained contributions to computational mathematics, with recent expansion into data science and interdisciplinary applications. His 2013 Polish-language publication Siedemdziesiąt Trzy indicates broader intellectual interests beyond pure mathematics. Teaching responsibilities encompass foundational and advanced mathematics courses including Calculus sequences, Differential Equations, Linear Algebra, Numerical Analysis, and specialized topics like Computational Electromagnetics and History of Mathematics. His extensive course portfolio reflects deep expertise across both theoretical and applied mathematics domains. Professional activities include significant administrative leadership as Assistant Dean, though specific grant funding details aren't documented in available materials. His departmental involvement supports multiple undergraduate and graduate programs including Mathematics (BA/BS), Data Science, and Actuarial Science tracks within the College of Science, Technology, and Health.