Find Academic Supervisors

• Mathematical Modelling
• Applied Analysis
• differential equations
• +6 more

Prof. Johannes Zimmer is Chair of Analysis and Modelling at the Department of Mathematics, School of Computation, Information and Technology at the Technical University of Munich (TUM). His research focuses on mathematical modeling and analysis of complex systems, with particular expertise in differential equations, variational problems, stochastic models, and nonequilibrium dynamics. His primary research interests include: Mathematical Modelling of physical systems Applied Analysis of differential equations and variational problems Stochastic models and scale-bridging techniques Nonequilibrium problems in statistical mechanics Hydrodynamic limits and fluctuations in particle systems Prof. Zimmer's recent work demonstrates a strong focus on connecting microscopic particle systems to macroscopic evolution equations, with applications in statistical physics, thermodynamics, and multiscale modeling. His research often combines rigorous mathematical analysis with physical insights from nonequilibrium statistical mechanics. A notable trend in his recent publications is the development of frameworks that bridge Hamiltonian dynamics with dissipative structures, as well as the application of machine learning techniques to coarse-graining problems in statistical physics. Among his scientific contributions are advancements in understanding: GENERIC formalism for non-equilibrium thermodynamics Dean-Kawasaki models for density fluctuations Fast-slow Hamiltonian systems and their thermodynamic interpretation Statistical-physics-informed neural networks Prof. Zimmer actively supervises students and collaborates with researchers across disciplines, including physicists, applied mathematicians, and computational scientists. His work often involves international collaborations with institutions in Europe and beyond. He teaches advanced mathematics courses at TUM, including Higher Mathematics for Mechanical and Chemical Engineering students, and leads seminars on mathematical modeling and nonequilibrium systems.

George BirosView profile

Professor

• Computational Engineering
• Parallel Algorithms
• Numerical Methods
• +7 more

George Biros is a Professor of Mechanical Engineering and holds the W.A. 'Tex' Moncrief Jr. Endowed Chair in Simulation-Based Engineering Sciences at the University of Texas at Austin. He leads the Parallel Algorithms for Data Analysis and Simulation Group within the Institute for Computational Engineering and Sciences (ICES). His research focuses on computational mathematics, parallel algorithms for physics-based simulations, and large-scale data analysis, with applications in biomedical imaging, tumor growth modeling, and high-performance computing. Biros earned his Ph.D. in Computational Science and Engineering from Carnegie Mellon University (CMU), followed by a postdoctoral fellowship at NYU's Courant Institute. He previously held faculty positions at the University of Pennsylvania and Georgia Tech. His work emphasizes scalable algorithms for exascale computing, including fast multipole methods and hierarchical matrix techniques. Research interests span parallel algorithms, numerical methods for PDEs, inverse problems, and biophysical modeling. Notable contributions include tumor growth models with mass effect, image registration algorithms (e.g., CLAIRE), and GPU-accelerated solvers for fluid dynamics and plasma simulations. His group develops open-source libraries like PyKokkos for performance-portable Python kernels. Recent publications highlight advancements in grain microstructure prediction via graph neural networks, single-scan MRI tumor calibration, and tau protein dynamics modeling in Alzheimer's disease. He has pioneered GPU-based diffeomorphic image registration for large biomedical datasets, achieving real-time performance on multi-GPU systems. Awards: Two-time ACM Gordon Bell Prize (2010, 2016), DOE Early Career Award Grants: Leads projects in exascale computing, numerical methods, and medical image analysis Labs: ICES Parallel Algorithms Group, ExaNIML (Exascale Numerically Inspired Machine Learning)

Wing K. LiuView profile

Professor

• Computational Mechanics
• Multiscale Modeling
• Additive Manufacturing
• +5 more

