جست‌وجوی استاد و پژوهشگر

• Numerical Linear Algebra
• Model Order Reduction
• Matrix Equations
• +۵ مورد دیگر

Dr. Jens Saak is a leading researcher and team leader at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. He heads the Computational Methods in Systems and Control Theory research group and has held several leadership positions at the institute since 2010, including Head of the Scientific Computing Team (2010-2020), Head of the Matrix Equations Team (2013-2020), and currently serves as Head of the Data, Infrastructure, Software & Computing Team since 2021. Dr. Saak earned his undergraduate degree in Technomathematik from the University of Bremen (1996-2003), completed his PhD at TU Chemnitz (2003-2009), and held a postdoctoral position there (2009-2010) before joining the Max Planck Institute. His research spans computational mathematics with a focus on large-scale sparse matrix equations , model order reduction , and optimal control of complex systems. He specializes in developing efficient numerical algorithms for Lyapunov and Riccati equations, with applications in mechanical engineering (particularly machine tools), fluid dynamics, and thermal systems. His work bridges theoretical numerical analysis with practical high-performance computing implementations. Analysis of his recent publications reveals a clear trajectory toward energy-efficient computing solutions, sustainable research software practices, and increasingly sophisticated model reduction techniques for complex coupled systems. His work demonstrates a strong emphasis on practical implementations with industrial applications, particularly in mechanical engineering contexts. Dr. Saak has been instrumental in developing and maintaining research software infrastructure, with a growing focus on sustainability and reproducibility in computational science. His leadership extends to collaborative projects across the German mathematical community, particularly in research data management initiatives. His team actively contributes to open-source scientific computing tools, with significant work on the M-M.E.S.S. (Matrix Equations, Sparse Solvers) toolbox, which provides implementations of modern algorithms for matrix equations and model order reduction.

Olga Mulaمشاهده پروفایل

پژوهشگر

• Optimal Transport
• Wasserstein Spaces
• Model Reduction
• +۴ مورد دیگر

Olga Mula is a researcher at Eindhoven University of Technology (TU Eindhoven) in the Netherlands specializing in optimal transport theory, Wasserstein spaces, and model reduction techniques for partial differential equations. Her work bridges theoretical mathematics with practical applications in state estimation and inverse problems. Her research interests focus on Optimal Transport , Wasserstein Spaces , Model Reduction , and Numerical Analysis of PDEs . She develops algorithms for state estimation in metric spaces, particularly focusing on the Wasserstein space of probability measures. Her work includes developing reduced models using barycentric approximation, analyzing convergence properties, and addressing challenges in sensor placement for optimal data acquisition. Her recent publications demonstrate significant contributions to understanding how to build efficient reduced models in Wasserstein spaces for both forward and inverse problems. She has developed theoretical frameworks for state estimation algorithms, analyzed their performance in terms of Kolmogorov widths, and created practical implementations using sparse Wasserstein barycenters. Her work spans pure mathematical theory to applications in image processing and PDE-constrained optimization. Her scientific contributions include: Development of piecewise-affine algorithms for state estimation Nonlinear model reduction on metric spaces for conservative PDEs Sparse approximation using Wasserstein barycenters Applications to shape reconstruction and line completion Theoretical analysis of approximation rates in Wasserstein spaces She collaborates extensively with leading researchers in the field including Cohen, Dahmen, Feydy, and Rai. Her work demonstrates both theoretical depth and practical relevance, connecting abstract mathematical concepts to real-world problems in data assimilation and inverse modeling.

Nitis Mukhopadhyayمشاهده پروفایل

استاد

• Sequential Analysis
• Statistical Inference
• Confidence Interval Estimation
• +۵ مورد دیگر

