Ali Feizmohammadi is an Assistant Professor, Teaching Stream (LTA) in the Department of Mathematics at the University of Toronto Mississauga, affiliated with the Mathematical and Computational Sciences division. His research focuses on inverse problems, partial differential equations, and geometric analysis. He holds a position emphasizing teaching excellence within the university's framework. His work addresses advanced mathematical challenges such as coefficient identification in subdiffusion equations, fractional Laplacian problems on Riemannian manifolds, and nonlinear elliptic equations on manifolds. Recent articles highlight contributions to the Calderón problem in various contexts, wave equation control, and spacetime finite element methods. No scientific awards or grants are explicitly listed in the provided information. He has not yet listed advisees in the available data. His research trends emphasize rigorous mathematical analysis of inverse problems in both classical and fractional PDE frameworks, with applications to geometric and control-theoretic questions. Dr. Feizmohammadi's work spans theoretical advancements in inverse problems, numerical methods for control systems, and the interplay between differential geometry and PDEs. His contributions address both fundamental theory and applied methodologies in mathematical physics and engineering.
Dond Asha Kisan is an Assistant Professor at the School of Mathematics , Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM). His research focuses on numerical analysis and computational mathematics , particularly in finite element methods for partial differential equations. He can be contacted at ashadond@iisertvm.ac.in or via phone at +91 (0)471-2778247. PhD : Mathematics, Indian Institute of Technology Bombay M.Sc. : Mathematics, K.T.H.M. College, Nashik Kisan's research spans adaptive finite element methods , stabilized formulations for convection-diffusion problems , and optimal control governed by Stokes equations . His work includes convergence analysis, nonconforming discretizations, and hybrid numerical schemes. Recent publications (2023-2025) address stochastic modeling in liquid crystal physics, advanced WENO schemes, and adaptive algorithms for control problems. Scientific Awards : No explicit awards mentioned in the data, though he held prestigious postdoctoral fellowships including National Post-Doctoral Fellowship and NBHM Post-Doctoral Fellowship. Kisan has extensive teaching experience , including MATLAB workshops and undergraduate course assistantships. He has presented at major international conferences like ICIAM and Hyperbolic Problems, demonstrating global engagement in computational mathematics.
Bernardo Cockburn is a Distinguished McKnight University Professor in the School of Mathematics at the University of Minnesota. He has been a faculty member since 1987, progressing from Assistant Professor to Associate Professor in 1992, and achieving full Professor status in 1997. He also held positions as an Affiliate Professor at the University of Delaware (2019-2020) and Chair Professor of Mathematics at King Fahd University of Petroleum and Minerals in Saudi Arabia (2012-2014). Education: Ph.D. from University of Chicago (1986), Doctorat de 3eme Cycle from University of Paris VI/INRIA (1983), Masters and Licenciatura from Universidad Nacional de Ingenieria in Lima, Peru Research Focus: Numerical methods for partial differential equations, particularly discontinuous Galerkin methods Cockburn's research primarily centers on the devising and analysis of efficient methods for numerically solving linear and nonlinear partial differential equations . His most significant contribution has been in the development and analysis of discontinuous Galerkin methods , particularly the hybridizable discontinuous Galerkin (HDG) methods which he pioneered. His work spans error estimation for hyperbolic problems, continuous dependence for Hamilton-Jacobi equations, and numerous applications across fluid dynamics, structural mechanics, and electromagnetics. He has developed theoretical frameworks for superconvergence properties and created practical algorithms for a wide range of engineering applications. Analysis of his recent publications reveals a strong focus on hybridizable discontinuous Galerkin methods , with significant contributions to superconvergence theory, error estimation, and applications to diverse physical problems including Stokes flow, linear elasticity, Timoshenko beams, and convection-diffusion problems. His work demonstrates a clear trajectory from theoretical foundations to practical implementation, with increasing emphasis on curved domains, adaptive methods, and coupling techniques between different numerical approaches. Doctor Honoris Causa from Universidad Nacional de Ingenieria, Lima, Peru (2013) Invited Speaker at the International Congress of Mathematicians, Numerical Analysis Section (2010) Distinguished McKnight University Professor, University of Minnesota (2007) Cockburn has supervised an impressive 23 PhD students throughout his career, many of whom have gone on to become professors at major universities worldwide including the University of Puerto Rico, Purdue University, and University of Concepcion in Chile. His advisees have produced significant research in discontinuous Galerkin methods, particularly in applications to structural mechanics, fluid dynamics, and Hamilton-Jacobi equations. His research has been supported by numerous grants from the National Science Foundation and other funding agencies, enabling extensive collaboration with researchers across the United States and internationally. Cockburn leads a vibrant research group focused on computational mathematics, with particular emphasis on developing and analyzing discontinuous Galerkin methods. His work has fostered significant collaboration between mathematicians and engineers, with applications spanning aerospace, civil engineering, and materials science. The research group maintains strong connections with institutions worldwide, including regular collaborations with researchers in Peru, Chile, and Europe, reflecting Cockburn's international background and influence.