Wing K. Liu is the Walter P. Murphy Professor of Mechanical Engineering, Civil and Environmental Engineering (by courtesy), and Materials Science and Engineering at Northwestern University. He leads the HiDeNN-AI research group, focusing on mechanistic data science, hierarchical deep learning neural networks (HiDeNN), and multiscale modeling for advanced manufacturing and materials innovation. His work integrates physics-based principles with machine learning to address challenges in additive manufacturing, computational mechanics, and materials design. Education : Ph.D., M.S., California Institute of Technology B.S. (Hons) in Engineering Science, University of Illinois at Chicago Research Interests : Liu’s research spans mechanistic data science, AI-driven materials innovation, and multiscale modeling. His HiDeNN framework enables fast design, optimization, and discovery in engineering systems. Key areas include laser powder bed fusion, image-based modeling, and predictive manufacturing processes. He emphasizes combining scientific principles with data to accelerate technology development. Awards and Recognition : Gauss-Newton Medal (2012) John von Neumann Medal (2007) ASME Robert Henry Thurston Lecture Award (2007) ISI Highly Cited Researcher in Engineering (2001) Teaching and Academic Leadership : Liu teaches advanced finite element methods, multiscale modeling, and computational mechanics. He has served as editor for Computational Mechanics , vice president of the International Association for Computational Mechanics, and past president of the U.S. Association for Computational Mechanics. Labs and Teams : His research group, Wing K. Liu Group , develops cutting-edge tools like JAX-FEM and C-HiDeNN, focusing on GPU-accelerated simulations and AI integration in engineering systems.

Michele NottoliView profile

Researcher

• Computational Chemistry
• Quantum Mechanics
• Molecular Dynamics
• +5 more

Michele Nottoli is a researcher at the University of Stuttgart's Institute of Applied Analysis and Numerical Simulation (Faculty 08), specializing in Numerical Mathematics for High-Performance Computing. He holds a Ph.D. and works as a Postdoc and Research Assistant. His research focuses on developing advanced computational methods for quantum mechanics and molecular dynamics, emphasizing polarizable embedding models, QM/MM coupling, and high-performance numerical algorithms. Key contributions include the development of the ddX library for polarizable continuum solvation and optimizing linear-scaling methods for large-scale molecular simulations. Nottoli's work addresses challenges in modeling light-driven processes in complex systems, such as photoreceptors and antenna complexes, with applications in biochemistry and materials science. His methodologies integrate domain-decomposition techniques, fast multipole methods, and symmetry-preserving approaches to enhance computational efficiency and accuracy. He actively contributes to open-source software development, including the OpenMMPol library, to facilitate multiscale simulations of embedded molecules in polarizable environments. His research also explores excited-state dynamics, solvation effects, and nonperiodic boundary conditions, with implications for understanding magnetic shifts and optical properties in chemical systems. Nottoli collaborates on projects involving light-harvesting mechanisms in purple bacteria and the molecular mechanisms of solvent shifts in nitroxide radicals. Despite his prolific publication record, no scientific awards are currently listed. Advising and grants: Nottoli is actively involved in postdoctoral research but no specific advisees or grant details are provided in the available information. His affiliation with the Numerical Mathematics group positions him at the intersection of algorithm design and applied computational science.

• Computational Electromagnetics
• Antenna Design
• Numerical Methods in Electromagnetics
• +3 more

Iván González Diego is an Associate Professor at the University of Alcalá's Department of Computer Science, affiliated with the GEC Grupo de Electromagnetismo Computacional (Computational Electromagnetic Group) . He holds a Ph.D. from the University of Alcalá (2004) for his thesis on improving the Geometrical Theory of Diffraction (GTD) for complex radiative systems. His work focuses on computational electromagnetics, antenna design, and advanced numerical techniques such as the Method of Moments (MoM), Multilevel Fast Multipole Method (MLFMM), and hybrid approaches combining asymptotic and rigorous methods. Key research areas include electromagnetic scattering analysis of large structures, reflectarray antenna design, and optimization of radio frequency systems using evolutionary algorithms. He has developed tools like NEWFASANT and MONURBS for electromagnetic simulations and pre-processing. His contributions span radar systems, V2X communication channels, and antenna-radome interaction studies. Publications highlight advancements in accelerating ray-tracing, improving preconditioners for iterative solvers, and analyzing wind turbines' electromagnetic interference. Notably, his work on multi-beam reflectarray antennas and efficient mesh generation techniques has been widely applied in aerospace and satellite communication contexts.

