Find Academic Supervisors

• Numerical Methods
• Computational Engineering
• Multiphysics Simulation Techniques
• +2 more

Prof. Thomas Weiland is a Full Professor of Computational Electromagnetics at the Technische Universität Darmstadt since 1989. His research focuses on numerical methods, computational engineering, and multiphysics simulation techniques, particularly in accelerator physics and beam dynamics. He holds a Dr.-Ing. from TU Darmstadt and has held postdoctoral and research positions at CERN and TU Darmstadt. His work includes pioneering contributions to electromagnetic field simulations, including advanced finite element methods, discontinuous Galerkin techniques, and boundary element approaches. Education highlights include his Diplom in Electrical Engineering from TU Darmstadt (1975) and a Habilitation in Experimental Physics from the University of Hamburg (1984). His research spans computational electromagnetics, accelerator physics, and numerical methods for electromagnetic field problems. Notable areas of innovation include transparent boundary conditions, eigenmode calculations, and high-performance simulation frameworks for rotating systems and particle accelerators. His publications emphasize advancements in electromagnetic simulation tools, such as the MagPEEC method and Trefftz-discontinuous Galerkin approaches. Collaborative projects include modeling RF photoinjectors for light sources and electrohydrodynamic droplet dynamics. Technical contributions also extend to wake field analysis in particle accelerators and SAR distribution studies in bioelectromagnetics. Research interests further include multiphysics coupling (thermal-electromagnetic effects in surge arresters), stochastic modeling of electromagnetic systems, and field-circuit co-simulation techniques. His work addresses challenges in large-scale eigenvalue problems, adaptive mesh optimization, and high-precision numerical methods for complex geometries.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +3 more

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

Wang-Hung TseView profile

Researcher

• Applied Mathematics
• Pattern Formation
• Computational Biology
• +3 more

Dr. Wang-Hung Tse is a researcher and faculty member in the Department of Mathematics at Trinity Western University (TWU), affiliated with the Faculty of Natural & Applied Sciences. He holds a PhD from the University of British Columbia (2016) and an MPhil from The Chinese University of Hong Kong (2009). His primary research focuses on pattern formation in biological systems, nonlinear dynamics, and the integration of machine learning with experimental data analysis. He has contributed to studies on crime hotspot dynamics, biological morphogenesis, and satellite data assimilation. Dr. Tse has taught courses including Differential Equations (MATH 321), Calculus III (MATH 223), and Business Mathematics (MATH 101). His interdisciplinary research emphasizes reproducible outcomes and involves agent-based modeling, computational simulations, and mathematical analysis of complex systems. Recent work includes collaborations on sea surface temperature modeling and crime pattern stability. His research methodologies span applied mathematical analysis of singularly perturbed systems, numerical bifurcation methods, and high-performance computing for large-scale simulations. Key contributions include studies on localized stripe stability in reaction-diffusion systems and the interplay between Gaussian curvature and pattern formation on Riemannian manifolds.

Massimiliano PontilView profile

Professor

• Machine Learning
• Dynamical Systems
• Optimal Transport
• +5 more

Massimiliano Pontil is a Professor at University College London's Department of Computer Science within the Faculty of Mathematical & Physical Sciences, with additional affiliation at the Italian Institute of Technology in Genoa. He leads cutting-edge research at the intersection of machine learning and dynamical systems theory. His research focuses on developing theoretical frameworks for learning transfer operators of stochastic dynamical systems using kernel methods. Pontil's work provides rigorous spectral learning bounds and addresses representation learning challenges in modeling time-evolving phenomena. His research spans both theoretical foundations and practical applications in data-driven science and engineering. Pontil's recent publications reveal a strong emphasis on Koopman operator theory, with particular attention to spectral analysis, error bounds, and efficient algorithms for large-scale dynamical systems. His work connects kernel methods with dynamical systems theory to create mathematically grounded approaches for forecasting and understanding temporal phenomena. His scientific contributions include multiple publications in top-tier venues including NeurIPS and ICLR, with recent work on invariant representations, long-term forecasting, and randomized algorithms for operator regression. Pontil collaborates extensively with researchers including Vladimir Kostic, Karim Lounici, and Pietro Novelli, leading a productive research group in this specialized area of machine learning theory.

