جست‌وجوی استاد و پژوهشگر

• Riemannian Optimization
• Matrix Manifolds
• Low-Rank Matrix Completion
• +۴ مورد دیگر

Pierre-Antoine Absil is a Full Professor at the Louvain Polytechnic School (EPL) , part of the Université catholique de Louvain (UCLouvain) , affiliated with the Mathematical Engineering Center (INMA) and the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM) . His research spans Riemannian optimization , matrix manifolds , and low-rank approximation with applications in astronomical imaging , signal processing , and environmental data imputation . His work emphasizes Riemannian optimization algorithms matrix and tensor completion geometric data analysis applications in astrophysics and bioinformatics Recent publications focus on Stiefel and Grassmann manifolds , variable projection methods , and exoplanet detection via alternating minimization . While no explicit scientific awards are listed, his collaborations with institutions like IEEE and Springer highlight his impact in computational mathematics and engineering. He has supervised researchers such as Simon Vary , Guillaume Olikier , and Valentin Christiaens through projects in direct imaging , economic dispatch , and graph-based data processing . His laboratory, INMA , drives methodological advances in manifold-valued data analysis and nonlinear optimization .

• Polyhedral combinatorics
• Extended formulations
• Combinatorial optimization
• +۲ مورد دیگر

Samuel Fiorini is Associate Professor in the Department of Mathematics at the Université libre de Bruxelles (ULB) , member of the Algebra and Combinatorics group (CP 216). His research centres on polyhedral combinatorics, extended formulations, combinatorial optimisation and approximation algorithms , with frequent overlap into structural graph theory. Research in depth: Fiorini’s work explores how high-dimensional polytopes can sometimes be expressed compactly through extended formulations, proving exponential lower bounds when they cannot. He has contributed new approximation algorithms for classical problems such as vertex cover, clique transversal and odd-cycle packing, and has advanced the understanding of sorting and entropy in partially ordered sets. His papers often combine tools from graph minors, communication complexity and polyhedral theory. Scientific recognition: Best Paper Award, 44th ACM Symposium on Theory of Computing (STOC 2012) Programme committees: FOCS, IPCO, APPROX, STACS, WAOA Organiser, Sixth Cargese Workshop on Combinatorial Optimization Advising & grants: He currently supervises PhD students Carole Muller and Matthew Drescher and has mentored six completed PhDs as well as more than a dozen post-doctoral researchers. His group has been supported by an ERC starting grant and other national and international projects focusing on polyhedral approaches to hard optimisation problems. Lab & team: Fiorini leads a vibrant team within the Algebra and Combinatorics cluster at ULB, maintaining active collaborations with researchers worldwide and hosting frequent visitors working on discrete optimisation and polyhedral combinatorics.

Silvère Bonnabelمشاهده پروفایل

استاد

• Systems and control
• Robotics
• Machine learning
• +۵ مورد دیگر

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.

Pierre-Yves Gousenbourgerمشاهده پروفایل

مدرس

• Interpolation on Manifolds
• Optimization on Manifolds
• Bézier Curves and Surfaces
• +۵ مورد دیگر

Pierre-Yves Gousenbourger is a Teaching Assistant at the École polytechnique de Louvain (Louvain School of Engineering), part of the Catholic University of Louvain. He is affiliated with the ICTEAM research institute and supervises students in the Department of Electrical Engineering. His pedagogical approach includes the Apprentissage Par Projet (project-based learning) methodology. He holds a PhD (2020) and a Master's in Applied Mathematics from UCLouvain, both supervised by Pierre-Antoine Absil. His doctoral thesis, Interpolation and fitting on Riemannian manifolds , developed efficient algorithms for manifold-valued data fitting with applications in UAV control, medical imaging, and model reduction. His research focuses on interpolation, optimization, and imaging on Riemannian manifolds , emphasizing Bézier curves/surfaces for data approximation. Key areas include wind field estimation via covariance matrix fitting on SPSD manifolds, medical shape reconstruction (e.g., endometrial surfaces), and parametric model order reduction using manifold interpolation. His work integrates Riemannian geometry with computational efficiency, leveraging tools like exponential/logarithm maps. His publications consistently address data fitting on nonlinear manifolds , featuring Bézier-based methods, variational formulations, and applications spanning computer vision, medical imaging, and aerodynamics. Trends include denoising corrupted data, accelerating computational workflows, and generalizing Euclidean splines to manifolds. He collaborates with the RANSO and Dysco research groups and has worked with institutions including MIT, TU Chemnitz, and the University of Münster. He develops open-source numerical tools for manifold interpolation, distributed under the GNU GPL license.

