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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.

Anastasios KyrillidisView profile

Associate Professor

• Optimization for Machine Learning
• Convex and Non-convex Algorithms
• Large-Scale Optimization
• +3 more

Overview Anastasios Kyrillidis (Tasos) is a Noah Harding Associate Professor in the Department of Computer Science at Rice University, part of the George R. Brown School of Engineering. His research focuses on optimization for machine learning, convex/non-convex algorithms, and large-scale systems, with recent ventures into quantum computing and bioinformatics applications. He has held postdoctoral positions at IBM T.J. Watson Research Center and the University of Texas at Austin, following his PhD in Computer Science from EPFL (Switzerland). Education PhD, School of Computer and Communication Sciences, EPFL (2014) MSc, Electrical and Electronics Engineering, Technical University of Crete (2010) Diploma, Electrical and Electronics Engineering, Technical University of Crete (2008) Research Interests His work bridges optimization theory and practical machine learning systems. Key areas include: Designing efficient algorithms for high-dimensional problems Quantum computing applications in optimization Protein structure prediction via 3D Patterson maps (CrysFormer framework) Federated learning and distributed systems Neural network compression and acceleration Awards & Funding Recipient of NSF CAREER Award, Amazon Research Award, and Welch Foundation Grant. His research is supported by NSF, Amazon, Microsoft, and Intel collaborations. Notable recognitions include the Goldstine Fellowship (IBM) and Simons Foundation Postdoctoral Fellowship. Teaching Instructs advanced optimization courses (COMP 545, COMP 414) and Algorithmic Thinking (COMP 182). Focuses on connecting theory with real-world machine learning challenges. Labs & Teams Leads the OptimaLab at Rice, advancing research in optimization, quantum algorithms, and bioinformatics. Collaborates with interdisciplinary teams on projects like AI-OWLS and CrysFormer protein modeling.

• 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.

Javier Perez AlvaroView profile

Associate Professor

• Numerical Linear Algebra
• Matrix Analysis
• Numerical Analysis
• +8 more

Javier Perez Alvaro is an Associate Professor in the Department of Mathematical Sciences at the University of Montana, College of Humanities and Sciences. His research is centered on numerical linear algebra and matrix analysis, with applications in scientific computing and data science. He teaches courses such as Data Science Analytics, Numerical Analysis, and Linear Algebra. Research Interests: His primary focus lies in Numerical Linear Algebra , particularly Nonlinear Eigenvalue Problems , Matrix Polynomials , Linearizations , and Conditioning Analysis . He explores the theoretical and computational aspects of polynomial and rational eigenvalue problems, with applications in mechanical systems, fluid dynamics, and photonic crystals. The recent publications reflect a strong trend in structured linearizations, perturbation theory, and rational approximations for nonlinear eigenvalue problems. His work often involves developing and analyzing new families of linearizations (e.g., Fiedler-like, block-Kronecker) that preserve matrix structure and ensure numerical stability. The focus is on backward error analysis, eigenvector accuracy, and efficient solution methods for large-scale problems. Advising and Grants: He actively mentors undergraduate students in research projects related to Data Science and Machine Learning, including generative modeling, deep neural networks, and tiny machine learning. He is involved in the Master of Science in Data Science program. While specific grants are not listed, his collaborative work with researchers from institutions like KU Leuven, University of Manchester, and UC Santa Barbara indicates active research funding and partnerships. Labs and Teams: His research is conducted in collaboration with a broad network of mathematicians and computational scientists. Key collaborators include María C. Quintana, Maribel Bueno, Froilán M. Dopico, Paul Van Dooren, Karl Meerbergen, and Vanni Noferini. These collaborations span topics in structured linearizations, rational matrix functions, and numerical stability.

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.

• Parallel Computing
• Systems Theory
• Control Theory
• +5 more

Martin Köhler is a researcher at the Max Planck Institute for Dynamics of Complex Technical Systems, specializing in computational methods within systems and control theory. His work focuses on leveraging modern computer architectures for solving generalized eigenvalue problems and large-scale matrix equations efficiently. Research Interests: Parallel algorithms for system and control theory Generalized eigenvalue problems on advanced computing architectures High-performance computing for matrix equations Multicore and multi-GPU programming Contact: koehlerm@mpi-magdeburg.mpg.de

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.

