Find Academic Supervisors

• Scientific Machine Learning
• Numerical Linear Algebra
• Numerical Multilinear Algebra
• +7 more

Peter Benner is a Professor and Director at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, where he leads the Computational Methods in Systems and Control Theory group. He also holds an Honorarprofessor position for Mathematics at Otto-von-Guericke Universität Magdeburg since 2011. Benner has previously served as Managing Director of the Max Planck Institute during multiple periods (2013-2014, 2021-2022, and 2025-2026), demonstrating his leadership in the field. Benner's research focuses on Scientific Machine Learning, Numerical Linear and Multilinear Algebra, Model Order Reduction and Reduced-order Modeling, Numerical Methods in Systems and Control Theory, PDE Constrained Optimization, High-performance and Power-aware Computing, and Mathematical Software development. His work bridges theoretical mathematics with practical engineering applications, particularly addressing challenges in large-scale dynamical systems. Analysis of his recent publications reveals a strong emphasis on developing efficient computational methods for complex systems. Benner has pioneered approaches combining model order reduction with tensor methods to tackle high-dimensional problems in uncertainty quantification and PDE-constrained optimization. His work shows a consistent trend toward integrating data-driven techniques with traditional model-based approaches, particularly for nonlinear and parametric systems. Throughout his career, Benner has actively mentored students and collaborated with researchers worldwide, delivering numerous invited talks at prestigious institutions and conferences across Europe, North America, and Asia. His research has received significant funding, supporting the development of mathematical software and computational methods for industrial applications. Benner leads the Computational Methods in Systems and Control Theory department at the Max Planck Institute, which focuses on developing and implementing advanced numerical methods for large-scale dynamical systems. The group maintains strong connections with both theoretical mathematics and practical engineering applications, particularly in fluid dynamics, energy systems, and control theory.

Chun-Hua GuoView profile

Professor

• Matrix analysis
• Scientific computing
• Numerical linear algebra
• +3 more

Chun-Hua Guo is a Professor in the Department of Mathematics and Statistics at the University of Regina, Faculty of Science. His research focuses on matrix analysis, scientific computing, and applications in tensor computations and nonlinear matrix equations. He teaches advanced courses such as MATH 869 Numerical Analysis. Dr. Guo’s recent work emphasizes iterative methods for solving eigenvalue problems of nonnegative tensors, matrix equations arising in nano research, and convergence analysis of numerical algorithms. His publications address topics like Newton-Noda iteration, modified Newton methods for Z-eigenpairs, and algebraic Riccati equations associated with M-matrices. His research trends highlight advancements in computational techniques for matrix functions (e.g., matrix pth root), tensor Perron pairs, and stability analysis of iterative algorithms. These contributions bridge theoretical linear algebra with practical computational challenges in engineering and scientific domains. Dr. Guo’s advising and grants involve developing efficient numerical methods for complex systems. His work has implications for fields requiring high-precision matrix computations, such as control theory, stochastic modeling, and nano-material simulations.

Anders RantzerView profile

Professor

• Control Systems
• Optimal Control Theory
• Energy Networks
• +4 more

Anders Rantzer is a **Professor** at the **Department of Automatic Control** at Lund University, affiliated with LTH (Lund Institute of Technology). He is also a member of key initiatives like ELLIIT (IT and mobile communication) and LTH's profile areas for AI & Digitalization and The Energy Transition. His research focuses on scalable control systems, energy networks, and adaptive methodologies. He has published extensively in top journals and led major projects like the WASP NEST initiative on learning in networks. Rantzer advises numerous PhD students and collaborates internationally on topics ranging from district heating optimization to AI-driven control systems. His work bridges theoretical advancements with practical applications in energy and digital infrastructure.