Nitis Mukhopadhyay is a Professor in the Department of Statistics at the University of Connecticut, with extensive research contributions in sequential analysis and statistical inference. His academic work focuses on developing innovative methodologies for confidence interval and point estimation problems, particularly in sequential and multistage sampling frameworks. His research has significant applications across various domains including environmental science, clinical trials, and survey methodology. Professor Mukhopadhyay's research interests center on sequential analysis, with particular emphasis on confidence interval estimation, point estimation, survey sampling, environmental sampling, clinical trials, and multivariate data analysis. His work bridges theoretical statistical developments with practical applications, developing methodologies that address real-world data challenges while maintaining rigorous mathematical foundations. His research often involves complex statistical problems requiring sophisticated solutions that balance accuracy with efficiency. Analysis of Professor Mukhopadhyay's recent publications reveals a strong focus on advanced sequential methodologies, particularly in minimum risk point estimation, fixed-width confidence interval problems, and multistage sampling strategies. His work demonstrates increasing attention to big data contexts and computational implementations, while maintaining rigorous theoretical foundations. The publications show consistent development of second-order asymptotic properties and practical implementations across various parametric families including normal, exponential, and gamma distributions. Professor Mukhopadhyay maintains active correspondence with the statistical community through his office at AUST 331 on the Storrs Campus of the University of Connecticut. His work continues to influence methodological developments in sequential analysis and statistical inference, with applications spanning environmental monitoring, clinical research, and various scientific domains requiring sophisticated sampling strategies.

Rubén Sevillaمشاهده پروفایل

استاد

• Computational Engineering
• High-order Finite Elements
• Numerical Methods
• +۵ مورد دیگر

Professor Rubén Sevilla is a Computational Engineering academic at the Faculty of Science and Engineering , Swansea University . He holds a Chair in Civil Engineering and has held leadership roles including President of the UK Association for Computational Mechanics and Chief Editor of the European Journal of Computational Mechanics . PhD in Civil Engineering (2009), UPC-BarcelonaTech Postdoctoral Researcher (2009-2012), Zienkiewicz Centre for Computational Engineering Lecturer (2012), Senior Lecturer (2015), Associate Professor (2016), Full Professor (2021) Research Interests focus on high-order numerical methods for engineering problems, including: Face-Centred Finite Volume Methods (FCFV) Hybridizable Discontinuous Galerkin (HDG) NURBS-Enhanced Finite Element Methods (NEFEM) Reduced Order Modeling Machine Learning for Mesh Optimization Computational Fluid and Electromagnetic Dynamics Geometrically Parametrized Problems Article Trends show a focus on hybrid numerical methods for fluid-structure interaction, geometrically accurate mesh generation using NURBS, machine learning integration for flow simulations, and parametric modeling of complex systems. His work bridges CAD and FEM through NEFEM while advancing reduced-order models for real-time engineering applications. Scientific Awards include: European Association for Computational Methods in Applied Sciences award Spanish Association for Computational Methods in Engineering award Birkhauser-Verlag Best Thesis award (Spain/Europe) EMERALD award SIAM award Welsh Government recognition Teaching & Supervision spans modules like Finite Element Computational Analysis and Problem Solving with MATLAB . He supervises PhD students in computational mechanics and co-led the International MSc in Computational Mechanics since 2012. Grants & Projects include: EPSRC-funded "Feature-Independent Mesh Generation" (2020-2023, £427,929) ELEMENT - Exascale Mesh Network (2020-2021, £245,611) EPSRC Solar Absorber Project (2017-2020, £315,556) H2020 Advanced Model Reduction (2015-2019, €2,080,164.96)

• Statistics
• Actuarial Science
• Markov chain Monte Carlo methods
• +۴ مورد دیگر

Iliopoulos Georgios is a Professor and Department Chair at the Department of Statistics and Actuarial Science, School of Finance and Statistics, University of Piraeus. He has held this position since 2015, following his progression from Assistant Professor (2003-2010) to Associate Professor (2010-2015) at the same institution. Education: 1993: B.A. in Mathematics, Department of Mathematics, University of Patras 1999: PhD in Statistics, Department of Mathematics, University of Patras Iliopoulos specializes in statistical theory and methodology, with particular expertise in Markov chain Monte Carlo methods, Statistical Decision Theory, and Scale parameter estimation. His research focuses on accurate inference under censorship and constrained inference arrangement, addressing fundamental challenges in statistical analysis of complex data structures. His work bridges theoretical statistics with practical applications in reliability analysis and survival analysis. His publication record shows consistent contributions to exact statistical inference methods, particularly for censored data and complex distributions like Laplace and Gamma. His research demonstrates expertise in both parametric and semiparametric approaches, with significant work on variance reduction techniques in computational statistics and solutions to the label switching problem in Bayesian mixture models. Iliopoulos teaches undergraduate courses including Linear Algebra, Statistics II: Hypothesis Testing, and Special Topics in Statistics (Bayesian Statistics), as well as the postgraduate course Computational Statistical Techniques for the Master of Science in Applied Statistics.