Endre Süli is a Professor of Numerical Analysis at the University of Oxford, affiliated with Worcester College and Linacre College. He has held various academic roles since 1985, including Fellowships and Tutorships in Mathematics. University Education: B.Sc. in Mathematics, University of Belgrade (1974-1978) M.Sc. in Mathematics, University of Belgrade (1978-1980) Ph.D. in Mathematics, University of Belgrade (1985) M.A., University of Oxford (1985) British Council Visiting Student, Reading University and University of Oxford (1983/84) Süli's research focuses on numerical methods for partial differential equations (PDEs), with expertise in finite element methods, adaptive algorithms, error control, and computational modeling of fractures and non-Newtonian fluids. His work bridges mathematical theory and practical applications in fluid dynamics and material science. His recent publications emphasize finite element approximations, nonlinear PDEs, and stochastic models for polymer dynamics. Themes include multiscale methods, tensor-sparsity for high-dimensional problems, and compressible flow simulations. Scientific Awards: Fellow of the Royal Society (2021) London Mathematical Society Naylor Prize and Lectureship (2021) Pro Urbe Prize, City of Subotica (2021) SIAM Fellow (2016) Member, Academia Europaea (2020) Foreign Member, Serbian National Academy of Sciences and Arts (2009) IMA Service Award (2011) Fellow, European Academy of Sciences (EurASc) (2010) Fellow, Institute of Mathematics and its Applications (2007) London Mathematical Society/New Zealand Mathematical Society Forder Lecturer (2015) Professor Hospitus, Charles University, Prague (2012) Distinguished Visiting Chair Professor, Shanghai Jiao Tong University (2013) Invited Speaker, International Congress of Mathematicians, Madrid (2006) Süli has supervised numerous research projects and held visiting appointments globally. His contributions to numerical analysis span foundational work on error estimation, nonlinear stability, and advanced computational frameworks for complex physical systems.
Dr. Fengyan Li is a Professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute (RPI). She holds a PhD in Applied Mathematics from Brown University (2004) and previously held a postdoc at the University of South Carolina. Her research focuses on numerical analysis and scientific computing, particularly discontinuous Galerkin methods for applications in wave propagation, fluid dynamics, plasma physics, and nonlinear optics. She has received prestigious awards including the NSF-CAREER Award (2009) and Alfred P. Sloan Fellowship (2008). Dr. Li serves on editorial boards of journals like SIAM Journal of Numerical Analysis and IMA Journal of Numerical Analysis. Education: PhD in Applied Mathematics (Brown University, 2004); MS & BS in Computational Mathematics (Peking University, 2000 & 1997). Research interests emphasize multi-scale simulations, reduced-order modeling, and high-order methods. Her work addresses challenges in kinetic transport, nonlinear optics, and plasma dynamics. She has delivered plenary talks at major conferences, including ICOSAHOM (2018) and NAHOMCon (2022). Professional service includes leadership roles in the Association for Women in Mathematics (AWM), co-organizing symposiums, and mentoring. She is a 2025 AWM Fellow and advises RPI's AWM Student Chapter.