Issmaïl El HallaouiView profile

Professor

• Operations Research
• Optimization
• Mathematical Modeling
• +5 more

Issmaïl El Hallaoui is a Full Professor in the Department of Mathematical and Industrial Engineering at Polytechnique Montréal. His research focuses on operations research, optimization, and mathematical modeling, with applications in transportation, vehicle routing, and software development. He is affiliated with the Institute for Data Valorization (IVADO) and the Research Group in Decision Analysis (GERAD). Operations Research and Management Science Combinatorial Optimization Online Optimization Vehicle Scheduling Transportation Logistics Software Development With over 116 publications, his work spans 2025 INFORMS Journal on Applied Analytics, 2024 Transportation Research Part E, and 2023 European Journal of Operational Research. His recent projects include supply chain optimization for OCP, real-time dial-a-ride problem solving, and energy system modeling via game theory. Dr. El Hallaoui has supervised 11 PhD and 3 Master's students, including Bani, A., Er Raqabi, E. M., and Tahir, A. His teaching portfolio includes courses on Integer Programming (MTH6404) and Real-time Optimization (MTH6601).

• Algorithmic development
• Stochastic modelling
• Risk/reliability analysis
• +9 more

Dr. Tanmoy Chatterjee is a Lecturer in Resilient Design at the University of Surrey's School of Mechanical Engineering Sciences, affiliated with the Surrey Institute for People-Centred AI and the Institute for Sustainability. Previously, he was a postdoctoral researcher at Swansea University (2018–2022), funded by the EPSRC 'DigiTwin' grant. His research focuses on algorithmic development, stochastic modeling, and machine learning for structural systems, with applications in nonlinear dynamics, metamaterials, and energy harvesting. Education: PhD in Civil Engineering (2014–2018), Indian Institute of Technology Roorkee Masters in Structural Engineering (2011–2013) Key Research Areas: Resilient design of structures under uncertainty Data-driven discovery of nonlinear dynamics Metamaterials for wave propagation control Machine learning for uncertainty quantification Publications: Recent work includes stochastic model updating, Bayesian inference for aeroelastic systems, and PINN-based reliability analysis. His 2025 paper on nonlinear structural joints introduced a novel framework combining experimental data with deep learning. Awards: Recipient of CSIR and Ministry of Education scholarships for his master's and PhD. Supervision: Co-supervised PhD students James Smith (University of Surrey) and Pushpa Pandey (Swansea University). Open to exceptional candidates for funded PhD projects in AI-driven engineering and resilient design. Labs/Teams: Active in Surrey's Centre for Engineering Materials and collaborations with industry partners like Autodesk and UK Atomic Energy Authority.

Amos LawlessView profile

Professor

• Data Assimilation
• Numerical Weather Prediction
• Meteorology
• +4 more

Professor Amos Lawless is a leading academic in the Department of Meteorology at the University of Reading, specializing in data assimilation and inverse problems. His research focuses on advancing numerical methods for meteorological and oceanographic modeling, particularly in variational data assimilation, error covariance analysis, and coupled atmosphere-ocean systems. He holds a Professorship position and contributes to the development of algorithms for improving forecasting accuracy in numerical weather prediction (NWP) systems. His work integrates computational mathematics, environmental science, and applied statistics to address challenges in climate modeling and predictive systems. Key research themes include the conditioning of data assimilation problems, model bias correction, and the application of optimization techniques to enhance forecast precision. His publications span prestigious journals like the Quarterly Journal of the Royal Meteorological Society and SIAM Journal on Applied Dynamical Systems, reflecting his expertise in both theoretical and applied aspects of data assimilation. Collaborations involve institutions such as the Met Office and international research groups, aiming to bridge the gap between computational methods and real-world environmental modeling challenges. Recent work emphasizes hybrid data assimilation approaches, cross-domain error correlations in coupled systems, and the use of randomized preconditioning to tackle large-scale computational problems. His contributions are pivotal in advancing the mathematical foundations of environmental forecasting systems, ensuring robustness and efficiency in modern NWP frameworks.

• 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 .

