Find Academic Supervisors

Michael J. NeilanView profile

Professor

• Computational and Applied Mathematics
• Numerical Analysis
• Scientific Computing
• +7 more

Michael J. Neilan is a Professor and Director of Graduate Studies in the Department of Mathematics at the University of Pittsburgh, part of the Dietrich School of Arts and Sciences. His research focuses on computational and applied mathematics, with emphasis on the finite element method, numerical approximations for nonlinear PDEs, and structure-preserving discretizations for incompressible fluid models and surface PDEs. Education: PhD in Mathematics, University of Tennessee, 2009 Research Interests: Computational PDEs, finite element methods, structure-preserving discretizations, incompressible fluid dynamics, surface PDEs, fully nonlinear PDEs (e.g., Monge-Ampère equations, optimal transport), and discontinuous Galerkin methods. Publications Trends: His work bridges theoretical analysis and computational implementation, with key contributions to mixed finite element methods, divergence-free formulations, and numerical techniques for nonlinear PDEs. Recent focus includes Monge-Ampère equations and stability analyses for fluid flow models. Editorial Roles: Managing Editor, Mathematics of Computation Associate Editor, IMA Journal of Numerical Analysis Associate Editor, Calcolo Associate Editor, Journal of Numerical Mathematics Professional Activities: Co-organizer of the Finite Element Circus, a leading conference in finite element theory and applications.

• Geotechnical Engineering
• Seismic Analysis
• Soil Mechanics
• +3 more

Youssef M A Hashash is the W. W. Grainger Chair and Professor in the Department of Civil and Environmental Engineering at the University of Illinois. His research focuses on geotechnical and earthquake engineering, with emphasis on seismic site response analysis, soil-structure interaction, and advanced computational methods like the Discrete Element Method (DEM). He has led projects on infrastructure resilience, including studies of buried water reservoirs, railway systems, and post-earthquake reconnaissance. Hashash has developed influential models for site amplification in Central and Eastern North America, contributing to seismic hazard assessments. His work integrates experimental centrifuge testing, numerical simulations, and field data. Notable contributions include guidelines for implementing NGA-East ground motion models and advancements in pore-water pressure generation models for liquefaction evaluation. Key Research Areas: Ground movement, seismic response, soil dynamics, and geotechnical data systems Major Projects: NGA-East Geotechnical Working Group, Beirut Explosion Analysis, and LA Metro Tunnel Projects Recipient of prestigious awards including the NAE Membership (2022), PECASE (2000), and Walter L. Huber Prize (2006), he collaborates internationally on earthquake engineering and geotechnical innovations. His lab develops tools like the DEEPSOIL software for nonlinear site response analysis and explores AI applications in geotechnical data interpretation.

Daniel Berwick-EvansView profile

Associate Professor

• Supersymmetric Quantum Field Theory
• Differential Geometry
• Algebraic Topology
• +3 more

Daniel Berwick-Evans is an Associate Professor in the Department of Mathematics at the University of Illinois at Urbana-Champaign (UIUC), part of the College of Liberal Arts & Sciences. He holds a PhD from UC Berkeley (2013) and previously served as a Szego Assistant Professor at Stanford University (2013–2015). His research bridges supersymmetric quantum field theories with differential geometry and algebraic topology. Education: PhD in Mathematics, UC Berkeley, 2013 Research Interests: Explores geometric and topological structures in supersymmetric field theories, including topics like elliptic cohomology, string structures, and localization techniques. His work integrates advanced tools from category theory, homotopy theory, and computational geometry. Recent Research Trends: Publications span theoretical frameworks (e.g., infinity-sheaves, modularity) and applied methods (e.g., discrete exterior calculus). Themes include higher categorical structures and their applications to physics. Awards & Grants: No awards explicitly listed in the text. Grants or funding details are not provided. Labs/Teams: No specific lab affiliations or research teams mentioned, though collaborations with co-authors like Eric Cliff and Arjun Nakade are evident from publications.

• Mechanobiology
• Computational Mechanics
• Finite Element Methods
• +7 more

Jose J. Muñoz is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Mathematics and the LACÀN research group. His work focuses on computational mechanics, mechanobiology, and inverse problems in biological systems. He holds a PhD in Aeronautics from Imperial College London (2004) and a dual degree in Mechanical Engineering from UPC and Civil Engineering from École Centrale Paris (1997). His research combines finite element methods, vertex models, and optimal control to study tissue morphogenesis, wound healing, and cancer mechanics. Current roles include leading the LACÀN research group and supervising PhD students in projects like optimal control of contractile systems and inverse mechanical analysis. Former students include Ashutosh Bijalwan and Cécilia Olivesi. He teaches Numerical Methods and Computational Mechanics at the UPC's Faculty of Mathematics and Statistics. His research interests span vertex and finite element modeling, cell rheology, and stability analysis of biological tissues. Notable contributions include models for epithelial wound healing, Drosophila embryo development, and mechanical oscillations in tissues. His work often integrates experimental data with computational frameworks to infer non-observable mechanical parameters.

