Find Academic Supervisors

Hong WangView profile

Professor

• Numerical Analysis
• Differential Equations
• Fractional Calculus
• +3 more

Hong Wang is a Professor in the Department of Mathematics at the University of South Carolina, part of the McCausland College of Arts and Sciences. He specializes in numerical analysis and differential equations, with a focus on numerical methods for fractional and variable-order equations. His work addresses complex boundary conditions, optimal control, and scientific computing challenges in advection-diffusion systems. Education Ph.D. in Mathematics, University of Wyoming (1992) Research Interests His research emphasizes numerical approximation techniques for differential/integral equations, particularly fractional diffusion-wave equations, variable-order models, and stochastic systems. Key areas include finite element methods, spectral methods, and fast algorithms for solving high-dimensional and time-dependent problems. He explores applications in optimal control, viscoelasticity, and multi-scale modeling. Recent Work Trends Recent publications highlight advancements in fractional calculus applications, including variable-exponent diffusion, distributed-order equations, and stochastic fractional differential equations. His work often combines theoretical analysis with computational efficiency, addressing challenges like nonsmooth parameters and singular density functions. Grants and Advising No specific grants or advisees are listed, but his research collaborations span computational mathematics, applied physics, and engineering systems. Labs/Teams No dedicated lab or team is explicitly mentioned, though his research aligns with computational and applied mathematics groups at the University of South Carolina.

Amneet BhallaView profile

Associate Professor

• Fluid-Structure Interaction
• Multiphase Flows
• Aquatic Locomotion
• +7 more

Dr. Amneet Bhalla serves as an Associate Professor in the Department of Mechanical Engineering within the College of Engineering at San Diego State University (SDSU). His primary contact email is asbhalla@sdsu.edu, with office located in Engineering Building Room 323-G, and phone number (619) 594-2043. Education: Ph.D., Mechanical Engineering, Northwestern University (2013) M.S., Mechanical Engineering, Indian Institute of Technology Kharagpur (2009) B.S., Mechanical Engineering, Indian Institute of Technology Kharagpur (2004-2008) Postdoctoral Training: University of North Carolina at Chapel Hill (Mathematics Department) and Lawrence Berkeley National Laboratory (Computational Research Division) Research Interests: Dr. Bhalla develops advanced numerical methods and high-performance computing techniques for computational fluid dynamics (CFD) and fluid-structure interaction (FSI) problems. His work spans aquatic locomotion, renewable energy device modeling, multiphase flows, vehicular aerodynamics, and bioengineering applications. He creates mathematical models to interrogate underlying flow physics for engineering design optimization, with emphasis on open-source software development through the IBAMR library. Publication Trends: Recent publications (2023-2025) focus on robust numerical frameworks for multiphase flows with phase change, acoustic streaming, and fluid-structure interaction. Key themes include mass conservation in level set methods, adaptive mesh refinement, and solvers for non-isothermal gas-liquid-solid systems. Applications range from aquatic locomotion and renewable energy devices to microfluidics and biomedical flows, demonstrating commitment to both theoretical advances and practical engineering solutions. Scientific Awards: No awards mentioned in the provided text Advising and Grants: Dr. Bhalla secured an NSF CAREER award (2023) for "Consistent Continuum Formulation and Robust Numerical Modeling of Non-Isothermal Phase Changing Multiphase Flows". As PI of the CFD Lab, he mentors graduate students in computational mechanics, leveraging prior industrial experience at ExxonMobil Upstream Research Company. His research integrates industrial practicality with academic rigor through collaborations with national laboratories. Laboratory and Team: The Computational Fluid Dynamics and Flow Physics Laboratory (CFD Lab) develops the open-source IBAMR software—a distributed-memory parallel implementation of the immersed boundary method with adaptive mesh refinement. The lab emphasizes transparency, community engagement, and reproducibility, establishing cross-institutional collaborations while advancing computational methods for complex flow phenomena in engineering and biological systems.