Mark IwenView profile

Professor

• Computational Harmonic Analysis
• Mathematical Data Science
• Signal Processing
• +12 more

Mark Iwen is a Full Professor with a dual appointment in the Department of Mathematics and the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University. His research bridges theoretical mathematics and computational science, focusing on high-dimensional data analysis, signal processing, and numerical algorithms. Research Interests: His work centers on computational harmonic analysis, mathematical data science, and algorithms for large-scale data. Key areas include sparse spectral methods, phase retrieval, tensor decomposition, compressive sensing, sublinear-time algorithms, and Johnson-Lindenstrauss embeddings. He develops provably accurate and efficient algorithms for problems in high dimensions, such as solving PDEs, function approximation, and manifold learning. Publications and Trends: His recent publications (2023–2025) emphasize sparse and low-rank tensor methods, phase retrieval, and high-dimensional PDE solvers. There is a strong focus on algorithmic efficiency, provable guarantees, and applications in imaging and data science. Many works involve collaborative efforts and include open-source code. Scientific Awards: Scholarship established in honor of Benjamin (Ben) Segal, reflecting his mentorship and academic impact Advising and Grants: He advises numerous students, including undergraduates and graduate researchers, many of whom are co-authors on publications and code repositories. While specific grants are not listed, his sustained research output and collaborative projects suggest active funding. He co-founded the One World MINDS Seminar, demonstrating leadership in the academic community. Labs and Teams: He leads a research group focused on algorithm development for high-dimensional problems, with strong ties to computational mathematics and data science. His team develops and shares code for sparse FFTs, phase retrieval, tensor methods, and PDE solvers, indicating a collaborative and open research culture.

Gianna Del CorsoView profile

Associate Professor

• Quantum Computing
• Numerical Linear Algebra
• Spectral Analysis
• +3 more

Gianna Del Corso is an Associate Professor at the Department of Computer Science, University of Pisa. Her research interests span Quantum Computing, Numerical Linear Algebra, and Spectral Analysis. She has advised PhD students in quantum computing topics such as quantum algorithms and machine learning. Her teaching includes courses on Numerical Calculus, Parallel Scientific Computing, and Introduction to Quantum Computing, delivered across multiple academic years at the University of Pisa. She has also contributed to advanced courses in the PhD program in Computer Science. Her research focuses on quantum algorithms for machine learning, quantum walks, eigenvalue computation of structured matrices, and spectral techniques for web analysis. Recent work includes applications of quantum k-means clustering and variance estimation subroutines. Her publications span quantum computing, numerical methods, and scientific citation models. Her articles highlight contributions to PageRank computation, matrix factorization for recommendation systems, and analysis of citation-based research evaluation models. She has actively engaged in international conferences, presenting on topics such as orthogonal iterations for nonlinear eigenvalue problems and quantum hitting time algorithms. Labs/Teams: Research conducted within the Department of Computer Science at the University of Pisa, focusing on quantum computing and numerical methods.

• Numerical Analysis
• High-Dimensional Problems
• Low-Rank Matrix and Tensor Techniques
• +4 more

Bart Vandereycken is an Associate Professor in the Mathematics Department at the University of Geneva, specializing in numerical analysis and scientific computing. His research focuses on large-scale and high-dimensional problems solved using low-rank matrix and tensor techniques, with applications in numerical linear algebra, optimization, and nonlinear eigenvalue problems. He previously held positions as an instructor at Princeton University and postdoctoral researcher at EPF Lausanne and ETH Zurich, and earned his PhD from KU Leuven in 2010. His research interests include Riemannian optimization algorithms, multilevel preconditioning, and machine learning applications. He serves as an associate editor for SIAM Journal on Matrix Analysis and Applications and Linear Algebra and its Applications . Bart organizes the Numerical Analysis seminar with colleagues, and advises students interested in numerical analysis or numerical linear algebra. Recent work emphasizes convexity structures in matrix decompositions, robust preconditioning techniques, and scalable low-rank algorithms for high-dimensional PDEs. His 2024–2025 publications explore advancements in Riemannian optimization schemes, subspace iteration methods, and distributed computing applications of matrix decompositions. Key themes include improving convergence guarantees and developing geodesic-based optimization frameworks for challenging numerical problems.