Ed ChienView profile

Assistant Professor

• Computer Science
• Graphics
• Computational Engineering
• +3 more

Ed Chien is an Assistant Professor at Boston University's Department of Computer Science within the College of Arts & Sciences. He specializes in applying differential geometry and topology to graphics, computational engineering, and machine learning. Previously, he was a postdoctoral researcher at MIT's CSAIL and Bar-Ilan University. His work focuses on mathematically rigorous solutions to problems in geometric data processing and optimal transport. Education PhD in Mathematics, Rutgers University (2015) A.B. in Mathematics & Physics, Dartmouth College (2009) Research Highlights Dr. Chien's research includes fundamental studies on hexahedral mesh topology for Finite Element Modeling, optimal transport applications in machine learning, and singularity-free geometric algorithms. His work bridges theoretical mathematics with computational tools for engineering and graphics. Publications span top venues like NeurIPS, Eurographics, and SIGGRAPH, reflecting contributions to geometry processing and machine learning intersections. Program committee roles include Eurographics SGP (2019) and AAAI (2020). Awards & Recognition No specific awards listed, but notable service includes program committee memberships and peer-reviewed contributions to leading conferences. Grants & Advising Active in mentoring through academic positions but no explicit grant details provided. Research focuses on advancing geometric algorithms and optimal transport methodologies.

Ruming ZhangView profile

Academic

• Partial Differential Equations
• Inverse Problems
• Numerical Methods
• +4 more

Prof. Dr. Ruming Zhang is a Tenure-Track Professor at TU Berlin's Faculty II - Mathematics and Natural Sciences, leading the Analysis and Applications group since May 2023. He specializes in numerical methods for partial differential equations and inverse problems, with a focus on wave scattering and periodic structures. His research bridges theoretical analysis and computational techniques, addressing challenges in areas like photonic crystals and non-destructive testing. Education & Career: PhD in Mathematics (Chinese Academy of Sciences, 2014) Postdoctoral Researcher at Michigan Technological University Marie-Curie Fellow (University of Bremen, 2015-2018) Junior Group Leader at KIT (Karlsruhe Institute of Technology, 2018-2023) Research Interests: Analysis and numerical methods for PDEs, inverse scattering problems, waveguide analysis, periodic structures, and their applications in nanotechnology and engineering. His work emphasizes high-order numerical schemes and theoretical frameworks for ill-posed problems. Key Contributions: Development of nonuniform mesh methods for periodic surface scattering, high-order numerical techniques for bi-periodic structures, and monotonicity-based shape reconstruction in waveguides. His methods address challenges in computational wave physics and mathematical modeling. Awards: Richard-von-Mises Prize (GAMM, 2023) Marie-Curie Fellowship (EU FP7-PEOPLE, 2015-2017) Teaching & Mentorship: Offers courses on inverse problems, scattering theory, boundary element methods, and applied analysis. Advises students on thesis topics in mathematical theory for photonic crystals and wave propagation. Actively promotes interdisciplinary collaboration between mathematicians and engineers. Grants & Projects: DFG Grant (2019-2024): Higher-order methods for acoustic scattering in periodic structures Marie-Curie COFUND Fellowship (Bremen TRAC, 2015-2017) Labs/Teams: Leads the Analysis and Applications research group at TU Berlin, focusing on interdisciplinary projects combining mathematical theory with computational tools for real-world applications.

• Radiative heat transfer
• Computational thermal sciences
• Turbulence-radiation interaction
• +4 more