• Computational Electromagnetics
• Radar Cross-Section Analysis
• Antenna Design
• +3 more

Lorena Lozano Plata is an Associate Professor in the Department of Computer Science at the Universidad de Alcalá. Her research focuses on computational electromagnetics, radar cross-section (RCS) analysis, antenna design, and high-performance numerical methods. She holds a PhD from Universidad de Alcalá (2006) with a thesis on accelerating ray-tracing techniques for RCS reduction using physical optics. She is part of the GEC (Computational Electromagnetic Group) and has developed software tools like NewFasant and FASANT for electromagnetic analysis. Her work emphasizes efficient algorithms for solving large-scale electromagnetic problems, including multilevel fast multipole algorithm (MLFMA), characteristic basis function method (CBFM), and hybrid techniques combining high-frequency asymptotic methods with rigorous solvers. Applications include antenna placement optimization, urban V2X communication channels, wind turbine interference characterization, and aerospace satellite communication modeling. She has contributed to GPU-accelerated parallelization strategies and radar system simulations in traffic scenarios. Lozano Plata's research also involves radio propagation analysis in complex environments, radar Doppler spectrum analysis, and pre-processing/meshing optimization for electromagnetic software. Her work bridges theoretical developments with practical tools used in industry and academia for electromagnetic design and simulation challenges.

Karl MeerbergenView profile

Professor

• Algebraic eigenvalue problems
• Algebraic model order reduction
• Preconditioning
• +7 more

Karl Meerbergen is a Full Professor in the Department of Computer Science at KU Leuven, Faculty of Engineering Sciences. He leads research in numerical analysis and applied mathematics with a focus on eigenvalue problems, model order reduction, and computational linear algebra. He is a member of the Numerical Analysis and Applied Mathematics (NUMA) research unit and holds affiliations with multiple KU Leuven institutes including iSi Health, Leuven.AI, Leuven.AM, and the LGI Gravitation Institute. His research spans several key areas of numerical mathematics with emphasis on algebraic eigenvalue problems, algebraic model order reduction, preconditioning techniques, computational acoustics, tensor computations, exascale computing, generic programming, and parallel computing. His work bridges theoretical numerical analysis with practical applications in engineering and scientific computing. Analysis of his recent publications shows a strong focus on advanced numerical methods for eigenvalue problems, model order reduction techniques, and parallel computing approaches. His work frequently addresses challenges in large-scale scientific computing, with applications in structural dynamics, acoustics, and optimization problems. The research demonstrates increasing sophistication in handling nonlinear and parametric systems through rational approximation methods and specialized preconditioning techniques. Dr. Meerbergen actively contributes to the academic community through his teaching responsibilities and supervision of graduate students. His work has significant implications for computational science and engineering applications requiring efficient numerical solutions to complex mathematical problems.

• Applied Analysis
• Multiscale Modeling
• Mathematical Materials Science
• +12 more