Michael C. GurnisView profile

Professor

• Computational Geodynamics
• Mantle Dynamics
• Plate Tectonics
• +12 more

Michael C. Gurnis is the John E. and Hazel S. Smits Professor of Geophysics at the California Institute of Technology and has served as Director of the Seismological Laboratory (2009-2024) and Schmidt Academy for Software Engineering (2019-). With a B.S. from the University of Arizona (1982) and a Ph.D. from Australian National University (1987), he has held faculty positions at Caltech since 1994, becoming full Professor in 1996 and Smits Professor in 2005. His research spans computational geodynamics, mantle convection, and plate tectonics. California Institute of Technology Division of Geological and Planetary Sciences Seismological Laboratory Schmidt Academy for Software Engineering Research Interests: Gurnis focuses on computational geodynamics to link mantle convection to surface tectonics, using forward/inverse models to study plate motion dynamics , subduction zone mechanics , and deep mantle structures . His work bridges present-day geophysics with historical models through 4D Earth modeling , integrating seismic tomography, mineral physics, and paleogeography via the GPlates consortium. Recent projects include dynamic emergence of continents , plume interactions , and impact-driven subduction . Publications: Gurnis' research spans high-resolution global models of mantle flow, subduction zone rheology, and cratonic basin dynamics. Key trends include multi-physics coupling , Bayesian inversion techniques , and planetary-scale tectonic drivers . His 2024 papers address nonlinear mantle viscosity , shear zone weakening , and impact origins of tectonics . Scientific Awards: Gordon Bell Prize Finalist (2008, 2010) Springer CSE Prize (2011) for plate tectonics simulation work Advising: Mentors graduate students in computational geodynamics, including Jiaqi Fang, Erin Hightower, Yida Li, and Ojashvi Rautela. Collaborates with postdocs and international institutions like ETH-Zurich and National University of Mexico on mantle wedge dynamics and seismic geography. Labs & Teams: Leads the Seismological Laboratory and co-founded the Schmidt Academy for Software Engineering , driving innovations in geodynamic simulations and computational tools.

Mira SchedensackView profile

Assistant Professor

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +3 more

JProf. Dr. Mira Schedensack is a faculty member at the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science , University of Münster. Her expertise lies in Numerical Analysis, Machine Learning, and Scientific Computing , with a focus on finite element methods and numerical solutions for partial differential equations. Research Interests: Numerical methods for PDEs, mixed finite element formulations, adaptive algorithms, and thermo-optical interactions in computational physics. Teaching: Offers courses in Numerical Partial Differential Equations and Adaptive Finite Element Methods. Students: Mentors doctoral student Jonas Ketteler . Publications: Key contributions to non-conforming FEM, Stokes equations, and gradient elasticity.

Joscha GedickeView profile

Professor

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +3 more

Joscha Gedicke is a Professor at the Institute for Numerical Simulation (University of Bonn), specializing in Numerical Analysis , Finite Element Methods , and Scientific Computing . His research focuses on adaptive algorithms, error estimation, and computational methods for partial differential equations (PDEs) and optimal control problems. Contact: gedicke@ins.uni-bonn.de | +49 228 73-69835 Teaching: Lectures on Hybrid High-Order Methods (V5E1), Adaptive Finite Element Methods (S4E1), and Discontinuous Galerkin Methods (V5E5). Research Trends: Gedicke's work spans Numerical Methods for PDEs , Adaptive Finite Element Analysis , Mixed and Discontinuous Galerkin Formulations , and Error Estimation . His recent publications emphasize Virtual Element Methods and Robust Discretizations for magnetostatic and optimal control problems. Collaborative Networks: He collaborates with researchers in computational mathematics, including institutions like TU Munich, University of Milano-Bicocca, and the University of Bonn's research seminar on Mathematics of Computation .

• hp-Finite Element Method (hp-FEM)
• Fractional Diffusion Equations
• Boundary Element Method (BEM)
• +3 more

Prof. Jens Markus Melenk is a Professor at TU Wien's Faculty of Mathematics and Geoinformation, leading the Computational Mathematics Research Group (E101-02-1). His research focuses on advanced numerical methods for partial differential equations, with a strong emphasis on hp-FEM (hp-Finite Element Method), fractional diffusion equations, and wave propagation problems. He has contributed significantly to the development of robust and efficient algorithms for complex domains and heterogeneous media. Key research interests include the application of hp-FEM to fractional operators (e.g., integral fractional Laplacian), boundary element methods (BEM), and the analysis of wavenumber-explicit convergence for Maxwell's equations and elastic wave equations. His work bridges theoretical analysis and computational implementation, addressing challenges in multiscale problems and singular perturbations. Notable contributions: Exponential convergence of hp-FEM for fractional Laplacian problems, wavenumber-explicit error estimates for wave equations in heterogeneous media. Collaborations: Active involvement with researchers like Markus Faustmann, Christoph Schwab, and Dirk Praetorius. Supervised theses: Includes works on hp-FEM for fractional operators, RBF interpolation, and error estimators for elliptic PDEs. His research group develops and analyzes numerical schemes for challenging PDE scenarios, with applications in electromagnetism, acoustics, and computational mechanics.

