جست‌وجوی استاد و پژوهشگر

Vedran Dunjkoمشاهده پروفایل

استاد

• Quantum Computing
• Quantum Machine Learning
• Quantum Artificial Intelligence
• +۷ مورد دیگر

Professor Vedran Dunjko is a faculty member at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, with affiliations to the Leiden Institute of Physics (LION). He leads the Applied Quantum Algorithms group and co-founded the Quantum@LIACS initiative, focusing on the intersection of quantum computing, machine learning, and artificial intelligence. His research interests include quantum machine learning, quantum-enhanced reinforcement learning, quantum heuristics, and the application of AI to quantum computing challenges. Dunjko's work bridges theoretical foundations with experimental implementations on near-term quantum devices, exploring both quantum advantages in learning and the use of classical AI for quantum system design. The recent publications show a strong trend toward proving quantum advantages in learning tasks, optimization, and topological data analysis, with publications in Nature , Nature Communications , and NeurIPS . Key themes include quantum policy gradients, quantum TDA, and reinforcement learning for quantum circuit optimization. ERC Consolidator Grant (2024) PNAS Cozzarelli Prize (2018) Editor’s Suggestion in Physical Review Letters (2014, 2018) Featured in Physics (American Physical Society) (2014, 2018) Dunjko advises several PhD candidates and postdocs, including Rahul Bandyopadhyay, Sofiene Jerbi, and Lea Trenkwalder. He has received competitive grants, most notably the ERC Consolidator Grant in 2024. His group fosters international collaborations with institutions across Europe and industry partners. The Applied Quantum Algorithms group and the Quantum@LIACS team combine theoretical investigations with practical implementations on quantum hardware, focusing on scalable quantum algorithms and AI-driven quantum discovery.

Matthias Schlottbomمشاهده پروفایل

دانشیار

• Numerical Analysis
• Computational Mathematics
• Radiative Transfer
• +۴ مورد دیگر

Matthias Schlottbom is an Associate Professor specializing in Mathematics of Computational Science, with a focus on numerical methods and their applications in physics, biology, and engineering. His research integrates advanced computational techniques with interdisciplinary problems, including radiative transfer, photonic crystals, and chemotaxis modeling. Research Interests: Schlottbom’s work spans numerical analysis, finite element methods, and machine learning. He develops high-order discretization schemes, iterative solvers for anisotropic transport, and mathematical frameworks for biological network formation. Publications: Recent articles highlight his contributions to accelerating radiative transfer simulations, extending component mode synthesis for Helmholtz equations, and analyzing diffusion limits in kinetic models. His work often bridges computational mathematics with practical applications in photonics and multiscale systems. Collaborations: He actively collaborates on datasets for optical simulations, radiative transfer algorithms, and photonic crystal modeling, contributing to open-access repositories like 4TU.Centre and Zenodo. Activities: Schlottbom has organized workshops such as the Kinetic Theory Workshop in the Netherlands and delivered keynotes on residual minimization and data-driven methods for transport equations. Scientific Awards: No specific awards or fellowships are mentioned in the provided materials. Advising & Grants: Details about students, advising roles, or grant funding are not included in the available data.

• Mathematics
• Differential Equations
• Galois Theory
• +۳ مورد دیگر

Prof. M. van der Put is a faculty member at the University of Groningen , specifically in the Department of Mathematics . His academic role is centered on research and teaching in advanced mathematical domains. Research interests include Differential Galois Theory , p-adic Differential Equations , and Valuation Theory . His work explores connections between differential equations and algebraic structures, with applications in positive characteristic and rigid geometry. The trends and fields of his recent articles (when dated) emphasize theoretical advancements in Algebraic Geometry , Group Theory , and Differential Algebra , focusing on iterative equations, Galois representations, and non-Archimedean geometries. Teaching responsibilities include Wiskunde 5 (complex function theory), as outlined in the course syllabus. His contact information is available via email at M.van.der.Put@math.rug.nl .