Johannes Zimmer is a Professor in the Department of Mathematical Sciences at the University of Bath. His research lies at the intersection of applied analysis, mathematical physics, and materials science, with a focus on multiscale modeling and the derivation of macroscopic laws from microscopic dynamics. He is actively involved in research networks supported by the LMS, EPSRC, and EU, and has contributed to major workshops and collaborative projects in nonlinear PDEs and molecular dynamics. PhD, Technische Universität München (2000) Postdoctoral Fellow, California Institute of Technology Head of Emmy Noether Group, Max Planck Institute for Mathematics in the Sciences, Leipzig Faculty Member, University of Bath Zimmer's research centers on multi-scale methods for dynamic problems , particularly in the context of phase transitions, gradient flows, and molecular systems. He investigates the derivation of thermodynamic quantities such as entropy from microscopic models using tools like large deviation principles and Gamma-convergence . His work addresses rare events in particle systems, kinetic relations in phase boundaries, and the variational structure of irreversible processes. He has made significant contributions to the mathematical analysis of atomistic models , including the Fermi-Pasta-Ulam problem and Frenkel-Kontorova chains. His recent publications reveal a strong trend toward fluctuation theory , hydrodynamic limits , and regularized stochastic models such as the Dean-Kawasaki equation. He explores the geometry of dissipative evolution equations and the orthogonality of forces and fluxes in non-equilibrium systems. His work often combines probabilistic and analytical techniques to bridge scales in physical models. Scientific contributions include: Development of the MODOI and GeometricMD software packages for molecular dynamics simulations Co-organization of the One World Dynamics Seminar Participation in LMS Symposia and EPSRC-funded networks Editorship of Analysis and Stochastics of Growth Processes and Interface Models (Oxford University Press) Zimmer has advised multiple PhD students and postdoctoral researchers, including Daniel Sutton, Marios Stamatakis, and Marcus Kaiser. He has secured research funding from EPSRC, Leverhulme Trust, and EU networks. His teaching includes core courses such as Mathematics 2 , Numerical Solution of PDEs , and Advanced PDEs . He has also taught at Caltech and Technische Universität München. He leads research in variational methods for evolution and is part of the Bath Institute for Complex Systems, contributing to summer schools and interdisciplinary initiatives on complex networks and multiscale modeling.

Richard RabbitzView profile

Assistant Professor

• Computer Graphics
• Virtual Reality
• Augmented Reality
• +3 more

Richard Rabbitz is an Assistant Professor at Rowan University’s College of Science & Mathematics, Department of Computer Science. He holds an MSc in Computer Science from the University of Pennsylvania and a BSc in Computer Science from West Chester University. Education: MSc in Computer Science, University of Pennsylvania (Thesis: "Fast Collision Detection of Complex Dynamic Environments," supervised by Dr. Norm Badler) BSc in Computer Science, West Chester University Research Interests: Richard focuses on computer graphics, GPU computing, and immersive technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR). His work also includes systems simulation and modeling, as well as visualizing large-scale datasets in scientific programming contexts. Industry Experience: He has served as a Principal Member of the Engineering Staff at Lockheed Martin, acting as a subject matter expert in disruptive technologies (GPUs, VR/AR/MR, Digital Twins, Metaverse). At Analytical Graphics Incorporated (AGI), he was a Senior Software Developer specializing in computer graphics and numerical algorithms for solar system visualization. Academic Contributions: Since 2014, he has taught courses at Rowan University, including Computer Vision, Computer Graphics, Object-Oriented Programming with Java, and Introduction to Systems Simulation and Modeling.

• Wave Propagation
• Numerical Methods
• Boundary Element Method
• +5 more

Stéphanie Chaillat-Loseille is a CNRS Research Scientist HDR at ENSTA Paris, working within the Applied Mathematics Unit (UMA) and the Wave Propagation, Mathematical Study and Simulation (POEMS) laboratory. She is actively involved in the GDR Ondes modeling and simulation thematic group and serves on the Academic Council of Institut Polytechnique de Paris, where she leads the parity, diversity and equal opportunities committee. Dr. Chaillat-Loseille's research focuses on developing fast algorithms and numerical methods for simulating wave propagation problems in large-scale domains, particularly acoustic and seismic wave propagation. Her work centers on advancing the boundary element method for mechanical problems, including seismic wave simulation in soil, underwater explosions, contact problems, and radiated noise analysis. She has developed the fast solver COFFEE, which integrates her research contributions and is distributed through collaborations. Her recent publications demonstrate a strong focus on boundary integral equation methods, Green's functions, and multi-physics wave propagation problems. Her work spans applications from geophysics to underwater acoustics, with particular emphasis on developing fast and accurate numerical algorithms for complex wave phenomena. Her research has significant practical applications in seismic hazard assessment, underwater acoustics, and structural engineering. Dr. Chaillat-Loseille participates in teaching activities at ENSTA ParisTech and collaborates extensively with researchers across multiple institutions. Her work combines mathematical rigor with practical engineering applications, making significant contributions to computational methods for wave propagation problems.

