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Wojciech L. GolikView profile

Professor

• Numerical Analysis
• Computational Mathematics
• Computational Electromagnetics
• +3 more

Dr. Wojciech L. Golik serves as Professor of Mathematics and Assistant Dean for Mathematical and Natural Sciences at Lindenwood University's College of Science, Technology, and Health. With a distinguished academic career spanning over three decades, he holds leadership responsibilities while maintaining active research and teaching commitments. His educational background includes a Master of Science in Mechanical Engineering from Poznan Technological University (1982), followed by an M.S. (1985) and Ph.D. (1988) in Mathematics from New Mexico State University. His doctoral dissertation focused on Convergence of the Boundary Integral Methods in Numerical Solutions of Fourier Problems . Dr. Golik's research centers on advanced computational methodologies, with particular expertise in numerical analysis, computational electromagnetics, and data science applications. His work demonstrates sophisticated integration of wavelet theory with electromagnetic modeling, developing innovative algorithms for solving complex boundary value problems and integral equations. The publication timeline reveals sustained contributions to computational mathematics, with recent expansion into data science and interdisciplinary applications. His 2013 Polish-language publication Siedemdziesiąt Trzy indicates broader intellectual interests beyond pure mathematics. Teaching responsibilities encompass foundational and advanced mathematics courses including Calculus sequences, Differential Equations, Linear Algebra, Numerical Analysis, and specialized topics like Computational Electromagnetics and History of Mathematics. His extensive course portfolio reflects deep expertise across both theoretical and applied mathematics domains. Professional activities include significant administrative leadership as Assistant Dean, though specific grant funding details aren't documented in available materials. His departmental involvement supports multiple undergraduate and graduate programs including Mathematics (BA/BS), Data Science, and Actuarial Science tracks within the College of Science, Technology, and Health.

Carola Bibiane SchönliebView profile

Professor

• Image Analysis
• Machine Learning
• Applied Mathematics
• +4 more

Professor Carola Bibiane Schönlieb is a Professor of Applied Mathematics at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge. She leads the Cambridge Image Analysis (CIA) group and serves as Director of the Cantab Capital Institute for the Mathematics of Information (CCIMI) and the EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging (CMIH). She is a Fellow of Jesus College, Cambridge, and co-Chair of the Cambridge Centre for Data Driven Discovery (C2D3). Her research focuses on variational methods, partial differential equations, and machine learning for image analysis, with applications in biomedical imaging, art restoration, and interdisciplinary collaborations with clinicians, biologists, physicists, and engineers. Career: Since 2018, Professor at DAMTP; Reader (2015–2018); Lecturer (2010–2015); Postdoc (2009–2010); Research Assistant roles in Austria and Germany (2002–2009). Education: PhD in Mathematics (2009, University of Cambridge); Master’s in Mathematics (2004, University of Salzburg). Research Interests: Image analysis, inverse problems, biomedical imaging, machine learning, and interdisciplinary applications in medicine, biology, and art. Awards: Wolfson Fellowship (2020), Calderón Prize (2019), Philip Leverhulme Prize (2017), Whitehead Prize (2016), and others. Labs/Teams: Leads CIA group and directs CCIMI and CMIH, collaborating with institutions globally on projects like Alzheimer’s disease diagnosis and medical imaging technologies. Her work bridges mathematical theory and practical applications, driving advancements in imaging science through innovative methodologies and interdisciplinary partnerships.

Scott MacLachlanView profile

Professor

• Computational Applied Mathematics
• Scientific Computation
• Multigrid Methods
• +3 more