• Numerical Linear Algebra
• Krylov Methods
• Matrix Functions
• +۲ مورد دیگر

Michele Rinelli is a postdoctoral researcher at the Numerical Analysis and Applied Mathematics (NUMA) department of KU Leuven . His work focuses on advanced computational methods for matrix analysis and applications. Numerical Linear Algebra Krylov Methods Matrix Functions Trace Estimation Structured Matrices Recent publications highlight his contributions to stochastic probing techniques, rational Krylov methods, and decay bounds in matrix functions. These works address challenges in handling sparse and large-scale matrices, with applications in quantum computing and computational science. Collaborations with researchers like Andrea Frommer, Martin Benzi, and Stefano Pozza underscore his interdisciplinary approach. Though no explicit awards or teaching activities are detailed in the provided information, his research outputs indicate active participation in academic conferences and invited presentations, including engagements at Charles University in Prague and Sorbonne Université in Paris.

• Computer Engineering
• Mathematical Engineering
• Graph Algorithms
• +۵ مورد دیگر

Alexandru-Cristian Mara is a postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture , affiliated with the Department of Electronics and Information Systems . His research focuses on network representation learning, graph embeddings, and machine learning methodologies. Research Interests : Network embedding evaluation, link prediction, graph robustness, and reproducibility in machine learning. Methodological Contributions : Developed EvalNE , a framework for evaluating network embeddings on link prediction tasks. Key Findings : Demonstrated limited progress in network embedding methods compared to simple heuristics and analyzed robustness against network perturbations.

Ivan Markovskyمشاهده پروفایل

دانشیار

• Structured Low-Rank Approximation
• System Identification
• Data-Driven Control
• +۷ مورد دیگر

Ivan Markovsky is an Associate Professor at the Department of Fundamental Electricity and Instrumentation (ELEC) of the Vrije Universiteit Brussel. He previously held positions at the School of Electronics and Computer Science (ECS) at the University of Southampton. His research focuses on structured low-rank approximation, system identification, and data-driven control, with applications in signal processing and control systems. Education: PhD in Electrical Engineering from Katholieke Universiteit Leuven (2005). Research Interests: Specializes in low-rank approximation methods, system identification, and their applications in control theory and signal processing. Develops numerical optimization algorithms for structured matrices and tensors, with emphasis on practical engineering problems. His work bridges theoretical mathematics and applied control systems engineering. Awards: Received an ERC Starting Grant (2011) for research on structured low-rank approximation. Grants & Projects: ERC Starting Grant (2011-2016) - Structured Low-Rank Approximation FWOAL827 (2017-2020) - Block-oriented nonlinear identification using Volterra series FWOAL758 (2015-2018) - Decoupling multivariate polynomials in nonlinear system identification Labs/Teams: Leads research groups focused on system identification, matrix approximation, and data-driven control methodologies. Collaborates internationally on projects involving tensor approximation and nonlinear system modeling.

Karl Meerbergenمشاهده پروفایل

استاد

• Algebraic eigenvalue problems
• Algebraic model order reduction
• Preconditioning
• +۷ مورد دیگر

Karl Meerbergen is a Full Professor in the Department of Computer Science at KU Leuven, Faculty of Engineering Sciences. He leads research in numerical analysis and applied mathematics with a focus on eigenvalue problems, model order reduction, and computational linear algebra. He is a member of the Numerical Analysis and Applied Mathematics (NUMA) research unit and holds affiliations with multiple KU Leuven institutes including iSi Health, Leuven.AI, Leuven.AM, and the LGI Gravitation Institute. His research spans several key areas of numerical mathematics with emphasis on algebraic eigenvalue problems, algebraic model order reduction, preconditioning techniques, computational acoustics, tensor computations, exascale computing, generic programming, and parallel computing. His work bridges theoretical numerical analysis with practical applications in engineering and scientific computing. Analysis of his recent publications shows a strong focus on advanced numerical methods for eigenvalue problems, model order reduction techniques, and parallel computing approaches. His work frequently addresses challenges in large-scale scientific computing, with applications in structural dynamics, acoustics, and optimization problems. The research demonstrates increasing sophistication in handling nonlinear and parametric systems through rational approximation methods and specialized preconditioning techniques. Dr. Meerbergen actively contributes to the academic community through his teaching responsibilities and supervision of graduate students. His work has significant implications for computational science and engineering applications requiring efficient numerical solutions to complex mathematical problems.