• Computational Mathematics
• Applied Mathematics
• Partial Differential Equations
• +7 more

Frédéric Valentin is a Senior Researcher at the National Laboratory for Scientific Computing (LNCC) in Brazil, where he led the Department of Computational and Mathematical Methods from 2015 to 2021. He holds an INRIA International Chair (2018–2023) and served on the applied mathematics board of Brazil's National Science Foundation (CNPq) from 2017 to 2020. He currently leads the IPES Research Group and acts as the Brazilian Scientific Director for the Inria-Brasil partnership. His work focuses on computational and applied mathematics, particularly in developing innovative numerical methods for multiscale phenomena in engineering and life sciences. National Laboratory for Scientific Computing - LNCC (2015–2021) INRIA International Chair (2018–2023) Inria-Brasil Partnership (Scientific Director) Valentin's research involves partial differential equations, finite element methods, domain decomposition, and numerical analysis. He explores the integration of numerical algorithms with machine learning and high-performance computing, including applications to supercomputing projects like the Brazil-European Community's HPC4E initiative. His publications span over 60 high-impact journal articles and book chapters, primarily in computational mathematics and numerical analysis. He has co-advised over a dozen PhD students and postdoctoral researchers, contributing to international collaborations and advancing multiscale modeling techniques. Notably, he played a pivotal role in developing the Santos Dumont petaflop supercomputer at LNCC, the most advanced in Latin America.

• Partial Differential Equations
• High Performance Computing
• Discontinuous Galerkin Methods
• +3 more

Maurice S. Fabien is an Assistant Professor in the Department of Mathematics at the University of Wisconsin-Madison and a SIAM-MGB Early Career Fellow. In 2025, he will join MIT's Schwarzman College of Computing as an MLK Assistant Professor while on leave from UW-Madison. His research focuses on computational mathematics with specialization in partial differential equations, high-performance computing, and numerical methods including discontinuous Galerkin formulations and multigrid solvers. Research interests span: Development of structure-preserving discretizations for hyperbolic systems GPU-accelerated computational algorithms Hybridizable discontinuous Galerkin (HDG) frameworks Multiscale modeling in porous media and biomechanics Numerical analysis of nonlinear PDEs Publications demonstrate strong focus on: High-order methods for conservation laws Efficient solvers for elliptic/parabolic systems Applications in fluid dynamics and materials science GPU-based performance optimization Error analysis of energy-stable schemes Awards & Honors: SIAM-MGB Early Career Fellowship (2025) Research Team: Austin Anyanwu (Undergraduate) - Finite precision arithmetic Alexis Liu (Alumni) - GPU-accelerated elliptic solvers Patrick Li (Undergraduate) - GPU-based mesh refinement Neer Mehta (Alumni) - Adaptive mesh refinement algorithms Significant involvement since 2007 in STEM diversity initiatives focused on recruitment/retention of underrepresented groups in academia.

Michele RuggeriView profile

Assistant Professor

• Numerical Analysis
• Partial Differential Equations
• Materials Science
• +6 more

Michele Ruggeri is an Assistant Professor of Numerical Analysis (RTD-B) at the Department of Mathematics, University of Bologna, and an Honorary Lecturer at the University of Strathclyde, UK. His research focuses on numerical analysis of partial differential equations in materials science and engineering, including liquid crystal theory, micromagnetism, nonlinear elasticity, and spintronics. He is also active in developing numerical methods for uncertainty quantification and model order reduction. Education: PhD in Technical Mathematics (TU Wien, Austria), Laurea Magistrale in Mathematics (University of Pavia, Italy), and a Diploma in Science and Technology from Scuola Universitaria Superiore IUSS, Italy. He holds a Fellowship (FHEA) from Advance HE and a PGCert in Learning & Teaching in Higher Education from the University of Strathclyde. His work emphasizes finite element methods and their applications, with contributions to computational micromagnetics and software development (e.g., Commics). He is affiliated with the (AM)² Research Center on Applied Mathematics and ARCES at the University of Bologna. Awards: Fellowship (FHEA), Advance HE, UK. Key Projects: Development of numerical methods for micromagnetic simulations, uncertainty quantification in stochastic models. Contact: m.ruggeri@unibo.it

Miao LiView profile

Senior Lecturer

• Computational mechanics
• Numerical modelling
• Computational fluid dynamics
• +7 more