Scott MacLachlan is a Professor of Mathematics at Memorial University of Newfoundland. He holds a B.Sc. (Hon.) from the University of British Columbia and a Ph.D. in Applied Mathematics from the University of Colorado at Boulder. Previously, he served as Associate Professor at Tufts University. His research focuses on computational applied mathematics and scientific computation, particularly multigrid methods, finite-element discretizations, and numerical linear algebra. He is an associate editor for the SIAM Journal on Scientific Computing and SIAM Journal on Matrix Analysis and Applications, and co-chairs the program committee for the Copper Mountain Conference on Multigrid Methods. Education: B.Sc. (Hon.) in Mathematics and Computer Science (UBC, 2000), Ph.D. in Applied Mathematics (CU Boulder, 2004). Postdoctoral work at University of Colorado, University of Minnesota, and Delft University of Technology. Research interests include multiscale numerical analysis, PDE-based models in fluid/solid mechanics, and algorithm development for scientific computing. His work integrates functional analysis, numerical linear algebra, and physical modeling. He develops open-source software such as XBraid for parallel time integration and adaptive AMG codes. Publications span multigrid methods, domain decomposition, and liquid crystal simulations. He collaborates on projects involving thermal slip, magnetohydrodynamics, and geophysical electromagnetics. Teaching includes courses on numerical methods for PDEs at both Memorial University and Tufts. Professional activities include editorial roles, conference organization, and contributions to international workshops on multiscale modeling. His software tools and algorithmic innovations address challenges in parallel computing and large-scale simulations.

Shuang LiuView profile

Assistant Professor

• Numerical Methods
• Free Boundary Problems
• Mathematics of Scientific Computing
• +4 more

Shuang Liu is an Assistant Professor in the Department of Mathematics at the University of North Texas. Previously, she held positions as a SEW Assistant Professor at UC San Diego and Postdoc Research Associate at Los Alamos National Laboratory. She earned her PhD in Mathematics from the University of South Carolina and holds MS/BSc degrees from Henan Normal University. Her research focuses on numerical methods for free boundary problems with applications in ecology, plasma physics, and computational biology. Key areas include population dynamics modeling, cell movement simulation, and plasma equilibrium computations. Her publications demonstrate consistent focus on developing robust numerical algorithms for complex biological and physical systems, particularly advancing techniques for moving boundary problems and high-performance computing implementations. Awards: AMS Simons Travel Grant (2021-2023) NSF Graduate Internship at LANL (2019) George W. Johnson Fellowship (2019) Multiple travel awards from AMS/SIAM and USC She has supervised graduate students including Zunding Huang and received research funding from UCSD and AMS. Her work involves collaborations with research groups in computational mathematics and plasma physics.

Hannah MorganView profile

Assistant Professor

• High Performance Computing
• Numerical Models
• Algorithms
• +3 more

Hannah Morgan is an Assistant Instructional Professor in the Department of Computer Science at the University of Chicago and also serves as a Lecturer in the Masters Program in Computer Science. She previously held a postdoctoral position in numerical methods at Argonne National Laboratory. Her work focuses on high performance computing, numerical models, and algorithms, particularly in the context of exascale computing and parallel implementations. Her research emphasizes developing performance models for high-performance computing algorithms, including those for solving large sparse systems and finite element methods for fluid models. At Argonne, she contributed to the Exascale Computing Project by studying computational kernels on heterogeneous CPU-GPU architectures. Her publications span topics like PETSc library enhancements for exascale systems, performance analysis of Krylov solvers, and finite element methods for fluid dynamics. These reflect her expertise in parallel computing, numerical linear algebra, and scalable simulation techniques. Morgan’s work bridges theoretical numerical methods with practical high-performance computing challenges, aiming to advance computational tools for scientific discovery at extreme scales.

• Numerical Linear Algebra
• Scientific Computing
• Image Reconstruction
• +5 more

Misha Kilmer is the William Walker Professor of Mathematics at Tufts University, with a secondary appointment in Computer Science. She also serves as Deputy Director of ICERM at Brown University and co-PI of Tufts TRIPODS Institute. Her research focuses on numerical linear algebra, scientific computing, and image reconstruction/restoration. Kilmer has held leadership roles, including Chair of Tufts' Mathematics Department (2013-2019) and has received prestigious recognition, including SIAM Fellowship (2019) and the Tufts Undergraduate Teaching Award (2001). Education: PhD in Applied Mathematics, University of Maryland (1997) MA in Mathematics, Wake Forest University (1994) BS in Mathematics, Wake Forest University (1992) Research Interests: Kilmer's work emphasizes tensor decompositions, iterative methods, and model reduction. She develops algorithms for high-dimensional data analysis, imaging applications, and inverse problems. Her techniques bridge computational mathematics and engineering challenges, with applications in biomedical imaging, environmental monitoring, and machine learning. Publications Trends: Recent work highlights advancements in tensor-based neural networks, dynamic image reconstruction, and efficient algorithms for large-scale data. Key contributions include randomized algorithms for tensor decompositions and compressed sensing techniques for multi-modal data. Awards: SIAM Fellow (2019) Tufts Undergraduate Initiative in Teaching Award (2001) Advising & Grants: Kilmer has secured funding from NSF, NIH, IARPA, DARPA, and IBM. She co-chairs major SIAM conferences and serves on editorial boards for journals like SIAM Review and La Matematica . Labs/Teams: Active in Tufts' Data Intensive Studies Center (DISC) and co-leads the TRIPODS Institute. Collaborates internationally on projects involving tensor analysis and computational science.

