Find Academic Supervisors

• Numerical Analysis
• Geometric Integration
• Quantum Dynamics
• +5 more

Prof. Christian Lubich is a Professor in the Mathematical Institute at the University of Tübingen, Germany. His research focuses on numerical analysis, geometric integration, quantum dynamics, and partial differential equations. He has authored influential books such as *Geometric Numerical Integration* (with E. Hairer and G. Wanner) and *From Quantum to Classical Molecular Dynamics*. His work emphasizes stability, accuracy, and computational efficiency in numerical methods for high-dimensional and oscillatory systems. Lubich's contributions span dynamical low-rank approximation, oscillatory Hamiltonian systems, and wave propagation in dispersive media. He has developed novel algorithms for tensor networks and matrix differential equations, addressing challenges in quantum many-body dynamics and stiff evolution problems. His research bridges numerical analysis with applications in physics, chemistry, and engineering. Key areas of exploration include: (1) structure-preserving integrators for long-time simulations, (2) error analysis of finite difference methods in semi-classical regimes, and (3) parallel algorithms for tensor-based computations. His work on pseudospectra and eigenvalue optimization has advanced stability analysis in linear dynamical systems. Lubich collaborates extensively on boundary element methods, finite element algorithms for evolving surfaces, and variational discretization techniques. His publications reflect a commitment to both theoretical rigor and practical computational solutions for complex physical systems.

• Multiphase Flow Modelling
• Lattice-Boltzmann Methods
• Energy Systems Engineering
• +3 more

Dr. Duo Zhang is a Research Fellow at the School of Chemistry and Chemical Engineering, University of Surrey. He holds a PhD from the University of Liverpool (2015). His research focuses on multiphase flow modeling, energy systems engineering, and sustainable technology development. Key areas include lattice-Boltzmann method applications in porous media, anaerobic digestion optimization, and redox flow battery performance analysis. His work bridges computational fluid dynamics with real-world energy challenges, particularly in renewable energy systems and environmental policy implications. Recent studies explore techno-economic analyses of energy infrastructure and carbon credit valuation in anaerobic digestion. Publications span a decade of contributions to fluid dynamics, electrochemical systems, and sustainable energy solutions. No awards are listed, but his research has significant industrial and policy relevance. Advising and grant details are not provided in available texts. His work contributes to advancing energy storage and environmental sustainability technologies.

Tabea TscherpelView profile

Professor

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations (PDEs)
• +3 more

Tabea Tscherpel is a tenure-track professor at the Department of Mathematics of TU Darmstadt, specializing in Numerical Analysis and Scientific Computing. Previously, she held postdoctoral positions at Universität Bielefeld (under Lars Diening) and RWTH Aachen (under Sebastian Noelle), following her DPhil from the University of Oxford under Endre Süli’s supervision. Her research focuses on numerical methods for PDEs, finite element methods, and computational fluid dynamics. Key research interests include exact integration for finite elements, divergence-free spaces, and energy-consistent discretizations. Her work bridges theoretical analysis with practical implementation, addressing challenges in computational geometry and adaptive methods. She actively contributes to conferences such as the Biennial Numerical Analysis Conference (2024) and plans to lead workshops on adaptive finite element methods. Contact: Office S4|10 101a, TU Darmstadt | tscherpel@mathematik.tu-darmstadt.de

Marc PfetschView profile

Professor

• Discrete Optimization
• Mathematical Programming
• Operations Research
• +3 more

Prof. Dr. Marc Pfetsch is a full professor of Discrete Optimization at the Technical University of Darmstadt, holding the W3 chair since 2012. He leads the Optimization Group within the Department of Mathematics and has served as Dean of the Department from October 2022 to September 2024. His research focuses on optimization methodologies, particularly in gas network modeling, discrete and mixed-integer programming, and computational algorithms. He is a core developer of the SCIP Optimization Suite, a leading solver for mixed-integer programming problems. Education : Mathematics studies at the University of Heidelberg (1992–1997) Operations Research at Cornell University (1997–1998, via Fulbright Scholarship) PhD in Mathematics from TU Berlin (2002) Habilitation in Computational Aspects of Combinatorial Optimization (2008) Research Interests : Discrete and combinatorial optimization Gas network optimization and resilience design Symmetry handling in mixed-integer programming Algorithm development for SCIP and optimization software Key Projects : Transregio/SFB 154: Mathematical Modeling, Simulation, and Optimization of Gas Networks SCIP Optimization Suite development Clean Circles: Iron as an energy carrier for climate-neutral systems Awards : EURO Excellence in Practice Award 2016 for "Evaluating Gas Network Capacities" Grants and Labs : Principal investigator in multiple DFG projects (e.g., SPP 2298, Matheon) BMWi-funded projects on flexible heating networks and resilient systems