Tony StillfjordView profile

Associate Professor

• Numerical Analysis
• Partial Differential Equations
• Stochastic Optimization
• +4 more

Tony Stillfjord is an Associate Professor at the Centre for Mathematical Sciences, Lund University, Sweden. He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and Chalmers/University of Gothenburg. Ph.D. and MSc in Numerical Analysis from Lund University Funded by WASP (Wallenberg AI, Autonomous Systems and Software Program) Research Interests : Numerical methods for partial differential equations, splitting schemes, stochastic optimization, and large-scale differential Riccati equations. His work focuses on developing low-rank approximations and robust optimization algorithms. Recent Publications highlight advancements in Lie/Strang splitting for operator-valued Riccati equations, stochastic descent methods, and GPU-accelerated splitting schemes. Software Contributions : Developed DREsplit (MATLAB package for differential Riccati equations) and BST20_CODE for stochastic optimization experiments. Contact : Office at MH:562E, Lund University. Email: tony.stillfjord@math.lth.se . URL: tonystillfjord.net

Lars GrasedyckView profile

Professor

• Numerical Analysis
• Hierarchical Matrices
• Tensor Approximation
• +7 more

Lars Grasedyck is a Professor of Numerical Analysis at RWTH Aachen University. His research focuses on hierarchical matrices, tensor approximation, and numerical methods for partial differential equations and matrix equations. He has contributed to applications in biomedical engineering, particularly EEG/MEG inverse problems, and is involved in software development (HLIB, HLIB-pro). Education: Diploma and Ph.D. in Mathematics at Christian-Albrechts-Universität zu Kiel (1998, 2001), Postdoctoral work at Max Planck Institute, Leipzig (2002-2010). Research: Specializes in high-dimensional numerical methods, low-rank matrices, and tensors with applications in PDEs, uncertainty quantification, and biomedical modeling. Projects: Leads DFG-funded initiatives on adaptive tensor networks for parametric PDEs and tumor progression modeling. Advising: Supervises doctoral students including Thong Le, Maren Klever, and Dieter Moser. Software: Developed HLib and HLib-pro for hierarchical matrix computations. Conferences: Active in GAMM Fachausschuss Numerische Analysis, organizing workshops and symposia globally.

• Numerical Linear Algebra
• Matrix Theory
• Numerical Analysis
• +6 more

Davide Palitta is an Assistant Professor (tenure track) at the Department of Mathematics of the University of Bologna. His research focuses on numerical linear algebra, matrix equations, and their applications in fields like data assimilation, deep learning, and parallel computing. He holds a PhD in Mathematics from the University of Bologna (2018) and has held postdoctoral positions at the Max Planck Institute in Magdeburg (2018–2021) and a visiting fellowship at Brown University (2020). His work emphasizes the development of efficient numerical algorithms for large-scale matrix equations, including Sylvester, Lyapunov, and Riccati equations. Key contributions include Krylov subspace methods, randomized techniques, and time-parallel integration strategies. His recent publications span topics such as sketched Krylov methods, tensor-based solvers, and preconditioning strategies for weak-constraint data assimilation. Palitta collaborates actively with international institutions and organizes seminars like the Scube series on numerical linear algebra. He is involved in upcoming conferences on matrix equations, data assimilation, and parallel computing. No scientific awards are explicitly mentioned in the provided materials.

Moody T. ChuView profile

Professor

• Numerical Ordinary Differential Equations
• Numerical Linear Algebra
• Dynamical Systems
• +5 more

Moody T. Chu is a Professor in the Department of Mathematics at North Carolina State University since 1982, holding a PhD from Michigan State University. His research focuses on numerical linear algebra, dynamical systems, inverse problems, and quantum computing. He has received prestigious teaching awards including the Alumni Distinguished Undergraduate Professorship (2006) and multiple Board of Governors Awards (2010, 2013, 2014). His work bridges computational mathematics with applications in physics, engineering, and data science. Research interests include numerical methods for differential equations, tensor approximation, and quantum simulation. His articles explore topics like Cartan decomposition for quantum Hamiltonians, Lax dynamics, and low-rank tensor approximations. Over 200 publications span numerical analysis, inverse eigenvalue problems, and optimization techniques. Chu’s contributions also address algorithm design for matrix completion and structured low-rank approximations. He has advised numerous graduate students (details not listed here) and led projects on adaptive optics and stochastic processes. His work on nonnegative matrix factorization and Markov chain dynamics has influenced data mining and machine learning applications. Chu’s lab focuses on advancing computational frameworks for complex systems, emphasizing interdisciplinary collaboration.