Beth Anne Bennett is a Senior Lecturer in the Department of Mechanical Engineering at Yale University. Her research focuses on computational methods for solving complex fluid dynamics and combustion problems, particularly involving adaptive grid refinement techniques for nonlinear PDEs. She holds a Ph.D. from Yale University, where her doctoral work centered on developing efficient numerical algorithms for multidimensional combustion phenomena. Her research interests include laminar combustion, fluid dynamics, heat transfer, and solidification processes. She has pioneered solution-adaptive gridding techniques like Local Rectangular Refinement (LRR) for both nonreacting and reacting flows, with applications to steady and unsteady multidimensional systems. Bennett has been recognized with the National Science Foundation ADVANCE Fellows Award (2002-2006). Her publications span computational studies of ethanol/dimethyl ether blending effects in flames, oxygen-enhanced methane flames, and axisymmetric coflow flames. She actively contributes to professional societies including The Combustion Institute, ASME, SIAM, ASEE, and SWE. Her work integrates computational innovation with experimental validation, addressing challenges in parallelization, sparse matrix treatments, and algorithm optimization for convection-diffusion problems. Bennett's research bridges fundamental numerical methods and applied combustion engineering, advancing both theoretical frameworks and practical applications in energy systems.
Professor M. Grae Worster is a renowned academic in fluid dynamics and geophysics, affiliated with the University of Cambridge's Department of Applied Mathematics and Theoretical Physics (DAMTP) within the Faculty of Mathematics. He holds the title of Professor and specializes in fluid mechanics, solidification processes, and geophysical flows. His research focuses on buoyancy-driven flows, magma dynamics, sea ice evolution, and phase-change phenomena in porous media. Worster earned his Ph.D. in 1983 from Cambridge University with a thesis on "Convective Flow Problems in Geological Fluid Mechanics." His work bridges theoretical and experimental approaches, addressing complex fluid dynamics in natural systems like magma chambers, lava lakes, and sea ice formation. Key areas of expertise include mushy-layer convection, premelting dynamics, and viscous gravity currents. His research interests span fluid mechanics, solidification physics, and environmental fluid dynamics, with a strong emphasis on geophysical applications. Recent studies explore hydrogel mechanics, grounding-line dynamics in ice sheets, and thermal regelation in colloidal systems. He has authored over 140 peer-reviewed articles and co-edited influential works like Perspectives in Fluid Dynamics and Understanding Fluid Flow . Worster's contributions to fluid dynamics include groundbreaking studies on sea ice dynamics, where he developed models for brine drainage and ice growth mechanisms. His work on solidification processes in alloys and colloidal suspensions has advanced materials science and geophysics. Collaborations with experimentalists ensure his theoretical models are grounded in empirical validation.
Lina von Sydow is a Professor in Computational Science at Uppsala University's Department of Information Technology. She serves as Section Dean for the Mathematical-Computer Science Section since July 2023. Her academic journey includes becoming an Associate Professor in 2000, Senior Lecturer since 1997, and leading the Department of Information Technology from 2018 to 2023. PhD in Domain Decomposition Methods (1995, Uppsala University) Postdoctoral Fellow at Oxford University (1996-1997) Her research spans computational science with dual focuses on Computational Finance and Ice Sheet Modeling . In finance, she develops numerical methods for option pricing using PDEs, radial basis functions, and stochastic volatility models. In climate science, she contributes to ice sheet dynamics through full Stokes models and adaptive time-stepping approaches, particularly in simulating grounding line migration. Recent publications (2025) address gender disparities in IT education, including comparative analysis of admission trends and intervention studies to boost female enrollment. Earlier works (2020-2015) focus on high-order finite difference methods for financial derivatives, BENCHOP benchmarking projects, and preconditioning techniques for PDEs. Scientific awards include Excellent Teacher (2013) She actively collaborates on educational reforms, co-authoring studies like Gender-aware course reform in Scientific Computing (2013). Her leadership roles include Head of Department (2018-2023) and Section Dean (2023-present), influencing academic governance and interdisciplinary research. Labs and teams: Works with Uppsala University's Computational Science group, Elmer/ICE project collaborators (e.g., Per Lötstedt, Gong Cheng), and international partners in numerical finance and climate modeling.