• Particle and high energy physics
• Nuclear and plasma physics
• Astronomical sciences
• +7 more

Charles Jui is a Professor of Physics & Astronomy at the University of Utah, where he has served since 1994, progressing from Assistant Professor to his current position. He is a senior member and acting Principal Investigator of the Utah group for the Telescope Array (TA) experiment, the largest ultra-high-energy cosmic ray observatory in the Northern Hemisphere. His work focuses on the study of ultrahigh energy cosmic rays and their origins. Dr. Jui received his educational training as follows: B.Sc. in Physics from the University of Ottawa (1985) MS in Physics from Stanford University (1987) Ph.D. in Particle Physics from Stanford University (1992) Dr. Jui's research primarily centers on ultrahigh energy cosmic rays (UHECRs), with significant contributions to the High Resolution Fly's Eye (HiRes) experiment that first observed the Greisen-Zatsepin-Kuzmin (GZK) cutoff in the cosmic ray spectrum. His current work focuses on the Telescope Array project studying UHECR composition, energy spectrum, and arrival direction distributions. He has also expanded into atmospheric physics, particularly the study of Terrestrial Gamma-ray Flashes (TGFs) and their relationship to lightning phenomena. His research bridges particle physics, astrophysics, and atmospheric science. Dr. Jui's extensive publication record reveals strong trends in cosmic ray energy spectrum analysis, mass composition studies, and the investigation of high-energy atmospheric phenomena. His recent work shows increasing focus on the connection between cosmic ray detection and thunderstorm processes, particularly through the observation of downward-directed Terrestrial Gamma-ray Flashes. The Telescope Array observatory continues to be the central instrument for his research, with recent expansions (TAx4) enhancing data collection capabilities for UHECR studies. Dr. Jui has received notable recognition for his academic contributions: Honors Professor at University of Utah Honors College (2013) Dr. Jui actively mentors graduate students, supervising both Master's and Ph.D. thesis research as evidenced by his teaching activities. His research is supported by substantial external funding, primarily from the National Science Foundation, with continuous grant support since 1994. Current grants focus on "Analysis of Data from the Telescope Array Observatory" and "Operation of the Telescope Array Observatory" through 2025. Earlier funding supported the High Resolution Fly's Eye experiment, the Telescope Array Low-energy Extension (TALE), and the TAx4 expansion project. Dr. Jui leads the University of Utah Cosmic Ray Physics Group, which operates as part of the international Telescope Array collaboration. This group manages the Utah component of the Telescope Array observatory, a facility covering approximately 700 km 2 in the west desert of Utah. The group also developed and maintains the Astrophysics Science Project Integrating Research and Education (ASPIRE), an online science education platform for middle school classrooms that receives thousands of daily visits worldwide. His team continues to expand cosmic ray research capabilities through the TAx4 project, which aims to increase data collection rates for UHECR studies.

Weihua GengView profile

Professor

• Numerical Methods
• Partial Differential Equations
• Integral Equations
• +4 more

Weihua Geng is a Professor and Director of Undergraduate Studies in the Department of Mathematics at Southern Methodist University. He earned his Ph.D. from Michigan State University in 2008 and completed postdoctoral research at the University of Michigan. His research focuses on numerical methods for partial differential equations and integral equations, with applications in structural and systems biology. Professor Geng develops advanced computational techniques including matched interface and boundary methods, boundary integral formulations, and treecode-accelerated algorithms for electrostatics and biomolecular simulations. His work combines mathematical modeling with high-performance computing to study protein interactions, chromatin folding, and circadian rhythm systems. His recent publications demonstrate a strong focus on enhancing Poisson-Boltzmann solvers through machine learning integration, parallel computing optimizations, and novel regularization techniques. These developments advance computational capabilities for electrostatic analysis in biological systems. Professor Geng has mentored numerous graduate and undergraduate researchers in computational mathematics. His collaborative projects involve researchers from multiple institutions including University of Michigan, Pacific Northwest National Laboratory, and University of Alabama.