• Probability Theory
• Mathematical Physics
• Statistical Mechanics
• +4 more

Alessandra Occelli is an Associate Professor at the Université d'Angers, affiliated with the LAREMA research group (Laboratoire Angevin de Recherche en Mathématiques) in the Department of Mathematics. Her primary research focus is on Probability Theory, Mathematical Physics, and Statistical Mechanics, with a specialization in the KPZ universality class, interacting particle systems, and random matrix theory. She actively organizes and participates in international conferences, including the 2025 InterPlay workshop in Angers and the 2024 Mini workshop on KPZ and mixing times at Oberwolfach. Her academic journey includes organizing thematic semesters at the Centre Henri Lebesgue and serving as an invited speaker at prestigious events such as the Latin American and Caribbean Mathematics Congress (2024), the Italian Meeting on Probability and Mathematical Statistics (2024), and the Columbia University KPZ workshop (2024). She is also a key figure in the Probabilités, Physique Mathématique et Analyse (2PMA) seminar series at Université d'Angers. Her recent research trends emphasize the interplay between integrable probability and interacting particle systems, with notable contributions to Muttalib-Borodin processes, last passage percolation, and stochastic Burgers equations. Her articles explore both discrete and continuous models, often employing Riemann-Hilbert analysis and hydrodynamic techniques. Alessandra collaborates widely, with affiliations extending to institutions like the Simons Center for Geometry and Mathematical Physics (Stony Brook) and the Institut Henri Poincaré. Her work frequently bridges theoretical developments with applications in non-equilibrium statistical physics and universality phenomena.

Ivo DravinsView profile

Researcher

• Numerical Analysis
• Computational Mathematics
• Partial Differential Equations
• +3 more

Dr. Ivo Dravins is a PostDoc at the Chair of Numerical Analysis within the Faculty of Mathematics at Ruhr-Universität Bochum, working in Prof. Katharina Kormann's research group. He focuses on preconditioning techniques for implicit time-stepping algorithms, particularly within the PDExa project. His research spans numerical linear algebra, implicit Runge-Kutta methods, and PDE-constrained optimization. Research Interests: Preconditioning of large-scale linear systems Implicit time-stepping algorithms for PDEs High-order numerical methods Optimal control problems with constraints Numerical linear algebra applications Key Research Trends: His work emphasizes scalable preconditioning strategies for parallel computing environments, with a focus on achieving high-order accuracy in time integration. Recent efforts have addressed stage-parallel Runge-Kutta implementations and spectral analysis of preconditioned matrices in PDE-constrained optimization contexts. Labs/Teams: Member of Prof. Kormann's Numerical Analysis group, contributing to the PDExa project. Associated with the Kormann Group within the Faculty's Numerics division.

Dianne P. O'LearyView profile

Professor

• Scientific Computing
• Numerical Analysis
• Computational Science
• +6 more

Dianne P. O'Leary is a Professor affiliated with the Department of Computer Science at the University of Maryland. Her work focuses on scientific computing, numerical analysis, and computational science, with a particular emphasis on matrix factorizations, differential equations, and Monte Carlo methods. She authored the textbook *Scientific Computing with Case Studies* (SIAM Press, 2009), which integrates theoretical concepts with practical case studies across physics, engineering, epidemiology, and biology. Her research explores topics such as image deblurring, blind deconvolution, elastoplastic torsion, and iterative methods for solving large linear systems. The book includes nineteen case studies that illustrate algorithm design, mathematical modeling, and software development principles. Notably, the text emphasizes stability, error analysis, and MATLAB implementation grounded in machine arithmetic and memory management. While no explicit scientific awards or student advisees are mentioned in the provided texts, her contributions to computational education and algorithm development are central to her work. Supplementary materials include solution manuals, MATLAB code repositories, and detailed notes on topics like sensitivity analysis, computer memory management, and finite element methods. Her case studies often bridge theory and application, such as modeling epidemics, robot control systems, and structural engineering problems. The book’s structure reflects a commitment to hands-on learning, with units dedicated to dense matrix computations, optimization, Monte Carlo simulations, and sparse matrix techniques for partial differential equations.

• Numerical Linear Algebra
• Krylov Methods
• Matrix Functions
• +2 more

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.