Pedro Jorge Martins Coelho is a Professor in the Mechanical Engineering Department at Instituto Superior Técnico, University of Lisbon, Portugal. His academic career spans several decades with a focus on advanced thermal sciences and computational methods. His research has significantly contributed to the understanding of radiative heat transfer phenomena in complex systems. Dr. Coelho's educational background includes a Ph.D. in Mechanical Engineering, which has provided the foundation for his extensive research in thermal sciences. His work demonstrates a strong theoretical foundation combined with practical applications across various engineering domains. His primary research interests encompass radiative heat transfer, turbulence-radiation interaction, combustion modeling, and numerical methods for thermal systems. Recent work has expanded into biomedical applications of thermal radiation, particularly in laser-tissue interactions for cancer detection and treatment. His publications reveal a consistent focus on developing and refining computational methods for solving complex heat transfer problems, with particular emphasis on the radiative transfer equation in various media and geometries. Analysis of his recent publications shows a clear evolution toward more complex and interdisciplinary applications, including biomedical thermal applications, advanced turbulence modeling, and thermal management of electrical systems. His work consistently bridges fundamental theoretical developments with practical engineering applications, particularly in combustion systems, energy recovery, and thermal management. Dr. Coelho has served on editorial boards for prestigious journals including Heat Transfer Research, Computational Thermal Sciences, and International Journal of Energy for a Clean Environment, demonstrating his standing in the thermal sciences community. His research collaborations span numerous institutions and researchers worldwide, as evidenced by his extensive publication record with various co-authors across different countries. He has also been involved in conference organization, serving as Associate Editor for major international heat transfer conferences.

Stefan GlockView profile

Assistant Professor

• Discrete Mathematics
• Extremal Combinatorics
• Probabilistic Combinatorics
• +4 more

Professor Stefan Glock is an Assistant Professor of Discrete Mathematics at the University of Passau's Faculty of Computer Science and Mathematics, a position he has held since September 2022. Prior to this appointment, he spent three years as a Junior Fellow at the Institute for Theoretical Studies at ETH Zurich, following the completion of his doctorate at the University of Birmingham. Stefan Glock received his mathematics education at Technische Universität Ilmenau from 2009 to 2014, then pursued his PhD at the University of Birmingham, which he completed in 2018. His doctoral dissertation, "Decompositions of Graphs and Hypergraphs," was the runner-up for the Richard-Rado-Preis 2018. Professor Glock's research focuses on discrete mathematical structures, with particular emphasis on their asymptotic properties. His work spans several interconnected fields of combinatorics: Extremal Combinatorics : Investigating the maximum or minimum possible size of mathematical structures satisfying certain properties Probabilistic Combinatorics : Applying probability theory to solve combinatorial problems Graph Theory : Studying properties of graphs and networks Ramsey Theory : Examining conditions under which order must appear in large structures Design Theory : Creating arrangements of elements satisfying specific balance properties Discrete Geometry : Analyzing geometric problems with discrete structures Analysis of Professor Glock's recent publications reveals a consistent focus on solving long-standing open problems in combinatorics using innovative methods that combine probabilistic techniques with structural insights. His work often bridges theoretical mathematics with applications in theoretical computer science, particularly in the analysis of algorithms and network structures. A significant portion of his research addresses fundamental questions about graph and hypergraph decompositions, which have implications for coding theory, cryptography, and network design. Professor Glock has received notable recognition for his contributions to mathematics: Runner-up for the Richard-Rado-Preis 2018 for his dissertation "Decompositions of Graphs and Hypergraphs" Awarded funding through the prestigious DFG Emmy Noether Programme in 2024 for his research group on "the interplay of structure and randomness in mathematics" As a faculty member at the University of Passau, Professor Glock leads the Discrete Mathematics research group and actively collaborates with mathematicians worldwide. He has established a strong research program that has attracted funding for academic visitors and supports multiple research projects. His approach to mathematical problems emphasizes developing new methods that have far-reaching implications beyond the specific problems being solved. Professor Glock's research group at the University of Passau focuses on the interplay between structure and randomness in discrete mathematics. The group maintains active collaborations with leading institutions including ETH Zurich, University of Birmingham, and various research centers across Europe. Through the DFG Emmy Noether Programme funding, his group is expanding its research on combinatorial structures and their applications.

• Ice Mechanics
• Fracture Mechanics
• Arctic Marine Technology
• +4 more

Jukka Tuhkuri is a Professor at Aalto University's Department of Energy and Mechanical Engineering, specializing in ice mechanics and arctic marine technology . He serves as Editor-in-Chief of Cold Regions Science and Technology and became an Honorary Professor at University College London (Department of Earth Sciences) in 2023. His work spans numerical simulations using the Discrete Element Method (DEM) and experimental research in the Aalto Ice and Wave Tank, with fieldwork in both Arctic and Antarctic regions. Research Focus : Understanding ice fracture mechanics, sea ice ridge formation, and ice-structure interaction processes. He investigates how global warming alters ice conditions and affects loads on ships/marine structures, addressing risks from increased Arctic shipping activity. Scientific Awards 2023 POAC Founders Lifetime Achievement Award Teacher of the Year 2003 Espoo Ambassador 2012 1996 Best Dissertation Stipend from Helsinki University of Technology Collaborative Impact : His research directly informs offshore wind engineering and Arctic risk management frameworks through publications like Challenges with sea ice action on structures for Offshore wind (2023) and A comprehensive approach to scenario-based risk management for Arctic waters (2022).