Elvira KanebergView profile

Assistant Professor

• Humanitarian Logistics
• Emergency Preparedness
• Supply Chain Management
• +7 more

Elvira Kaneberg is an Assistant Professor of Business Administration at Jönköping International Business School (JIBS), part of Jönköping University. She is affiliated with the Media, Management and Transformation Centre (MMTC). Her research focuses on humanitarian logistics, safety and security, civil society, and strategic networks in emergency preparedness contexts, particularly in developed nations like Sweden and Mexico. She investigates how organizations and networks manage disruptions, allocate resources, and enhance responsiveness during crises. Education: PhD in Business Administration (2018), Jönköping University, JIBS Research Interests: Elvira's work emphasizes humanitarian aspects of supply chains, the integration of defense and civilian networks in emergencies, sustainability in logistics (e.g., hydrogen technology), and crisis information’s impact on customer behavior. She explores themes such as dynamic capabilities, disaster operations, and organizational resilience through a strategic lens. Publications Trends: Her articles and conference papers consistently address emergency preparedness challenges in developed countries, with a focus on Sweden. Recent work extends to global cases like Türkiye’s earthquake response and Mexico’s transportation sustainability. She collaborates widely, publishing in journals like Journal of Humanitarian Logistics and Supply Chain Management and International Journal of Hydrogen Energy . Awards & Grants: No scientific awards or grants are explicitly listed in the provided texts. Advising & Future Work: No formal advisees (students) are mentioned. Her research agenda includes advancing understanding of cross-sector collaboration in crises, leveraging hydrogen technology for sustainable supply chains, and refining dynamic capability frameworks for complex emergencies. Labs & Teams: Affiliated with the Media, Management and Transformation Centre (MMTC), which fosters interdisciplinary research on organizational transformation and societal challenges in business contexts.

Frank NeeseView profile

Professor

• Theoretical Chemistry
• Quantum Chemistry
• Molecular Spectroscopy
• +4 more

Prof. Frank Neese is the Managing Director of the Max Planck Institute for Coal Research and Honorary Professor at the University of Duisburg-Essen. His research focuses on molecular theory, quantum chemistry, and spectroscopy, with a strong emphasis on developing computational methods (e.g., the ORCA software) and their application to catalysis, electronic structure analysis, and transition metal systems. He holds numerous awards, including the Leibniz Prize and membership in prestigious academies. Education: Ph.D. (1997) and Habilitation (2001) in Bioinorganic Chemistry from the University of Konstanz. Postdoc at Stanford University (1997–1999). Previously served as Director of the Max Planck Institute for Chemical Energy Conversion (2011–2017) and Full Professor at the University of Bonn (2006–2011). Research interests include quantum chemical method development, computational studies of catalytic reactions (e.g., water oxidation in photosystem II), and advanced spectroscopic techniques like X-ray absorption/emission and Mößbauer spectroscopy. His ORCA software is widely used globally. Key contributions span over 600 publications, including work on correlated wavefunction methods (DLPNO-CCSD(T)), molecular magnetism, and heterogeneous catalysis. Awards include the Coulson Lectureship (2016), Davidson Lectureship (2015), and Leopoldina membership (2013). Grants and collaborations involve leadership in the Collaborative Research Centre TRR247 (catalysis) and international advisory roles (e.g., Faraday Discussions, GDCh committees). His lab hosts PhD students and postdocs in computational chemistry, spectroscopy, and materials science.

Duan ChenView profile

Associate Professor

• Numerical Linear Algebra
• Machine Learning Algorithms
• Partial Differential Equations
• +2 more

Dr. Duan Chen is Associate Professor of Mathematics at UNC Charlotte with affiliate appointment in the School of Data Sciences. His research spans randomized linear algebra, PDEs, and computational biology. Research Focus: Develops efficient algorithms for large-scale problems in machine learning, electromagnetics, and biological systems. Recent publications address kernel matrix compression and gene expression deconvolution. Grant support includes NIH R01GM148971 (2022-2025) and NSF DMS 1619713 (2016-2019).