Fei XueView profile

Associate Professor

• Scientific computing
• Numerical linear algebra

Fei Xue is Associate Professor in the Department of Mathematical Sciences at Clemson University. His research specializes in numerical linear algebra and scientific computing, particularly developing efficient algorithms for large-scale eigenvalue problems and linear systems. Research focuses on preconditioned iterative methods, Krylov subspace techniques, and numerical solutions to partial differential equations. Recent work has developed novel algorithms for eigenvalue computation, including the Block Preconditioned Harmonic Projection method for nonlinear eigenproblems and a Chebyshev-based Locally Optimal Block Preconditioned Conjugate Gradient method. Applications span quantum physics, materials science, and computational finance. Dr. Xue has received sustained NSF funding since 2011 for his work on eigenvalue solvers and matrix computations. Teaching responsibilities include graduate courses in numerical linear algebra and scientific computing.

Sicheng HeView profile

Assistant Professor

• Multidisciplinary Design Optimization (MDO)
• Computational Fluid Dynamics (CFD)
• Computational Aeroelasticity
• +8 more

Sicheng He is an Assistant Professor in the Department of Mechanical, Aerospace and Biomedical Engineering at the University of Tennessee, Knoxville since August 2023. He previously held postdoctoral research positions at MIT (2022-2023) and the University of Michigan, Ann Arbor (2020-2021), where he focused on computational aeroelasticity and multidisciplinary design optimization (MDO). Education: PhD in Aerospace Engineering (2020), University of Michigan, Ann Arbor MS in Applied Math (2015), University of Michigan, Ann Arbor MSE in Aerospace Engineering (2015), University of Michigan, Ann Arbor BSE in Aerospace Engineering (2013) and Mechanical Engineering (2013), Joint Institute, Shanghai Jiao Tong University Research Interests: Sicheng He specializes in optimization techniques for large-scale dynamical systems, with emphasis on PDE-constrained optimization, mixed integer programming, and machine learning-based surrogate models. His work includes applications in wind energy systems, aircraft design (particularly transonic buffet analysis), and stability-constrained optimization frameworks. Publication Trends: His research output focuses on computational physics and optimization, with recurring themes in aerospace engineering, wind energy, and multidisciplinary design. Key methodologies include algorithmic differentiation, time-spectral solvers, and gradient-enhanced neural networks for aerodynamic shape optimization. Awards: Best student paper in MDO, 2nd place, AIAA Aviation Conference 2019 Professional Involvement: Active in AIAA and ICAO CAEP Working Group 3. Developed educational workshops on figure visualization and Git version control at MIT. Serves as reviewer for journals including AIAA Journal, Structural and Multidisciplinary Optimization, and International Journal for Numerical Methods in Fluids.

Gert LubeView profile

Professor

• Numerical Methods for Partial Differential Equations
• Computational Fluid Dynamics
• Turbulence Modeling
• +2 more

Prof. Dr. Gert Lube is a faculty member at the Institute for Numerical and Applied Mathematics (NAM) within the Faculty of Mathematics and Computer Science at Georg-August-University Göttingen. His research focuses on numerical methods for partial differential equations , with emphasis on stabilized finite element methods , turbulence modeling , and magnetohydrodynamics (MHD) . Workshops Organized : Calibration of Viscosity Models for Turbulent Flows (2010), Variational Multiscale Methods (2008), Local Projection Stabilization (2008), BAIL Conferences. His academic contributions include 15+ publications since 2010 on topics like Navier-Stokes simulations , LES/VMS methods , stabilized FEM , and FEM-BEM coupling . Collaborations span institutions like TU Graz, Saarbruecken University, and DLR Göttingen. Key Research Areas : Finite Element Methods, Turbulence Modeling, MHD, Incompressible Flows, Singularly Perturbed Problems, Parallel Computing. Advisees include PhD candidates working on topics such as non-isothermal flows , mass conservation , hybrid RANS/LES , and domain decomposition .