Marc Alexander SchweitzerView profile

Professor

• Numerical Methods
• Computational Mechanics
• Meshfree Methods
• +4 more

Marc Alexander Schweitzer is a Professor of Mathematics at the University of Bonn and affiliated with Fraunhofer SCAI. His work focuses on numerical methods and computational mechanics, particularly meshfree methods, peridynamics, and algebraic multigrid techniques. He contributes to advancing computational tools for engineering and materials science applications. Research interests include partition of unity methods, multiscale modeling, fracture mechanics, and efficient linear solvers. His work integrates mathematical theory with practical computational challenges, such as adaptive algorithms and parallel computing. Publications highlight contributions to peridynamic fracture models, scalable solvers for material design, and visualization of fracture progression in particle-based systems. Collaborative efforts with Fraunhofer SCAI emphasize industrial applications and algorithmic innovation.

Richard BrowerView profile

Professor

• Lattice Field Theory
• Quantum Computing
• High-Performance Computing
• +5 more

Richard Brower is a Professor in the Department of Electrical and Computer Engineering at Boston University's College of Engineering, with an affiliated faculty role in the Department of Physics. His research focuses on numerical lattice field theory, quantum computing, and high-performance computing for exascale supercomputers. He holds a PhD in Physics from the University of California. Education: B.S. Physics, Harvard University, 1963 M.A. Applied Math, Harvard University, 1964 PhD, Physics, University of California, 1968 Research Interests: Brower advances multi-grid software for exascale systems, explores quantum computing applications for qubit and analog hardware, and investigates quantum physics in particle, string, and gravity contexts. His work includes stealth dark matter studies, Lorentzian conformal field theories, and lattice QCD simulations. Publications Trends: His recent articles address lattice quantization methods, stealth dark matter spectra, and conformal symmetry in 2D/3D Ising models. He frequently integrates geometric and topological approaches with computational physics. Scientific Awards: A.P. Sloan Research Fellow (1974–1976) Past Managing Editor, International Journal of Computational Physics Advising & Grants: Brower collaborates on DOE exascale computing projects and leads efforts in multigrid algorithms for fermion calculations. His work spans lattice gauge theory applications in quantum computers and beyond-Standard-Model physics. Labs/Teams: Affiliated with BU's computational physics and quantum computing research groups, contributing to national HEP computing initiatives.

Cristóbal BertoglioView profile

Associate Professor

• Cardiovascular Mathematics
• Biomedical Engineering
• Fluid-Structure Interaction
• +3 more

Cristóbal Bertoglio is an Associate Professor at the University of Groningen, affiliated with the Computational and Numerical Mathematics group at the Bernoulli Institute within the Faculty of Science and Engineering. His research focuses on cardiovascular mathematics, biomedical engineering, and fluid-structure interaction in biological systems. He develops mathematical models and numerical methods to study cardiovascular dynamics, integrating data and models across scales to improve diagnostics and treatment for cardiovascular diseases. His work involves computational hemodynamics, inverse problem methods, and medical imaging techniques such as phase-contrast MRI and 4D flow MRI. Notable contributions include software benchmarks for cardiac elastodynamics, denoising algorithms for MRI velocity data, and parametric models for blood flow simulations. He collaborates internationally with researchers in engineering, mathematics, and clinical medicine to advance translational research in cardiovascular science. Dr. Bertoglio leads the Cardiovascular Mathematics Team, emphasizing interdisciplinary approaches to address challenges in modeling cardiac mechanics, pressure estimation, and fluid dynamics in vascular systems. His research also explores parameter identification, numerical stability, and optimization techniques for biomedical applications.

Sergei PopovView profile

Professor

• Nano-photonics
• Nano-plasmonics
• High-capacity optical communication
• +5 more

Sergei Popov is a Professor at KTH Royal Institute of Technology, affiliated with the department of LJUS OCH MATERIENS FYSIK within the School of Electrical Engineering and Computer Science (EECS). He holds an interdisciplinary research role focusing on photonics, optical communications, and materials science. His academic journey includes a PhD in Applied Physics from Helsinki University of Technology (1998) and extensive industry experience at Ericsson Telecom AB and Acreo AB before joining KTH in 1999. He has held visiting professorships at institutions like École Polytechnique Fédérale de Lausanne (EPFL), Zhejiang University, University of Tokyo, and ETH Zurich. His research interests span nano-photonics, nano-plasmonics, high-capacity optical communication systems, and novel optical materials, with particular emphasis on transparent wood composites and terahertz communications. He has authored/co-authored over 300 journal papers and holds 11 patents. Notable contributions include advancements in free-space optical transmission, quantum cascade lasers, and deep learning applications in optical systems. Popov has received the OSA (Optica) Fellow award and actively contributes to editorial boards of journals like the Journal of the European Optical Society and Applied Sciences . He has organized major conferences such as the Northern Optics and Photonics (NOP 2015) and chaired the OSA David Richardson Medal Committee (2014–2015). His teaching includes roles as examiner and instructor in courses like Fiber-Optic Communication and Electromagnetism and Waves , reflecting his commitment to both research and education. Current research explores interdisciplinary applications of photonics and materials science, addressing challenges in high-speed data transmission and sustainable optical materials.