Sylvain LefebvreView profile

Researcher

• Computer Graphics
• Texture Synthesis
• Additive Manufacturing
• +4 more

Sylvain Lefebvre is a permanent researcher at INRIA (Institut National de Recherche en Informatique et en Automatique) in France, where he leads the MFX research team since 2018. Previously, he was part of the ALICE group at INRIA Nancy (2009-2018) and the REVES team in Sophia Antipolis (2006-2009). His career includes a postdoctoral position at Microsoft Research Seattle (2005) following his PhD at INRIA Rhones-Alpes under Fabrice Neyret. His educational background includes a PhD in Computer Graphics from Université Joseph Fourier (Grenoble) in 2005, preceded by a Master in Computer Graphics from INP Grenoble in 2001. His habilitation thesis focused on Runtime Texture Synthesis. Lefebvre's research centers on simplifying content creation for highly detailed patterns, structures, and shapes with applications spanning Computer Graphics to additive manufacturing. He develops fast, controllable by-example synthesis approaches that generate content while enforcing user-specified constraints. His work addresses computational challenges through novel data structures and algorithms optimized for GPUs and FPGAs, including his Silice programming language. The ERC-funded ShapeForge project (2012-2017) advanced shape generation for 3D printing, leading to the IceSL software for digital modeling and fabrication. Analysis of his 15 most recent publications reveals a strong focus on additive manufacturing optimization, with recurring themes in structural integrity, material efficiency, and geometric algorithms. His work bridges computer graphics theory with practical fabrication constraints, particularly in microstructure design, slicing techniques, and mechanical metamaterials. The interdisciplinary nature spans computer science, materials engineering, and robotics. EUROGRAPHICS Young Researcher Award (2010) ERC Starting Grant for ShapeForge project (2012) Lefebvre has advised over 25 PhD students and interns including Marco Freire, Thibault Tricard, and Jimmy Etienne. His ShapeForge project received significant ERC funding, supporting research in computational fabrication. He serves on numerous program committees including SIGGRAPH, Eurographics, and SIGGRAPH Asia, reflecting his leadership in the computer graphics community. As leader of the MFX team since 2018, Lefebvre directs research in computational fabrication, focusing on IceSL software development for 3D printing workflows. The team integrates computer graphics techniques with manufacturing constraints, developing tools that simplify complex object design and fabrication while addressing real-world challenges in material usage and structural integrity.

Christian EngwerView profile

Professor

• Partial Differential Equations
• Numerical Analysis
• Finite Element Methods
• +7 more

Christian Engwer is a full Professor at the University of Muenster in the Institute for Applied Mathematics, specializing in Analysis and Numerics. He leads the Engwer Group focused on Applications of Partial Differential Equations and is actively involved in the Cells in Motion initiative as a supervisor in the CiM-IMPRS Graduate Programme. His research centers on developing numerical methods for partial differential equations, particularly addressing challenges in complex geometries and multi-physics applications. He specializes in Unfitted Discontinuous Galerkin methods, which allow simulations on complex geometries without requiring domain-fitted meshes. His work spans porous media modeling, biological systems, and bioelectromagnetism applications, with significant contributions to EEG/MEG forward modeling in neuroscience. Analysis of his recent publications reveals a strong focus on model order reduction techniques, stabilized numerical schemes for cut-cell meshes, and applications in bioelectromagnetism. His work demonstrates a consistent trajectory toward developing robust, efficient numerical methods applicable to real-world problems in medical imaging and biological modeling, with increasing emphasis on high-performance computing implementations. Professor Engwer actively supervises doctoral students, with recent completions including Lukas Renelt (2025), Michael Wenske (2021), and Maria Carla Piastra (2019), among others working on topics related to numerical methods and biomedical applications. He leads several major research projects including BrainStorm: Highly Extensible Software for Advanced Electrophysiology and MEG/EEG Imaging (NIH-funded since 2019), multiple EXC 2044 Cluster of Excellence projects through 2025, and the InterKI interdisciplinary teaching program on machine learning and artificial intelligence. His group develops several important software packages including DUNE (Distributed and Unified Numerics Environment), duneuro (for bioelectromagnetism applications), and TPMC (Topology Preserving Marching Cubes). These tools support research in numerical methods and their applications to complex scientific problems.