Michiel E. Hochstenbachمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• Ill-posed and Inverse Problems
• Numerical Analysis
• +۳ مورد دیگر

Michiel E. Hochstenbach is an Associate Professor at Eindhoven University of Technology (TU/e), affiliated with the EAISI High Tech Systems and EAISI Foundational initiatives. He holds positions in the Department of Mathematics within the School of Mathematics and Computer Science. His research focuses on numerical linear algebra, ill-posed problems, and computational methods. Hochstenbach earned his PhD from Utrecht University (2003) and held roles at Düsseldorf University and Case Western Reserve University before joining TU/e in 2006. His work spans numerical analysis, scientific computing, and software development for numerical algorithms. He has secured grants including an NSF (2004–2007) and NWO Vidi (2012–2017). His research group advises PhD students who have also received awards for their contributions. Key research areas include matrix computations, inverse problems, and optimization techniques. Recent publications address subspace methods, gradient algorithms, and matrix factorization innovations. He is an editorial board member of several journals and actively contributes to interdisciplinary computational science projects. Notable awards include the NWO Vidi Award (2011) recognizing his rapid advancements in matrix methods. His teaching includes courses on calculus, linear algebra, and mathematics fundamentals. Hochstenbach collaborates internationally, contributing to computational mathematics and sustainable development through algorithmic efficiency.

Yuliana Yu. Linkeمشاهده پروفایل

دانشیار

• Regression analysis
• Change-point problem
• Nonparametric Estimation
• +۳ مورد دیگر

Yuliana Yu. Linke is an Associate Professor at the Chair of Probability Theory and Mathematical Statistics of Novosibirsk State University and a Senior Researcher at the Laboratory of Applied Inverse Problems of the Sobolev Institute of Mathematics. She holds a Candidate of Science (Ph.D.) from the Sobolev Institute of Mathematics (2000) and a Doctor Habilitatus from Lomonosov Moscow State University (2024). Education: 1992-1998, Novosibirsk State University, Mathematical Department 1998-2000, Postgraduate Course, Chair of Probability Theory and Math. Statistics, NSU 2000, Candidate of Science (Ph.D.), Sobolev Institute of Mathematics 2024, Doctor Habilitatus (Dr.Habil), Lomonosov Moscow State University Her research focuses on regression analysis and the change-point problem. She has extensively contributed to nonparametric estimation techniques, particularly kernel methods and asymptotic statistics, addressing challenges in stochastic processes and heteroscedastic models. Her publications over the past 15 years highlight advancements in kernel-type estimators for regression models, emphasizing uniform consistency, insensitivity to design correlation, and asymptotic normality under non-identical distributions. These works span theoretical developments and applications to stochastic processes and random fields. Linke has also taught courses for the Department of Natural Sciences at Novosibirsk State University, with teaching materials available in Russian.

Martijn Anthonissenمشاهده پروفایل

استادیار

• Computational Illumination Optics
• Numerical Mathematics
• Scientific Computing
• +۵ مورد دیگر

Martijn Anthonissen is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). He is a core member of the Computational Illumination Optics group, focusing on numerical methods for optical system design, freeform optics, and inverse problems in illumination engineering. His work combines advanced mathematical models with computational techniques to develop efficient optical systems for applications like LED lighting, automotive headlights, and laser technology. Education: Martijn earned his mathematics degree at TU/e and participated in the Japan Prizewinners Program, working at Hitachi in Tokyo. He completed his PhD on numerical combustion before transitioning to computational optics. He has held visiting positions at institutions worldwide, including Yale University and the Weierstrass Institute. Research: His research emphasizes solving Monge-Ampère equations and generated Jacobian equations to design freeform optical surfaces. Key areas include hyperbolic PDEs, Liouville's equation in geometrical optics, and Lie algebraic methods for aberration analysis. Collaborations with industry partners aim to translate theoretical advances into practical optical solutions. Teaching: He teaches courses in numerical linear algebra, mathematical modeling, and scientific computing, contributing to TU/e's teacher training programs. His educational materials, including YouTube videos on numerical methods, are widely used. Publications: Over 50 peer-reviewed articles since 2013, including high-impact work in Journal of the Optical Society of America , Journal of Computational Physics , and Optics Express . Current projects include neural network approaches for PDEs and optimization of gradient-index lenses. Labs/Teams: Leads the Computational Illumination Optics group, which has active PhD and industrial research programs. The group's interdisciplinary approach bridges mathematics, physics, and engineering.