• Fluid Mechanics
• Structural Mechanics
• Nonlinear Dynamics
• +3 more

Arne Nordmark is a Lecturer at the Department of Mechanics, KTH Royal Institute of Technology in Stockholm, Sweden. He is affiliated with the FLOW MECHANICS research group and actively involved in course coordination, examination, and teaching roles across multiple mechanics and fluid mechanics programs. His primary responsibilities include managing courses such as Mechanics I with Project (SG1132), Mechanics II (SG1140), and advanced fluid mechanics degree projects. Nordmark’s work focuses on computational mechanics, structural stability, and multiphysics modeling, with significant contributions to the analysis of membrane systems and impact oscillators. His research interests span fluid mechanics, structural mechanics, nonlinear dynamics, and applied mathematics. He has explored topics like turbulence on spherical geometries, stability of hyper-elastic membranes under pressure loads, and bifurcation phenomena in nonsmooth dynamical systems. His recent work emphasizes the development of numerical methods for stability analysis and multiphysics couplings in engineering simulations. Nordmark’s publications from 2020–2024 reflect a strong focus on structural and fluid-structure interaction problems, particularly in modeling membrane behavior under various conditions. He has investigated wrinkling mechanics, parametric instabilities, and the application of finite element methods to complex systems. He has no listed scientific awards or formal advisees. His contributions include curriculum development and experimental studies on mechanical systems with impacts, though specific grant details are not provided in the text.

• Computational Fluid Dynamics
• Biomedical Engineering
• Numerical Analysis
• +7 more

Johan Hoffman is a Professor of Numerical Analysis at KTH Royal Institute of Technology, Stockholm, Sweden, and Deputy Head of the Division of Computational Science and Technology. He leads research in computational science, with a focus on fluid dynamics, biomedical engineering, and numerical methods. His work integrates fundamental mathematics with applied research in aerodynamics, urban planning, renewable energy, and biomedicine. Hoffman is a founder of the FEniCS open-source software project and a partner in the EU’s GENEX project on digital twins. Education: Ph.D. in Applied Mathematics, Chalmers University of Technology Postdoctoral Fellow, Courant Institute, New York University Visiting Researcher at Oxford University, Stanford University, and others Research Interests: Hoffman’s research spans computational fluid dynamics, turbulence modeling, finite element methods, and biomedical applications such as cardiovascular simulations. He develops adaptive numerical methods and high-performance computing frameworks for complex systems. Publications Trends: Recent work emphasizes fluid-structure interaction in biomedical contexts (e.g., mitral valves), turbulence analysis, and multiphysics simulations in renewable energy. His methods prioritize stability, adaptivity, and scalability for large-scale problems. Awards: ERC Proof of Concept Grant (2015) Ingvar Carlsson Award (2005) Leslie Fox Prize (2005) Bill Morton Prize (2001) Advising & Grants: Hoffman’s funding includes grants from the Swedish Research Council, ERC, and EU Horizon 2020. He supervises research on digital twins, turbulence modeling, and biomedical simulation. His lab collaborates with industry and academic partners globally. Labs/Teams: Core contributor to the FEniCS project and leader of computational fluid dynamics teams at KTH, advancing open-source tools for multiphysics simulation.

Ivan YotovView profile

Professor

• Numerical Analysis
• Scientific Computing
• Fluid Dynamics
• +5 more

Ivan Yotov is a Professor in the Department of Mathematics at the University of Pittsburgh, affiliated with The Dietrich School of Arts and Sciences. He holds a PhD in Computational and Applied Mathematics from Rice University (1996). His research focuses on numerical analysis, scientific computing, and multiphysics systems, with expertise in fluid flow, porous media, and domain decomposition methods. Key research areas include mixed finite element methods, multiscale modeling, and coupled systems (e.g., fluid-poroelastic interactions). He has contributed to reservoir simulation frameworks like UT-MB and collaborated on projects involving multiphase flow, geomechanics, and biomedical applications. Yotov teaches advanced courses in numerical analysis, linear algebra, and scientific computing. His work emphasizes accurate discretizations and scalable algorithms for complex systems, addressing challenges in energy, environmental, and biomedical engineering. Notable projects include the Department of Energy-funded multiblock reservoir simulation initiative and collaborations with institutions like Argonne National Laboratory.