• Computational Methods
• Numerical Analysis
• Control Theory
• +۹ مورد دیگر

Boris Kramer is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California San Diego , affiliated with the Jacobs School of Engineering . He leads research in computational methods for control , optimization , and uncertainty quantification of complex systems, with applications in space weather modeling , systems biology , and soft robotics . Center for Extreme Events Research (CEER) Center for Computational Mathematics (CCoM) Air Force Center of Excellence Multi-Fidelity Modeling His work focuses on reduced-order modeling (ROM) and data-driven methods that preserve physical structures (e.g., energy conservation in Hamiltonian systems). Recent projects include collaborations with Samsung Electronics for semiconductor manufacturing optimization and leadership in DOE's PSAAP IV program for radiation resilience modeling. Kramer's research has been featured in Nature Computational Science and Science News , with grants from NSF , DoD , and AFOSR . His group has advised students like Opal Issan (published in Journal of Computational Physics) and Nate Linden (Nature Communications). Scientific awards include: NSF CAREER Award (2022) DoD Newton Award for Transformative Ideas (2020) Outstanding PhD Student Award (2024-2025, UCSD MAE) MURI funding for Digital Twins (2023) Outreach efforts include participation in the Barrio Logan Science & Art Expo and the Southeast San Diego STEM Ecosystem , emphasizing science communication for K-12 audiences.

Kapil Ahujaمشاهده پروفایل

استاد

• Machine Learning
• Network Science
• Computational Optimization
• +۴ مورد دیگر

Prof. Kapil Ahuja is a Full Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Indore (IIT Indore), where he heads the Mathematics of Data Science and Simulation (MODSS) research lab. After completing dual Master's degrees and a Ph.D. from Virginia Tech (USA) followed by postdoctoral work at the Max Planck Institute in Germany, he has held visiting positions at UT Austin, IMT Atlantique, Sandia National Labs, TU Dresden, and TU Braunschweig. His administrative roles include founding Dean of International Affairs and former Head of Computer Science & Engineering at IIT Indore. Education: Ph.D. in Mathematics, Virginia Tech (2011) M.S. in Mathematics, Virginia Tech (2009) M.S. in Computer Science, Virginia Tech (2007) B.Tech. in Mechanical Engineering, IIT (BHU) Varanasi (2001) Research Focus: Prof. Ahuja's work bridges theoretical advances with real-world applications, emphasizing machine learning algorithms for plant/cancer studies, game-theoretic poverty reduction models, exascale climate modeling solvers, and drone trajectory optimization. His interdisciplinary approach integrates numerical linear algebra with network science to solve complex systems problems across healthcare, agriculture, and climate science, supported by 4.85 Crores INR in external funding. Publication Trends: Recent work demonstrates growing emphasis on AI-driven optimization for physical systems (drones, climate models) and biomedical applications (cancer classification). His publications increasingly feature cross-disciplinary collaborations between computer science, biology, and economics, with notable contributions in explainable AI for healthcare and resource allocation algorithms for social networks. Scientific Recognition: National Teacher's Award (2024) from the President of India Five-time recipient of IIT Indore's Best Teacher Award (2013-2023) Best Poster Award at International Workshop on Game Theory & Networks (2019) Steeneck Graduate Research Fellowship (Virginia Tech, 2011) Multiple SIAM travel awards for international conferences Mentorship & Service: Prof. Ahuja has graduated 5 Ph.D. and 4 M.S. (Research) students while mentoring 75 B.Tech. projects. He serves as Associate Editor for Applied Intelligence Journal (Springer Nature) and Knowledge and Information Systems, organizes international conferences, and reviews for 35+ academic sources. His administrative leadership significantly expanded IIT Indore's global partnerships through the Research Park initiative. Research Infrastructure: The MODSS lab maintains active collaborations with Oak Ridge National Lab, Sandia National Labs, and European institutions. Current projects include AI-optimized drone swarms for agricultural monitoring and game-theoretic models for poverty intervention, utilizing high-performance computing resources for large-scale simulations.