Ilhan IzmirliView profile

Associate Professor

• Mathematics Education
• Philosophy of Mathematics
• History of Mathematics
• +4 more

Ilhan Izmirli is an Associate Professor in the Department of Statistics within the Volgenau School of Engineering at George Mason University. He maintains an active research program spanning mathematics education, philosophy of mathematics, history of mathematics, and interdisciplinary applications in music and physics. His institutional roles include Course Coordinator for STAT 250 – Introductory Statistics since 2013 and Department Representative on the Distance Education Committee. His educational background includes: PhD in History of Mathematics from American University PhD in Mathematics from University of South Carolina MS in Mathematics from University of Istanbul BS in Mathematics from Bosphorus University Izmirli's research program demonstrates remarkable interdisciplinary breadth, connecting mathematical concepts across domains. His work in mathematics education emphasizes social constructivist approaches, while his investigations into the philosophy of mathematics draw from Lakatos' quasi-empiricism. The music-mathematics interface appears consistently throughout his publications, exploring topics from dodecaphony to interval vectors and contour classes. His historical analyses of mathematical concepts—from Bernoulli's inequality to cubic equations—reveal deep engagement with the evolution of mathematical thought. Recent work shows increasing focus on the pedagogical implications of historical mathematical developments. His scientific contributions have been recognized through multiple honors: Fulbright Scholarship recipient Multiple comprehensive exams passed with distinction Strayer University Faculty Award of Excellence NSF workshop scholarship Selection to Who's Who Among America's Teachers Izmirli maintains active scholarly service through book and paper reviews for Mathematical Reviews/MathSciNet and various journals. His teaching portfolio spans foundational mathematics courses through advanced graduate seminars, with particular emphasis on statistics education. Professional memberships include the Mathematical Association of America, American Mathematical Society, and American Statistical Association. His current research trajectory shows continued exploration of mathematical structures in music theory alongside historical and philosophical investigations of mathematical concepts.

Augusto FerranteView profile

Professor

• Control Theory
• Quantum Computing
• Linear Algebra
• +5 more

Augusto Ferrante is a Full Professor at the Department of Information Engineering, University of Padova. His research spans control theory, quantum computing, and linear algebra, with a focus on optimal control, spectral estimation, and matrix analysis. University: University of Padova Department: Information Engineering Research Interests: Ferrante's work addresses theoretical and applied problems in control systems, including quantum control, Riccati equations, and covariance matrix estimation. His contributions bridge mathematical rigor with engineering applications, particularly in spectral analysis and optimization. Publications Trends: His recent work (2014-2012) emphasizes quantum channel estimation, entropy-based spectral methods, and generalized Riccati equations, reflecting a synthesis of control theory and quantum information science. Book Chapters: He has co-authored works on modeling, estimation, and control, including a 2007 Modeling, Estimation and Control volume dedicated to Giorgio Picci.

Stefan JohanssonView profile

Associate Professor

• Visualization
• Numerical Linear Algebra
• Computer Graphics
• +4 more

Stefan Johansson is an Associate Professor at Umeå University's Department of Computing Science, where he also serves as Deputy Head of Department overseeing finance and personnel. He maintains a part-time research affiliation with Curiosum Science Center, leading technical development for exhibitions and dome theaters. His research focuses on visualization , numerical linear algebra , and interactive educational tools . Current projects include VisA, which develops dome-theater visualizations of atmospheric data for science education. His work bridges computer graphics, software engineering, and didactic methods. Johansson's publications reflect dual expertise: earlier works emphasize matrix computations and control theory (2004-2017), while recent research shifts toward scientific visualization applications (2020-2025), particularly in geosciences and educational technology. Awards & Leadership Recognised University Teacher (Meritorious Teacher) 2021 Program Director, MSc in Computing Science (2022-2023) Deputy Director, Curiosum Science Center (2019-2021) He contributes to research groups in Matrix Computations with Applications and Parallel and Scientific Computing , and develops STEM curricula for primary/secondary schools.