Michael SchneierView profile

Adjunct Professor

• Computational Mathematics
• Uncertainty Quantification
• Machine Learning
• +3 more

Michael Schneier is an adjunct professor in the Department of Mathematics at the University of Pittsburgh. He holds a Ph.D. in Scientific Computing from Florida State University (2018), advised by Professor Max Gunzburger. His research focuses on computational mathematics, uncertainty quantification, machine learning, reduced order modeling, and computational fluid dynamics, with a particular emphasis on leveraging data to enhance traditional computational fluid dynamics algorithms. Research interests include advancing numerical methods for partial differential equations, turbulence modeling, and the integration of machine learning techniques into fluid dynamics simulations. His work emphasizes improving algorithmic efficiency and accuracy through innovative approaches like reduced-order models and latent diffusion frameworks. Selected publications explore topics such as latent neural PDE solvers, turbulence diagnostics, and ensemble-based numerical methods for fluid flows. His contributions span both theoretical advancements and practical applications in computational science. No scientific awards or grants are explicitly listed in the provided information. He currently advises no students, though his research may involve collaborative projects. His work is centered at the Department of Mathematics within the Dietrich School of Arts and Sciences.

• Pharmaceutical Sciences
• Drug Delivery Systems
• Colloid and Surface Chemistry
• +3 more

Ian Larson is a Senior Lecturer in the Department of Pharmacy and Pharmaceutical Sciences Education at Monash University's Faculty of Pharmacy and Pharmaceutical Sciences. His research focuses on drug delivery systems, colloid chemistry, and formulation science, with notable contributions to aerosolization behavior of powders and virtual learning tools like Pharmatopia. He has led or co-led 10 research projects, including initiatives on study strategies and quality improvement in pharmaceutical education. Teaching expertise spans colloid chemistry, polymer chemistry, and formulation design. He has received prestigious awards including the Vice Chancellor's Teaching Excellence Award (2010) and AACP Innovation in Teaching Award (2022). Larson's work aligns with UN Sustainable Development Goals related to quality education and good health. Key collaborations involve Monash Institute of Pharmaceutical Sciences and international partners like the University of Warwick. His projects emphasize interdisciplinary approaches, such as 3D printing for drug formulation and virtual reality for dementia education. Over 129 peer-reviewed outputs highlight his contributions to both scientific innovation and pedagogical advancements.

Yongtao ZhangView profile

Professor

• Computational Physics
• Mathematical and Computational Biology
• Numerical Solution of Differential Equations
• +3 more

Yongtao Zhang is a Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He holds a PhD from Brown University (2003) and an M.S. from Nankai University (1999). His research focuses on developing high-order numerical methods for partial differential equations (PDEs), including weighted essentially non-oscillatory (WENO) schemes, discontinuous Galerkin (DG) methods, and implicit integration factor (IIF) methods. These methods are applied to computational physics (e.g., shock wave dynamics, fluid flows) and computational biology (e.g., morphogenesis, tissue patterning). Dr. Zhang's work emphasizes numerical solutions for hyperbolic conservation laws, stiff advection-reaction-diffusion equations, and Hamilton-Jacobi equations. He has contributed to sparse-grid implementations, fast sweeping methods, and exponential time differencing schemes to enhance computational efficiency and accuracy. His research bridges theoretical advancements with practical applications in high-dimensional systems and complex geometries. He is an editorial board member for Journal of Scientific Computing and Communications on Applied Mathematics and Computation , reflecting his leadership in the field. Teaching responsibilities include numerical analysis, scientific computing, and applied mathematics courses. His work has been published in leading journals such as Journal of Computational Physics and SIAM Journal on Scientific Computing .

• Computational Fluid Dynamics
• Low Reynolds Number Hydrodynamics
• Integral Equation Methods
• +3 more

Dr. Mary Catherine Kropinski is a Professor (On Leave) in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. She holds a PhD in Applied Mathematics from Rensselaer Polytechnic Institute (1993), followed by a postdoctoral appointment at the Courant Institute at NYU. Her research focuses on computational fluid dynamics and low Reynolds number hydrodynamics, with an emphasis on developing integral equation methods for solving fluid dynamics equations like Stokes and Navier-Stokes. These methods are applied to complex systems such as microfluidic flows, sedimentation processes, and biological flows. Dr. Kropinski has been recognized with the NSERC Women's Faculty Award and has contributed to peer review for journals like the Journal of Computational Physics . Her work bridges theoretical advancements in numerical methods with practical applications in engineering and environmental science. She has served on NSERC committees and has authored numerous influential papers on fluid dynamics modeling and algorithm development.