Patrick Forréمشاهده پروفایل

استادیار

• Causal Inference
• Machine Learning Theory
• Information Theory
• +۴ مورد دیگر

Patrick Forré is an Assistant Professor and Lab Manager of the AI4Science Lab at the Informatics Institute, Faculty of Science, University of Amsterdam. His work bridges theoretical machine learning and scientific applications, fostering interdisciplinary collaboration across informatics, mathematics, ecology, chemistry, physics, biology, and astrophysics. His research centers on mathematical foundations of machine learning including causal inference, graphical models, information theory, conditional independence structures, and geometric deep learning. He specializes in applying these techniques to scientific data problems, particularly in electro-catalysis and nitrogen fixation, where machine learning enhances molecular simulations and quantum chemical modeling. His theoretical work addresses non-linear structural causal models with cycles and latent confounders. The AI4Science Lab under his management focuses on detecting hidden patterns in scientific data through projects like electrode-electrolyte interface modeling, nitrogen-fixing coordination complexes analysis, and classical DFT neural approximations. Located in LAB42 Building at Amsterdam Science Park, the lab connects diverse scientific disciplines through machine learning innovation while organizing colloquia, workshops, and PhD defenses.

• Convex Optimization
• Quantum Computing
• Quantum Information Theory
• +۷ مورد دیگر

Sander Gribling is an Assistant Professor in the Department of Econometrics and Operations Research at Tilburg School of Economics and Management, Tilburg University. His research bridges convex optimization, quantum computing, and quantum information theory, with a focus on quantum algorithms for classical optimization problems, semidefinite and polynomial optimization, and quantum graph parameters. PhD in Optimization and Quantum Information, Tilburg University (2019) MSc and BSc in Applied Mathematics, TU Delft His research interests include: Quantum algorithms for linear programming, semidefinite programming, and matrix scaling Polynomial and sum-of-squares optimization Quantum query complexity and the polynomial method Applications of optimization to quantum information and noncommutative graphs His recent publications reveal a strong focus on quantum algorithms, theoretical foundations of optimization, and sampling methods. Key trends include the development of optimal quantum linear solvers, complexity analysis of the Lasserre hierarchy, and the interplay between Grothendieck inequalities and quantum query models. Notable scientific roles: Associate Editor, INFORMS Journal on Computing Sander Gribling has advised or collaborated with numerous researchers including Simon Apers, Joran van Apeldoorn, Monique Laurent, and Ronald de Wolf. He has received no explicitly listed awards in the provided text, but his publications in high-impact journals such as Nature Reviews Physics and Quantum indicate strong recognition. He is involved in the Operations Research research group and contributes to advancing theoretical and applied quantum optimization.

• Control Systems
• Mechatronics
• Machine Learning
• +۷ مورد دیگر

T.A.E. Oomen is a Professor in the Department of Mechanical Engineering, actively engaged in research related to control systems and mechatronics. His academic contributions span interdisciplinary domains, integrating theoretical and applied approaches. Research Interests: His work focuses on Control Systems Mechatronics Machine Learning Dynamic Systems Noise Optimization Motion Control . Key themes include predictive repetitive control, inverse system identification, and the integration of neural networks in mechatronic applications. Recent Publications analyze challenges in precision engineering (e.g., laser frequency control in astrophysics, interventional X-ray imaging) and methodological advancements in sampled-data control and Bayesian modeling techniques. These works emphasize performance optimization through data-driven strategies. Collaborative Affiliation: He is associated with the team led by Jan-Willem van Wingerden , contributing to cutting-edge research in control systems and multidisciplinary engineering problems.