• Theoretical Physics
• Quantum Gravity
• Holography
• +3 more

Adrian Sanchez Garrido is a Research Fellow at the University of Southampton. His work focuses on theoretical physics, particularly exploring holography and chaos theory through the lens of Krylov complexity. He contributes to interdisciplinary research projects such as the UKRI (Research England)-funded initiative on Chaos and Coarse-Graining in Holography. Research interests include quantum gravity, integrable systems, and quantum information science. His publications demonstrate sustained engagement with Krylov space dynamics and its applications in understanding quantum system behavior. No scientific awards or supervised students are listed in his profile. He collaborates on research projects involving holographic dualities and chaos quantification.

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.

• Numerical Linear Algebra
• Optimization Methods
• Statistical Methods
• +3 more

Dr. Mei Yang is an Assistant Professor of Instruction in the Division of Data Science at The University of Texas at Arlington, where she has been serving since September 2024. Previously, she held positions as a Visiting Assistant Professor of Instruction in the Department of Mathematics (2023-2024) and as a Postdoc Researcher at both UT Arlington and Shanghai Jiao Tong University. Her academic journey includes a PhD from UT Arlington focused on Krylov subspace optimization. Her research spans three primary domains: Numerical Linear Algebra : Developing advanced iterative methods for matrix computations Optimization Methods : Creating efficient algorithms for mathematical programming Statistical Methods : Applying mathematical techniques to data analysis Her recent publications demonstrate a consistent focus on computational mathematics, particularly in developing novel Krylov subspace methods for linear systems, saddle point problems, and inverse problems. This work bridges theoretical mathematics with practical applications in engineering and measurement sciences. Dr. Yang has received significant recognition for her contributions: Innovative Teaching Award (College of Science, 2024) UTA CARES GRANT (2025) She teaches extensively in data science and mathematics, covering courses such as Machine Learning, Optimization, Statistical Inference, and Linear Algebra across undergraduate and graduate programs. No information is currently available regarding student advising or laboratory leadership roles.

John CagnolView profile

Researcher

• Applied Mathematics
• Mathematical Physics
• Computational Mechanics
• +3 more

John Cagnol is a Researcher at CentraleSupélec, affiliated with the Department of Mathematics and Computer Science within the Mathematics and Computer Science for Complexity and Systems Laboratory. His work spans applied mathematics, computational mechanics, and AI-driven healthcare solutions. Recent research focuses on shape optimization in Maxwell’s equations, hyperelastic shell dynamics, and AI applications in medical imaging. He collaborates extensively, with co-authors addressing topics like vaccine-resistant variant modeling and curriculum design for complex systems engineering. His contributions bridge theoretical frameworks (e.g., Hadamard well-posedness) with practical applications (e.g., insurance regulatory frameworks). Education: Details not explicitly stated in provided texts. Research interests emphasize interdisciplinary approaches, blending mathematical physics with real-world challenges in healthcare and engineering education. He leads efforts in curriculum innovation at CentraleSupélec, aiming to integrate systems thinking into engineering pedagogy. No scientific awards are mentioned, though his publication record reflects active collaboration across academia and industry.