Seulip Lee is a Norbert Wiener Assistant Professor in the Department of Mathematics at Tufts University, School of Arts and Sciences. His research focuses on scientific computing, numerical analysis, and computational fluid dynamics, with an emphasis on multiphysics simulations using finite element methods. He holds a PhD in Mathematics from the University of California, Irvine (2021), and degrees from Yonsei University (M.Sc., 2015; B.Sc., 2013). Lee’s work bridges theoretical analysis and computational experimentation, addressing challenges in partial differential equations and optimization. He has published extensively on enriched Galerkin methods and numerical techniques for fluid dynamics. Teaching responsibilities include MATH 51 (Differential Equations) and MATH 125 (Numerical Analysis). His courses emphasize both analytical rigor and computational implementation, using tools like MATLAB for practical problem-solving. Professional experience includes a Limited Term Assistant Professor role at the University of Georgia (2021–2024), with research collaborations under mentors like Xiaozhe Hu and James Adler. His lab and team activities focus on advancing numerical algorithms for complex physical systems.
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.
Santiago Badia is a Full Professor of Computational Science and Engineering at Universitat Politècnica de Catalunya (UPC), holding an adjoint researcher position at the International Center for Numerical Methods in Engineering (CIMNE). He leads the Large Scale Scientific Computing (LSSC) group at CIMNE, focusing on finite element methods, numerical analysis, and high-performance computing. His research emphasizes fluid dynamics, multiphysics problems, and scalable solvers for large-scale systems. Previously, he worked at Politecnico di Milano and Sandia National Labs. He developed the FEMPAR software framework, a parallel finite element tool for PDE simulations, achieving landmark scalability (e.g., 60 billion unknowns on 458,672 cores). FEMPAR is recognized in the High-Q Club of European codes. His expertise includes discontinuous Galerkin methods, XFEM, and domain decomposition preconditioners. Research interests span metal additive manufacturing, superconductor devices, and nuclear engineering applications. Awards include FEMPAR's High-Q Club inclusion. He advises PhD and MSc students (e.g., Jesus Bonilla, Eric Neiva, Marc Olm) and has open positions in postdoc/PhD levels. His team includes researchers like Javier Principe and Alberto Martín. Ongoing projects involve advancing parallel algorithms, multiphysics simulations, and software scalability for exascale computing.