Renata SotirovView profile

Professor

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

Renata Sotirov is a Full Professor at Tilburg University's Department of Econometrics & Operations Research within the Tilburg School of Economics and Management. She holds a PhD in Mathematics and has held academic positions including Associate Professor (2009–2014) and Assistant Professor (2007–2009). Her research focuses on optimization, operations research, and semidefinite programming with applications in combinatorial optimization and discrete mathematics. She has supervised PhD students such as Dr. Ning Ma and Dr. Uwe Truetsch and collaborates internationally, including with institutions like the University of Melbourne and McMaster University. Her work includes pioneering contributions to semidefinite relaxation techniques, integer SDPs, and algorithmic optimization. Recent projects involve the Chvátal-Gomory procedure for integer SDPs and complex cut polytopes. She has served as a research coordinator, director of graduate studies, and treasurer for her department. Her expertise spans teaching operations research methods, combinatorial optimization, and quantitative business analytics.

Ilse C.F. IpsenView profile

Professor

• Numerical Linear Algebra
• Randomized Algorithms
• Probabilistic Numerical Analysis
• +8 more

Ilse C.F. Ipsen is a Distinguished Professor in the Department of Mathematics at North Carolina State University, with a joint appointment in the Department of Statistics. She is a Fellow of the American Mathematical Society and the Society for Industrial and Applied Mathematics (SIAM), and delivered the Olga Taussky-Todd Lecture at ICIAM 2023. Institution: North Carolina State University School: College of Sciences Department: Department of Mathematics Rank: Professor Email: ipsen@ncsu.edu Her research centers on numerical linear algebra, randomized algorithms, and probabilistic numerical analysis. She has made significant contributions to error analysis in floating-point computation, matrix perturbation theory, and the development of randomized methods for large-scale data problems. Her work bridges theoretical foundations with practical applications in data science, quantum physics, and computational statistics. She is the author of the textbook Numerical Matrix Analysis (SIAM, 2009) and serves as Editor-in-Chief of the SIAM Book Series on Data Science. The recent publications highlight a strong trend toward probabilistic numerical methods, uncertainty quantification, and randomized matrix algorithms. Themes include error analysis for inner products and summation, Bayesian solvers for linear systems, and scalable methods for trace and diagonal estimation. Applications span biobank data analysis, quantum physics, and PageRank computation. Scientific Awards: Fellow of the American Mathematical Society (AMS) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Olga Taussky-Todd Lecture, ICIAM 2023 She has advised multiple graduate and undergraduate students, including J.T. Holodnak, T. Wentworth, and R. Rehman. Her collaborative research has been supported by interdisciplinary grants, particularly in quantum physics and data-intensive computing. She has served on editorial boards of leading journals such as SIAM Review , Acta Numerica , and Numerische Mathematik , and chaired SIAM activity groups, reflecting her leadership in the applied mathematics community. She is actively involved in the development of software and algorithms, including the MATLAB package kappa_SQ for randomized sampling, and has contributed to discussions on communicating applied mathematics through case studies.

Hao LiView profile

Research Fellow

• Topology Optimization
• Mechanical Engineering
• Thermal Systems
• +2 more

Hao Li is a MSCA Postdoc Fellow and Visiting Scholar at LIP6 , affiliated with the University of Southern Denmark in the Department of Mechanical Engineering . His research focuses on advanced computational methods for topology optimization in thermal, fluid, and structural engineering systems. Education: Not explicitly stated in the provided text. Current Projects: Leading EU-funded research on heat exchanger design using multiscale models and machine learning. Dr. Li's work spans multiscale topology optimization, level-set methods, and fluid-structure interaction, with applications in microchannel cooling, compliant mechanisms, and biodegradable composites. His recent publications highlight advancements in 3D conjugate heat transfer, adaptive meshing, and eigenfrequency maximization. The trends in his research output (2017–2025) emphasize thermal-fluid systems , high-resolution structural optimization , and manufacturable composite designs . Notable subfields include triply periodic minimal surfaces for cooling channels, nonlinear buckling analysis, and phasor-based dehomogenization techniques. Teaching & Supervision: Currently supervising projects on topology optimization frameworks for heat sinks and high heat flux cooling. His past projects (2018–2023) include research on piezoelectric transducers and thermal-fluid system design. Labs & Collaborations: Collaborates with institutions in Japan and France, focusing on experimental validation and industrial applications. His network includes partnerships with researchers in structural mechanics, computational fluid dynamics, and additive manufacturing.