• Model Order Reduction
• Nonlinear Model Order Reduction
• Parametric Model Order Reduction
• +۲ مورد دیگر

Nicodemus Banagaaya is a Researcher in the Computational Methods in Systems and Control Theory group at the Max Planck Institute for Dynamics of Complex Technical Systems, based in office S1.08. His work bridges computational mathematics and engineering applications through advanced model reduction techniques. His research specializes in developing efficient model order reduction frameworks for complex systems, with particular emphasis on: Nanoelectronics circuit simulation Nonlinear dynamical systems Parametric dependency handling Differential-algebraic equation (DAE) systems High-fidelity model compression Dr. Banagaaya contributes to the institute's mission of advancing computational methods for complex technical systems, focusing on mathematical rigor and practical engineering implementation within his research group.

Yao Yueمشاهده پروفایل

پژوهشگر

• Model Order Reduction
• Design Optimization
• Krylov Methods
• +۷ مورد دیگر

Yao Yue is a postdoctoral researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Their work focuses on computational methods in systems and control theory, including parametric model order reduction, design optimization, Krylov methods, and fast computation techniques for Simulated Moving Bed (SMB) models and vibration/structure simulations. Education : Bachelor of Electronic Science and Technology, Harbin Engineering University (2001–2005) Master of Integrated Circuit Design, Tsinghua University (2005–2008) PhD in Engineering (Applied Mathematics, Numerical Approximation, Linear Algebra) at KU Leuven, Belgium (2008–2012) Research Interests span both applied mathematics and philosophical foundations of cognition. In computational methods, they work on optimizing numerical algorithms and system modeling. In philosophy, they explore axiomatic frameworks for cognitive dispositions, negation tendencies, and the epistemological limits of logic and empirical observation. Additional Activities include maintaining a personal homepage where they develop philosophical works like Reconstruction of Philosophy , discussing topics such as multiverse theory, consciousness, and the role of negation in knowledge formation.

• Computational Fluid Dynamics
• Structural Dynamics
• Aerodynamics
• +۴ مورد دیگر

Haedong Kim is a Professor at Sejong University's Department of Aerospace Engineering. With over two decades of research activity, his work spans computational fluid dynamics, structural dynamics, and aerospace systems, focusing on advanced modeling techniques and computational efficiency. Research areas include reduced-order modeling, rotor blade analysis, and aerodynamic studies. His collaborations extend to international institutions, emphasizing composite materials and nonlinear structural behavior. Recent publications highlight applications in CFD , satellite orbit determination , and nonlinear structural dynamics . Key trends involve model-order reduction and computational efficiency in aerospace systems.

Julia Brunkenمشاهده پروفایل

پژوهشگر

• Transport-dominated problems
• Kinetic equations
• Hierarchical model reduction
• +۲ مورد دیگر

Julia Brunken is a Research Associate at the Institute for Analysis and Numerics (Department of Mathematics and Computer Science, University of Münster). She earned her PhD in Mathematics (Dr. rer. nat.) in 2023 under supervision of Prof. Dr. Mario Ohlberger and Dr. Kathrin Smetana, following an MSc (2015) and BSc (2013) in Mathematics with Physics minors. Research Focus: Stable Petrov-Galerkin discretizations for transport-dominated problems (linear transport equations, kinetic Fokker-Planck equations), hierarchical model reduction, reduced basis methods, and space-time variational formulations. Her work emphasizes uniform inf-sup stability and parameterized systems. Teaching: Collaborated on practical courses in nonlinear modeling (2020/21, 2016, 2015) and numerical methods (2016/17, 2015/16) at University of Münster. Projects: Participated in GlioMaTh (2016–2019), a federally funded project on glioma therapy modeling.

William J. Bowmanمشاهده پروفایل

استادیار

• Secure and Verified Compilation
• Dependently Typed Programming
• Verification
• +۴ مورد دیگر

William J. Bowman is an Assistant Professor in the Department of Computer Science at the University of British Columbia. He focuses on secure and verified compilation, dependently typed programming, and meta-programming systems. His work bridges high-level language design with low-level code generation to maintain correctness and security invariants throughout compilation. Education: PhD in Computer Science from Northeastern University Research spans type-preserving compilation, including: Typed closure conversion for dependently typed languages Allocation-aware type universes Hybrid embedding techniques Secure interoperability via multi-language semantics Recent publications examine: Heap allocation modeling through type universes Flat closure representations WebAssembly extensions with indexed types Control-effect semantics