Maike Meierمشاهده پروفایل

استادیار

• Applied Mathematics
• Numerical Linear Algebra
• Linear Inverse Problems

Maike Meier serves as an Assistant Professor within the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen's Faculty of Science and Engineering. Her role centers on advancing computational mathematics research and education within the university's academic framework. Her research expertise spans critical domains of applied mathematics: Applied Mathematics Numerical Linear Algebra Linear Inverse Problems These areas focus on developing robust algorithms for large-scale linear systems and inverse modeling, with applications in scientific computing and engineering. Dr. Meier maintains active contact through her office in room 5161.0451 of the Bernoulliborg building (Nijenborgh 9, 9747 AG Groningen) and via email at m.meier@rug.nl .

Katharina Prokschمشاهده پروفایل

پژوهشگر

• Statistics
• Biomedical Imaging
• Data Analysis
• +۴ مورد دیگر

Katharina Proksch is a researcher specializing in advanced statistical methodologies and their applications across diverse scientific domains. Her work focuses on regression models, simultaneous inference, and transforms, with notable contributions to nonparametric testing, inverse problems, and quantitative imaging techniques. She explores interdisciplinary applications in biomedical imaging, astrophysics, and biomechanical analysis, emphasizing rigorous statistical frameworks to ensure methodological robustness. Her research interests include developing statistical tools for super-resolution microscopy, fatigue detection through biomechanical data, and analyzing complex datasets from fluorescence microscopy and X-ray astronomy. Proksch's methodologies often address challenges in handling measurement errors, outliers, and multiscale data structures. Key contributions include a two-sample test using Wilcoxon rank sums, quantitative super-resolution microscopy techniques with statistical guarantees, and fatigue detection algorithms leveraging sequential testing. Her work bridges theoretical statistics with practical applications in health monitoring and astrophysical phenomena modeling.

• Mathematical Programming
• Correspondence Analysis
• Hierarchical Clustering
• +۷ مورد دیگر

Rick Willemsen is a Lecturer in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. His academic work lies at the intersection of mathematical programming, statistical data analysis, and computational optimization. Research Interests: His primary research focuses on correspondence analysis, hierarchical clustering, and dimension reduction techniques. He employs mathematical and mixed integer programming to develop exact and inverse solutions for statistical problems involving categorical data and contingency tables. His methodological contributions aim to improve the accuracy and interpretability of clustering and dimensionality reduction methods in econometric and data science applications. Publication Trends: Over recent years, Rick has published on exact algorithms for hierarchical clustering, uniqueness conditions in correspondence analysis, and novel optimization approaches for inverse problems. His research consistently integrates theoretical rigor with practical computational implementation, as evidenced by both journal articles and software outputs. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no public information available on students advised or grants received. However, his recent completion of a doctoral thesis suggests he is in the early stage of his independent academic career. Labs and Research Teams: Rick Willemsen collaborates closely with researchers such as Michel van de Velden and Willem van den Heuvel, likely within the Data Science or Quantitative Methods group at Erasmus School of Economics. His work appears to be part of a broader effort in developing optimization-based tools for statistical data analysis.

• Inverse Problems
• Variational Methods
• Total Variation Regularization
• +۷ مورد دیگر