Miao Li is a Senior Lecturer in Engineering at Charles Sturt University, affiliated with the School of Computing, Mathematics and Engineering. She is a member of the Imaging and Sensing Research Group and the Sustainability in Engineering Research Group (SERG) at the Gulbali Research Institute. Her academic journey includes a PhD in Civil Engineering from Griffith University and earlier degrees in geological and engineering geology from Chinese institutions. Doctor of Philosophy (Civil Engineering) Master of Science (Geological Engineering) Bachelor of Science (Engineering Geology) Dr Li's research spans computational mechanics, numerical modeling, wastewater treatment, and non-linear structural dynamics, with a strong focus on sustainable engineering solutions. Her work contributes to UN Sustainable Development Goals through innovations in geotechnical systems, energy storage, and low-carbon construction. She has published extensively in top journals, with recent work focusing on adaptive mesh refinement, rock damage modeling, and frost heave in soils. Her recent publications highlight a trend toward high-fidelity numerical simulations and experimental validation in geotechnical and structural systems, particularly under extreme or cyclic loading conditions. She applies computational fluid dynamics and finite element methods to solve complex engineering problems in energy, infrastructure, and environmental resilience. Executive Dean Teaching Award (2024) Fellow of Advance HE (2025) Dr Li is a registered thesis supervisor and actively mentors students through research projects and industry placements. She has secured research recognition through awards and grants, and her work is frequently covered in press and media, reflecting its real-world impact. She participates in major international conferences in ocean, offshore, and structural engineering, and contributes to editorial and academic organizing roles. She leads research initiatives within the Imaging and Sensing Research Group and SERG, focusing on smart sensing, sustainable materials, and computational modeling for resilient infrastructure.

Lars BEEXView profile

Researcher

• Computational Mechanics of Solids
• Bayesian Inference
• Multiscale Methods
• +5 more

Lars BEEX is a Senior Research Scientist at the University of Luxembourg's Faculty of Science, Technology and Medicine, Department of Engineering. He holds the right to supervise PhD students and has directed five to completion. His research focuses on computational mechanics of solids, including Bayesian inference, multiscale methods, and quasicontinuum approaches, with applications to materials like textiles, foams, and medical devices. His academic journey includes a PhD from Eindhoven University of Technology (2008-2012), supervised by Marc Geers and Ron Peerlings, as well as MSc and BSc degrees from the same institution. **Research Interests:** - Computational mechanics of solids - Bayesian inference and uncertainty quantification - Multiscale modeling (quasicontinuum method) - Mechanical modeling of fibrous and discrete materials - Phase-field damage models - Contact mechanics and elastoplasticity **Awards:** - Biezeno Solid Mechanics Award 2013 (Best PhD thesis in solid mechanics, Netherlands) - Cum laude distinction for both MSc and BSc degrees **Industrial Collaborations:** - SISTO Armaturen - IEE - Kiswire International **Teaching:** - Numerical methods for continuous optimization - Courses for Computer Science, Mathematical Modelling, and Engineering students **Lab/Affiliations:** - Legato Team (part of the University of Luxembourg's engineering research cluster)

Giovanni RussoView profile

Professor

• Computational Fluid Dynamics
• Numerical Methods for Conservation Laws
• Numerical Methods for Kinetic Equations
• +5 more

Giovanni Russo is a Full Professor of Numerical Analysis at the Department of Mathematics and Computer Science, University of Catania, Italy . He coordinates the PhD program in Pure and Applied Mathematics and has been a visiting scholar at institutions including Courant Institute, University of California, Los Angeles, University of Michigan, and University of Bordeaux. His career spans over four decades across academia and research institutions. Education: PhD in Physics (1986, University of Catania), Laurea in Nuclear Engineering (1982, Politecnico di Milano). Research Interests: Russo specializes in Computational Fluid Dynamics , Numerical Methods for Conservation Laws , and Kinetic Equations . His work includes asymptotic preserving schemes , IMEX methods , semi-Lagrangian schemes , and high-order numerical techniques for PDEs with applications to fluid dynamics, plasma physics, and multiscale modeling. Scientific Trends: Recent publications focus on modeling epidemic dynamics , kinetic equations for inert mixtures , semi-Lagrangian methods , and uncertainty quantification in quantum systems. These works reflect his expertise in high-order numerical schemes , multiscale analysis , and applied mathematical modeling . Scientific Awards: CNR-NATO Fellowship (1987) Advising and Grants: Russo has supervised nine PhD students and served as Principal Investigator (PI) for major projects including MOSCOVID (modeling COVID-19) and ModCompShock (Horizon 2020 Marie Curie project). He has also organized international conferences like the 18th European Conference on Mathematics for Industry with 370 participants. Labs and Teams: Russo collaborates with research groups at the University of Catania and has been a visiting researcher at Courant Institute, University of Michigan, and GSSI L’Aquila. He contributes to journals as an editor and reviewer, including SIAM Journal of Numerical Analysis and Journal of Computational Physics .