• Fluid Engineering
• Computational Fluid Dynamics
• Isogeometric Analysis
• +5 more

Yuto Otoguro is an Associate Professor (non-tenure-track) at Waseda University's Faculty of Science and Engineering, Department of Modern Mechanical Engineering, and also serves as a Researcher at the Institute for Frontier Fluid-Structure Interaction Analysis. He earned his PhD, M.Eng, and B.Eng in Modern Mechanical Engineering from Waseda University in 2018, 2016, and 2014 respectively. His academic career focuses on advanced computational methods for fluid dynamics and structural mechanics. Dr. Otoguro's primary research interests lie in Fluid Engineering, Computational Fluid Dynamics (CFD), and Isogeometric Analysis (IGA). His work centers on developing and applying space-time computational methods with isogeometric discretization for complex flow problems. He has made significant contributions to element length calculation in B-spline and T-spline meshes, stabilization parameters for variational multiscale methods, and general-purpose NURBS mesh generation techniques for complex geometries. His research has important applications in turbomachinery, wind turbine analysis, fluid-structure interaction, and computational aerodynamics. His publication record shows a strong focus on computational methods development, with particular emphasis on isogeometric analysis applications. His work on local-length-scale calculation in complex geometries, hyperelastic shell models, and space-time computational flow analysis represents cutting-edge research in computational mechanics. His papers frequently address challenges in representing complex geometries and handling moving boundaries in fluid flow simulations. Among his scientific achievements, Dr. Otoguro received the JSCES 20th Anniversary Scholarship Award. He has been actively involved in research projects including 'On new developments of Isogeometric Analysis (IGA) for highly accurate and efficient fracture mechanics analysis' funded by the Japan Society for the Promotion of Science, and 'Compressible-flow engine-valve analysis with response motion and contact' as part of the Early-Career Scientists program. As an educator, Dr. Otoguro teaches multiple courses at Waseda University including Fluid Dynamics, Engineering Thermodynamics, Material Mechanics, and Mechanical Engineering Laboratory courses. He has also organized workshops on isogeometric analysis to promote this emerging computational method within Japan's research community. His academic service includes participation in the Team for Advanced Flow Simulation and Modeling (T*AFSM), where he contributes to advancing computational methods for fluid-structure interaction problems.

Mohammad El SmailyView profile

Associate Professor

• Partial Differential Equations
• Nonlinear Analysis
• Reaction-Diffusion Models
• +7 more

Dr. Mohammad El Smaily is an Associate Professor in the Department of Mathematics and Statistics at the University of Northern British Columbia (UNBC), part of the Faculty of Science and Engineering. He holds a PhD in Mathematics from Aix-Marseille Université (2008). Before joining UNBC, he held postdoctoral positions at the University of British Columbia (PIMS postdoc), Carnegie Mellon University, and the University of Toronto (NSERC postdoc). His research focuses on Partial Differential Equations (PDEs) , dynamical systems , and their applications in population dynamics , mathematical biology , and ecology . Key areas include reaction-diffusion models, integro-difference equations, and the analysis of traveling waves in heterogeneous environments. Recent work explores mixed local/nonlocal operators , nonlinear advection-diffusion systems , and free boundary problems in ecological contexts. His publications span topics like front propagation in shear flows, Wolbachia infection models, and spectral analysis of nonlocal operators. Dr. El Smaily currently advises students in MSc and PhD Mathematics programs at UNBC. His research has been supported by collaborations with institutions globally, including the University of New Brunswick and INRAE (France). Publications (selected): Over 20 peer-reviewed articles, including work on KPP equations, integro-difference systems, and predator-prey dynamics with free boundaries. Full list available on Google Scholar .