• Mathematical Physics
• Network Science
• Complex Systems
• +۳ مورد دیگر

Overview Jean-François de Kemmeter is an academic researcher at the University of Namur, where he leads research projects focusing on reaction-diffusion processes, network science, and complex systems. He has been Principal Investigator on projects like Reaction-diffusion processes on temporal and non-normal networks (2020–2024), exploring dynamics in evolving networks and segregation phenomena. Research Interests His work spans Mathematical Physics (e.g., 6V model, tiling problems), Network Science (nonlinear random walks, multigraph reconstruction), and Complex Systems (contagion dynamics, criticality in interacting systems). He combines numerical simulations with analytical methods to study self-organization, power-law distributions, and phase transitions in both theoretical and applied contexts. Key Contributions Recent articles highlight his focus on boundary conditions in tiling models, distrust-driven social contagion on simplicial complexes, and segregation in reaction-diffusion systems. His 2024 thesis Emergence of criticality... integrates random walker dynamics and arctic curve phenomena. Awards Best presentation at the Annual Meeting of the COMPLEX Doctoral School (2022) Winner of the PhD Presentations contest (2022) Career Highlights Active in international conferences (SIAM Dynamical Systems, NERCCS) and workshops. Collaborations span topics like nonlinear problems and diffusion on temporal networks.

Nick Vannieuwenhovenمشاهده پروفایل

استادیار

• Tensor decompositions
• Numerical analysis
• Riemannian optimization methods
• +۲ مورد دیگر

Nick Vannieuwenhoven is an Assistant Professor at KU Leuven, affiliated with the Department of Computer Science and the NUMA Division. He serves as the Exchange Coordinator for the Master in Mathematical Engineering and is an Associate Editor for The Electronic Journal of Linear Algebra and SIAM Journal on Applied Algebra and Geometry . His research focuses on tensor decompositions, numerical analysis, Riemannian optimization, and applications in data science. He obtained his PhD in 2015 under Professors Karl Meerbergen and Raf Vandebril, funded by the FWO (Research Foundation Flanders). His postdoctoral research (2015–2021) was also supported by FWO fellowships. His research group investigates tensor decompositions, multilinear algebra, and numerical techniques for data science, with a focus on condition number analysis and Riemannian optimization. Collaborators include experts like Carlos Beltrán, Paul Breiding, and Simon Telen. Current students include Jana Jovcheva, Bram Leys, and David Thorsteinsson, working on manifold-valued function approximation, group-invariant networks, and data-based engineering. Key awards include FWO fellowships for his PhD and postdoctoral studies. Grants include support for postdoctoral researchers via MSCA and FWO schemes. Notable projects involve Tucker compression libraries (ATC) and geometric analysis of tensor networks. His work bridges algebraic geometry, numerical analysis, and machine learning, emphasizing stability and computational efficiency.

• Numerical Analysis
• Approximation Theory
• Fractional Calculus
• +۳ مورد دیگر

Amin Faghih is a postdoctoral scholarship holder at KU Leuven's Numerical Analysis and Applied Mathematics (NUMA) research unit within the Arenberg campus. His academic work focuses on advanced computational mathematics with emphasis on numerical methods for differential equations and approximation theory. His research interests span Numerical Analysis , Approximation Theory , and Linear Algebra , particularly investigating Sobolev spaces, orthogonal polynomials, and fractional differential equations. His recent work develops novel algorithms for rational approximation and spectral solvers, bridging theoretical mathematics with practical computational implementations. Analysis of his 2024-2025 publications reveals a strong trend toward efficient numerical methods for Sobolev-related problems, with increasing focus on rational Arnoldi approaches and orthogonal polynomial recurrences. His work demonstrates interdisciplinary connections between computational mathematics, linear algebra, and differential equations. Faghih actively contributes to the academic community through seminar presentations at institutions including UC Louvain, KU Leuven, and international conferences such as the International Linear Algebra Society meetings. His research presentations cover specialized topics in numerical linear algebra and approximation theory. Based at Celestijnenlaan 200a (box 2402) in Leuven, he operates within KU Leuven's NUMA research environment, collaborating with prominent researchers including Van Barel, Vandebril, and Van Buggenhout on cutting-edge numerical algorithms.

• Combinatorics
• Computer Science
• Log-concave Polynomials
• +۳ مورد دیگر

Jonathan Leake is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo . His research focuses on log-concave polynomials and their applications in combinatorics and computer science. Education: PhD in Mathematics, UC Berkeley (2014-2019) MS in Mathematics, Texas A&M University (2010-2012) BS in Computer Engineering and Applied Math, Texas A&M University (2006-2010) Research Interests: His work centers around log-concave polynomials and their connections to combinatorics, optimization, and computer science. Key areas include: Lorentzian polynomials and their applications Polynomial capacity and optimization Sampling algorithms and combinatorial structures Representation theory and algebraic combinatorics Scientific Awards: Dirichlet Postdoctoral Fellowship (TU Berlin, 2020-2022) James H. Simons Fellowship (Simons Institute, UC Berkeley, Spring 2019) Previous Positions: Postdoc Fellowship, Institut Mittag-Leffler, Stockholm (Spring 2020) Postdoc, KTH, Stockholm (Fall 2019) Developer, Teacher Retirement System of Texas (2012-2014)