Jannik Matuschke is an Associate Professor of Operations Management at the Department of Decision Sciences and Information Management, KU Leuven (Belgium). He holds affiliations with the KU Leuven Institute for Artificial Intelligence (Leuven.AI) and the KU Leuven Institute for Mobility (LIM). His research focuses on combinatorial optimization, algorithm design, game theory, robustness under uncertainty, and applications in logistics and production systems. Education: PhD in Mathematics (2013) from TU Berlin, advised by Martin Skutella and Britta Peis Postdoctoral positions at Universidad de Chile (2014) and TU München (2016–2018) DAAD P.R.I.M.E. fellowship at University of Rome 'Tor Vergata' (2015) Research Themes: Design of resilient infrastructures using multi-stage optimization Stochastic and robust project scheduling Applications in logistics network analytics and congestion modeling Recent Contributions: Recipient of the 2024 Meritorious Service Reward from Operations Research Journal Co-chair of WAOA 2025 and editorial roles at Omega, Operations Research Letters, and OR Spectrum Active in international workshops like FRICO 2025 and the Santiago Summer Workshop on Combinatorial Optimization Academic Leadership: Supervises 5 current PhD/postdoc researchers across logistics and optimization Coordinates the Master's Thesis program in Production and Logistics at KU Leuven Manages research projects on robust infrastructure design (2022–present) and stochastic scheduling (2020–present)
Shaowei Wang is a Professor in the Department of Engineering Mechanics at the School of Civil Engineering, Shandong University, China. He holds a Ph.D. in Mathematics (2007) from Shandong University and completed post-doctoral research in fluid mechanics (2009) at Peking University. His research focuses on heat and mass transfer in porous media , fluid mechanics , and mathematical methods in mechanics . His recent publications highlight advancements in electro-osmotic flows, oscillatory non-Newtonian fluid dynamics, and stability analysis of bioconvection in porous media. Key themes include modeling viscoelastic fluids (Oldroyd-B, Maxwell), fractional calculus applications, and microchannel transport phenomena. First Prize in Natural Science of the Ministry of Education (2015) Second Prize of Natural Science Award of Shandong Province (2015) Du Qinghua Young Scholar Award (2016) Wang serves as Director of Shandong Society of Mechanics and editorial board member of multiple journals. His work bridges theoretical fluid dynamics with practical applications in microfluidics and geophysical systems.
Engin Danis is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of Missouri, Columbia. His research focuses on advancing computational methods for complex fluid dynamics, particularly in high-speed and hypersonic regimes. With expertise in high-order discretizations, turbulence modeling, and large-scale numerical simulation, he develops algorithms that capture intricate flow phenomena with high fidelity and computational efficiency. Dr. Danis's research interests include: High-order numerical methods Hypersonic flows Turbulence modeling Tensor networks for fluid dynamics Compressible flow simulation His publication record demonstrates a strong trend toward applying tensor network methodologies to solve challenging problems in computational fluid dynamics. This innovative approach enables more efficient computation of complex fluid phenomena while maintaining high accuracy, particularly for high-speed and hypersonic flows. His research spans both fundamental numerical method development and practical applications in aerospace engineering. Professional affiliations: American Institute of Aeronautics and Astronautics American Physical Society Society for Industrial and Applied Mathematics Prior to joining the University of Missouri, Dr. Danis was a postdoctoral research associate at Los Alamos National Laboratory where he pioneered tensor-train-based solvers for partial differential equations. He also has industry experience from GE Aviation, where he contributed to high-fidelity simulation tools for aircraft engine thermal-fluid systems.
Prof. Lukas Einkemmer is a faculty member at the University of Innsbruck, holding a position in the Institute of Mathematics. He specializes in numerical analysis, plasma physics, and high-performance computing. His work focuses on developing advanced numerical methods for solving complex kinetic equations and PDEs, with applications in plasma simulation and computational fluid dynamics. Education: He earned a PhD in applied mathematics (2014) and MSc in physics (2013) from the University of Innsbruck, alongside BSc in applied mathematics (2010). He completed research stays at UC Merced and holds notable academic awards, including the SciCADE New Talent Award (2015) and participation in the Heidelberg Laureate Forum (2013). Research & Teaching: His research includes exponential integrators, dynamical low-rank methods, and semi-Lagrangian discontinuous Galerkin schemes. He teaches numerical methods, PDEs, and computational courses at both undergraduate and graduate levels. He also leads training programs in parallel computing (OpenMP/MPI) at the University’s Research Center for High-Performance Computing. Publications & Grants: Over 70 peer-reviewed articles in journals like J. Comput. Phys. and SIAM J. Sci. Comput. , focusing on numerical algorithms and their applications. He has secured grants from FWF and other agencies, advancing methods for plasma physics and kinetic theory. Awards & Recognition: Multiple honors, including the Oberwolfach Leibniz Graduate Student award (2014) and sustained scholarship support for academic excellence.