Patrick KürschnerView profile

Associate Professor

• Numerical Linear Algebra
• Model Order Reduction
• Eigenvalue Problems
• +8 more

Patrick Kürschner is an Associate Professor for Numerical Mathematics and Linear Algebra at Leipzig University of Applied Sciences (HTWK Leipzig) . He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and KU Leuven's Kulak Kortrijk Campus. His work focuses on computational methods in systems and control theory. PhD in Mathematics (Otto-von-Guericke University, 2015) MSc in Mathematics (Chemnitz University of Technology, 2010) BSc in Financial Mathematics (Chemnitz University of Technology, 2008) His research interests span numerical linear algebra, matrix equations and functions, eigenvalue problems, preconditioning, model order reduction, tensor methods, and numerical algorithms for digital signal processing. He develops efficient computational techniques for large-scale systems in control theory and data science applications. Recent publications include advancements in low-rank ADI iteration, time-limited balanced truncation, inexact linear solvers, and tensor decompositions for polynomial systems. His work addresses challenges in computational neuroscience, mechanical simulations, and optimal control.

• Control Systems
• Operator Theory
• Signal Processing
• +5 more

Arthur E. Frazho is a Professor in the School of Aeronautics and Astronautics at Purdue University, holding this position since 1980. He earned his B.S.E., M.S.E., and Ph.D. in Computer Engineering and Control Systems from the University of Michigan, Ann Arbor. His research focuses on applying operator theory to control systems, including deterministic and stochastic models, signal processing, and inverse scattering. He has authored influential books such as An Operator Perspective on Signals and Systems (2010) and Linear Systems and Control (2003). Frazho received the prestigious 2006 W. A. Gustafson Award for teaching excellence. His work spans theoretical advancements in Hinfinity controller reduction, recursive algorithms, and multirate filterbanks. Collaborations with institutions like Vrije Universiteit, Amsterdam, highlight his international academic reach. Education: B.S.E., University of Michigan, Ann Arbor, Computer Engineering (1973) M.S.E., University of Michigan, Ann Arbor, Computer Information and Control Engineering (1974) Ph.D., University of Michigan, Ann Arbor, Computer Information and Control Engineering (1977) His research integrates operator theory with practical engineering challenges, yielding solutions for control systems, signal processing algorithms, and mathematical frameworks for system analysis. Notable contributions include relaxed commutant lifting techniques and applications to inverse scattering. Frazho has presented invited lectures globally and contributed to over 20 publications across journals and conference proceedings. Awards: 2006 – W. A. Gustafson Award (Teaching Excellence) Grants & Advising: While no student advisees are listed, Frazho has secured research grants supporting his theoretical work in control systems and operator theory. His collaborations include projects on multirate filterbanks and hydraulic cylinder analysis. Labs & Teams: No specific laboratory affiliations are detailed, but his work aligns with Purdue’s broader aerospace and systems engineering initiatives.

Silvère BonnabelView profile

Professor

• Systems and control
• Robotics
• Machine learning
• +5 more

Silvère Bonnabel is a Professor at École des Mines ParisTech (Mines ParisTech), a prestigious engineering school in France. He holds a Research Habilitation HDR (2014) and a PhD (2007) from Mines ParisTech, along with advanced degrees in financial mathematics and engineering. His professional experience includes roles as Professor at the University of New Caledonia (2019-2022), Visiting Fellow at the University of Cambridge (2017), and Assistant Professor at Mines ParisTech (2009-2016). Bonnabel’s research focuses on systems and control, robotics, machine learning, and navigation, with notable contributions to invariant Kalman filtering and optimization on manifolds. He has authored/co-authored over 100 articles, 9 patents, and achieved an h-index of 39. Notable awards include the 2024 George N. Saridis Award and 2021 European Control Award. His work bridges theoretical advancements with industrial applications, including collaborations with companies like Safran, Thales, and Manitowoc. Bonnabel has supervised numerous PhD students, contributing to breakthroughs in inertial navigation, radar tracking, and autonomous systems. Education: 2014: HDR in Mathematics (Paris VI Sorbonne) 2004-2007: PhD, Mines ParisTech 2003-2004: Master in Financial Mathematics (Paris XII) 2001-2004: Engineering Degree, Mines ParisTech Industrial Collaborations: Partnerships with Safran, Thales, Manitowoc, and startups like OFFROAD, focusing on crane control, inertial navigation, and autonomous systems. Editorial Roles: Associate Editor for IEEE Control Systems Magazine, European Control Conference, and Systems & Control Letters. His research emphasizes geometric control theory, stochastic algorithms, and sensor fusion, with applications to robotics, aerospace, and industrial automation. Bonnabel’s work on invariant extended Kalman filters has been commercialized in products like Safran’s inertial navigation systems.

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.

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.