• Electromagnetism
• Neutron Scattering
• Fluid Mechanics
• +5 more

Patrick Ciarlet is a Professor at ENSTA Paris, part of the Institut Polytechnique de Paris, where he is a member of the POEMS (Wave Propagation, Mathematical Study and Simulation) research team within the Applied Mathematics Unit (UMA). He also serves as the responsible person for the Master's degree in Mathematics and Applications at the Institut Polytechnique de Paris. His research is structured around two main poles in applied mathematics: Modeling in electromagnetism, neutron scattering and fluid mechanics Numerical analysis of PDEs and scientific computing Professor Ciarlet's work demonstrates a strong focus on mathematical methods for solving problems in wave propagation and electromagnetism, particularly with challenging materials such as metamaterials that have sign-changing coefficients. His research spans theoretical mathematical analysis and practical numerical implementations, with applications in nuclear engineering (neutron diffusion equations) and plasma physics. His publication record shows consistent contributions to finite element methods, with particular expertise in stability analysis, error estimation, and specialized approaches like T-coercivity for handling problems with sign-changing coefficients. He has also developed educational resources through lecture notes on Maxwell's equations and variational methods for non-coercive problems. As an academic leader, he plays a significant role in shaping mathematics education at the graduate level through his responsibility for the Master's degree program in Mathematics and Applications, contributing to ENSTA Paris's research focus on cutting-edge areas with applications in sustainable energy, transportation, and defense.

Nicholas KevlahanView profile

Professor

• Applied Mathematics
• Computational Fluid Dynamics
• Geophysical Fluid Dynamics
• +5 more

Dr. Nicholas Kevlahan is a Professor in the Department of Mathematics and Statistics at McMaster University. He holds a BSc in Physics from the University of British Columbia (1985–1989), a PhD in Applied Mathematics from the University of Cambridge (1990–1994), and completed a Marie Curie postdoctoral fellowship at École Normale Supérieure in Paris (1998). He has held visiting positions at institutions including Université Grenoble-Alpes, INRIA, and the University of Cambridge. His research focuses on advanced computational and mathematical methods for fluid dynamics, including the development of the WAVETRISK code for adaptive climate modelling, data assimilation techniques, fluid-structure interaction, and compressive sampling. His work integrates interdisciplinary approaches across applied mathematics, geophysics, and engineering. Key research areas include geophysical fluid dynamics, numerical analysis, partial differential equations, and wavelet-based methods. He has published extensively on topics such as ocean circulation models, turbulence simulation, and adaptive numerical methods. Dr. Kevlahan teaches courses in numerical methods, differential equations, asymptotic analysis, and mathematical physics. He mentors a diverse group of PhD, MSc, and BSc students in his active research laboratory.

• Scientific Computing
• Computational Fluid Dynamics
• Finite Element Methods
• +3 more

Johan Jansson is an Associate Professor in Scientific Computing at KTH Royal Institute of Technology and BCAM (Basque Center for Applied Mathematics). He leads research in predictive Direct FEM Simulation (DFS) for aerodynamics and multiphase flows, and co-founded Icarus Digital Math as CEO. His work includes the FEniCS open-source finite element software project and MOOC-HPFEM educational initiatives. He holds roles as Director of the Center for Digital Math and collaborates internationally in computational science. Research focuses on high-performance computing (HPC), fluid-structure interaction (FSI), biomedical modeling, and renewable energy systems. Notable contributions include adaptive FEM frameworks for turbulent flow, vocal fold simulations, and wave energy converter modeling. His work bridges academic research with industrial applications, leveraging FEniCS-HPC and Unicorn solvers. Key achievements include election to the IVA Royal Swedish Academy of Sciences 100-list and securing the Severo Ochoa Center of Excellence Award. He has pioneered open-source tools like SimTek and contributed to major projects like the Salter Sink and vocal production modeling. Teaching responsibilities include courses on database technology, computational fluid mechanics, and research methodology. He actively engages in large-scale simulation projects involving marine energy, cardiac ablation protocols, and aerodynamic optimization.