• Numerical Analysis
• Finite Element Methods
• Fluid Mechanics
• +6 more

Gabriel Barrenechea is a Reader in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. His academic work centers on the development and analysis of finite element methods with strong theoretical foundations, particularly applied to fluid mechanics and partial differential equations. His research interests lie at the intersection of numerical analysis and computational mathematics, with a focus on ensuring stability, accuracy, and physical consistency in simulations. Key areas include discrete maximum principles, bound-preserving schemes, stabilization techniques for convection-dominated flows, and multiscale methods. His work often addresses challenges in non-Newtonian fluids and high-Weissenberg number problems. The recent publications reflect a sustained focus on finite element discretizations that preserve physical bounds and mathematical monotonicity. These works span elliptic, parabolic, and fluid flow problems, employing hybrid, nodal, and implicit-explicit formulations. The trend shows increasing sophistication in handling nonlinearities, variable coefficients, and complex physical constraints through mathematically rigorous approaches. Best paper award Sociedad Espanola de Matematica Aplicada (SEMA) 2018 Gabriel Barrenechea has been actively involved in research leadership and student training. He has served as Principal Investigator on multiple EPSRC-funded and institutional projects, including doctoral training partnerships. He supervises postgraduate research and contributes to academic development frameworks. His professional activities include organizing major conferences such as the Biennial Conference on Numerical Analysis and the Scottish PDE Colloquium. He is a key organizer of academic events including the Impact Case Studies in Maths & Stats workshop and the Early Career Network in Mathematics and Statistics. His collaborations span the UK and international institutions, reflected in co-authored works and joint projects.

Pranav SinghView profile

Senior Lecturer

• Numerical Analysis
• Quantum Mechanics
• Computational Mathematics
• +12 more

Pranav Singh is a Senior Lecturer (Associate Professor) in the Department of Mathematical Sciences at the University of Bath, United Kingdom. He is affiliated with the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa) and the Institute for Mathematical Innovation (IMI). His academic journey includes a Junior Research Fellowship at the University of Oxford and a PhD from the University of Cambridge. PhD in Applied Mathematics, University of Cambridge (2012–2016) MASt in Applied Mathematics, University of Cambridge (2008–2009) BTech & MTech in Computer Science and Engineering, Indian Institute of Technology Delhi (2000–2005) His research lies at the intersection of numerical analysis, quantum mechanics, and computational mathematics. Key areas include Numerical Methods for Evolutionary Equations , Quantum System Simulation , Optimal Control , and Geometric Numerical Integration . He employs advanced techniques such as splitting methods, Krylov subspace methods, deep learning, and Lie group methods. Applications span quantum computing, NMR/MRI, laser-matter interactions, and photovoltaic systems. The recent publications reflect a strong focus on high-accuracy numerical schemes for PDEs and quantum systems, with an emphasis on convergence, stability, and efficiency. Themes include splitting methods, Hamiltonian simulation, and rational approximants, indicating a trend toward provably convergent and structure-preserving algorithms in both classical and quantum domains. Pranav Singh has secured multiple research grants, including projects funded by the London Mathematical Society and the Heilbronn Institute for Mathematical Research. These include workshops on quantum complexity, computational mathematics for quantum technologies, and N-body computations, highlighting his leadership in emerging areas of computational mathematics. He is actively involved in software development for research, having created magpy for quantum spin simulations and expsolve for solving differential equations. He is accepting doctoral students and contributes to academic training through the SAMBa CDT. His work aligns with UN Sustainable Development Goal 4: Quality Education.

Iryna RybakView profile

Senior Lecturer

• Multiscale Methods
• Mathematical Modelling
• Numerical Algorithms
• +3 more

Iryna Rybak is a Senior Lecturer and Principal Investigator at the University of Stuttgart's Institute of Applied Analysis and Numerical Simulation. Her research focuses on mathematical modeling, numerical methods, and multiscale analysis for coupled free-flow and porous medium systems. She coordinates the German side of the ANR-DFG project FLUPOR and leads Project A03 in the Collaborative Research Centre (SFB) 1313 on interface-driven multi-field processes in porous media. Academic Roles: Research Assistant, Principal Investigator Institution: University of Stuttgart Key Projects: FLUPOR (ANR-DFG), SFB 1313 (DFG) Research Interests: Multiscale methods, averaging theories, Stokes-Darcy coupling, numerical algorithms for multiphysics problems, and model validation using neural networks. Her work addresses interface conditions, porous media flow, and sediment transport in three-phase systems. Publications emphasize fluid-porous interface modeling, preconditioning techniques, and sensitivity analysis. She has advised over 15 PhD/Master students on topics like preconditioners for coupled systems and surrogate-assisted validation frameworks. Teaching includes advanced numerical methods, mathematical programming, and partial differential equations at both undergraduate and graduate levels. Grant Roles: Principal Investigator in FLUPOR and SFB 1313; coordinator of DFG-funded projects on interface modeling (2012–2025).