Jose A. Iglesias Martínez is an Assistant Professor in the Department of Applied Mathematics at the University of Twente, affiliated with the Mathematics of Imaging and AI (MIA) group within the Faculty of Electrical Engineering, Mathematics and Computer Science. He conducts research at the intersection of variational analysis, inverse problems, shape analysis, and machine learning, with a geometric perspective on regularization and optimization. University: University of Twente School: Faculty of Electrical Engineering, Mathematics and Computer Science Department: Department of Applied Mathematics Research Group: Mathematics of Imaging & AI (MIA) His research focuses on analytic and variational methods for inverse problems in imaging, particularly involving total variation and related regularization models. The geometric structure of function domains and convex geometry in minimization problems plays a central role in his work. He applies these methods to imaging, shape analysis, and machine learning, with strong mathematical foundations in PDEs, functional analysis, and optimization. The recent publications reflect a consistent focus on variational regularization, extremal point analysis, sparse optimization, and PDE-constrained problems. Key themes include total variation, TGV, nonlocal perimeters, and shape optimization, with applications in image denoising, optical flow, and fluid dynamics. His work spans both theoretical convergence analysis and algorithmic development, often with computational implementations. Scientific Awards and Recognition: Member of the Editorial Board, Numerical Functional Analysis and Optimization (since August 2023) He actively teaches in applied mathematics, including courses such as Analysis 3, Mathematics behind Data-Driven Methods, and interdisciplinary topics like Art, Mathematics and Technology. He has supervised tutorials and projects in calculus, linear algebra, and modeling. His academic training includes dual MSc degrees in Mathematics and Telecommunication Engineering from Madrid, a PhD (2015) and habilitation (2021) in Mathematics from the University of Vienna, followed by postdoctoral work at the Radon Institute in Linz. He has no listed students yet, but is actively publishing and contributing to the mathematical imaging community. He is involved in editorial work and maintains a strong publication record in top-tier applied mathematics journals.

• Geotechnical Engineering
• Computational Geomechanics
• Soil Mechanics
• +۵ مورد دیگر

Michael A. Hicks is a Professor in the Department of Geo-engineering within the Faculty of Civil Engineering & Geosciences at Delft University of Technology (TU Delft). His research focuses on computational geomechanics, soil mechanics, and probabilistic analysis of geotechnical structures. His primary research interests include: Material Point Method (MPM) and computational techniques for geomechanics Slope stability analysis and reliability assessment Soil heterogeneity and spatial variability Earthquake engineering and seismic response analysis Probabilistic methods in geotechnical engineering Professor Hicks' recent work demonstrates a strong trend toward integrating advanced computational methods with geotechnical engineering challenges. His research increasingly incorporates machine learning applications for site characterization while maintaining a strong foundation in fundamental soil mechanics. The publications show consistent focus on improving numerical methods for simulating complex geomechanical behavior, particularly related to slope failures, soil heterogeneity, and earthquake response. His scientific contributions include editorial roles for major geotechnical journals: Editorial board member for Géotechnique (2019) Editorial board member for Georisk: assessment and management of risk for engineered systems and geohazards (2019) Editorial board member for Computers and Geotechnics (2019) Professor Hicks has been actively involved in public engagement, with media coverage on topics including earthquake damage in Groningen (2018), dike safety (2017), and flood risk reduction in the Netherlands (2017). His research group at TU Delft focuses on developing and applying advanced computational methods to solve complex geotechnical engineering problems, with particular emphasis on reliability analysis and uncertainty quantification.

Harald van Brummelenمشاهده پروفایل

استاد

• Computational Science and Engineering
• Multiscale Flow Problems
• Fluid-Structure Interaction
• +۸ مورد دیگر

Prof. Harald van Brummelen is a Full Professor and group leader of the Multiscale Engineering Fluid Dynamics Group at Eindhoven University of Technology (TU/e) in the Netherlands. His academic career spans appointments at Delft University of Technology (2001-2009) and a secondary role in the Department of Mathematics and Computer Science at TU/e. Van Brummelen specializes in advanced numerical techniques for multiscale flow problems, with applications in semiconductor lithography and inkjet printing. Academic Background: MSc in Mechanical Engineering (1997), Twente University of Technology PhD (2002), University of Amsterdam (Mathematical Institute) Research Interests: Fluid-Structure Interaction Free-Boundary and Interface Problems Phase-Field Models and Isogeometric Analysis Kinetic Models and Boltzmann Equation Error Estimation and Adaptivity Immersed Methods and Reduced-Order Modeling Scientific Contributions: Developed entropy-stable discretization techniques Led projects on multiscale fluid dynamics Advancements in preconditioning immersed methods Recipient of J.-L. Lions Award and Bill Morton Prize Supervision: Mentored doctoral candidates (Ahmed Aissa Berraies, Can Bozdoğan) Supervised master's theses on topics like flow instability and turbine design Current Projects: FIP 2.0: Complex Fluids on Complex Substrates (2020-2026)