Simon MasnouView profile

Professor

• Applied Mathematics
• Image Processing
• Shape Optimization
• +5 more

Simon Masnou is a Full Professor at Université Claude Bernard Lyon 1, affiliated with the Institut Camille Jordan (CNRS UMR 5208). He holds leadership roles as Head of the 'Applied Mathematics, Statistics' Master's degree and Head of the 'M2 Maths in Action' program. Previously, he served as Director of the Camille Jordan Institute (2018-2022). His research focuses on applied mathematics, image processing, shape optimization, and geometric measure theory, with contributions to variational models, geometric flows, and applications in computer vision and materials science. Education: PhD in Mathematics (1998, Paris Dauphine) and HDR (2008, Paris 6). Research projects include ANR STOIQUES (2024-2028), PEPR PDE-AI (2023-2028), and collaborations with industry on topics like defect prediction in aluminum production and high-dimensional data analysis. Teaching includes courses on linear algebra, optimization, and machine learning at undergraduate and graduate levels. Key contributions span phase field models, varifold-based surface approximation, and image inpainting. He supervises PhD students in geometric variational problems and computational methods. His work bridges theoretical mathematics with industrial challenges, addressing issues in materials science, medical imaging, and cultural heritage preservation.

• Machine Learning
• Data-Driven Modeling
• Numerical Solutions of Partial Differential Equations
• +5 more

Kailiang Wu is an Associate Professor at the Department of Mathematics, Southern University of Science and Technology (SUSTech), and holds concurrent roles at the Shenzhen International Center for Mathematics and National Center for Applied Mathematics Shenzhen. His research bridges Machine Learning and Computational Fluid Dynamics , focusing on High-Order Numerical Methods for Hyperbolic Conservation Laws and Relativistic Astrophysics . Education: Ph.D. in Mathematics (Peking University, 2016), B.Sc. in Mathematics and Statistics (Huazhong University of Science and Technology, 2011) His work develops Structure-Preserving Schemes for multidimensional PDEs, including Oscillation-Eliminating Discontinuous Galerkin (OEDG) and Geometric Quasilinearization (GQL) frameworks. These methods ensure positivity , divergence-free , and bound-preservation in simulations of relativistic flows and MHD systems. Recent publications emphasize Deep Learning applications in operator learning (e.g., DUE framework) and Data-Driven Modeling of unknown PDEs. His group has produced 20+ peer-reviewed articles in top journals (Math. Comp., SIAM J. Numer. Anal., JCP) since 2014. Honors: SUSTech President's Research Award (2025) World's Top 2% Scientist (2024) NSFC Major Program (2023, 0.7M CNY) Shenzhen Distinguished Young Scholar (2023, 4M CNY) National Excellent Young Scholar Program (2020, 2M CNY) Zhong Jiaqing Mathematics Award (2019) He advises 10+ graduate students and postdocs, with alumni securing academic positions at Sun Yat-sen University and HKUST. His lab collaborates on relativistic hydrodynamics , traffic models , and uncertainty quantification , supported by competitive funding.

Philippe MoireauView profile

Professor

• Inverse Problems
• Data Assimilation
• Partial Differential Equations
• +4 more

Philippe Moireau is a Full Professor in the Department of Applied Mathematics at École Polytechnique, where he is also affiliated with the Center for Applied Mathematics (CMAP). He serves as the head of the Inria Project-Team MΞDISIM (Mathematical and Mechanical Modeling with Data Interaction for Simulation in Medicine) and holds the distinguished position of Ingénieur Général of The Corps des Mines. His primary research focuses on inverse problems and data assimilation for partial differential equation models, with particular emphasis on: Observer-based methods from optimal control perspectives Stabilization approaches for evolution equations Numerical analysis of time-dependent control problems Digital twin applications in cardiovascular medicine Professor Moireau's publication portfolio demonstrates consistent focus on mathematical methods for physical systems, with recurring themes in: Data assimilation techniques for PDE-based models Numerical stabilization and discretization methods Cardiovascular biomechanics and hemodynamics Stochastic modeling of biological systems Epidemiological forecasting and control He leads the ANANKΞ project-team at Inria focused on Analysis And Numerics of physical-Knowledge-based Estimation. His educational contributions include lectures on data assimilation theory at CEMRACS and courses on mathematical modeling in cardiac biomechanics at Institut Polytechnique de Paris.

Mikael RinneView profile

Associate Professor

• Rock Fracture Mechanics
• Geotechnical Engineering
• Nuclear Waste Repository
• +3 more

Mikael Rinne is an Associate Professor in the Department of Civil Engineering at Aalto University's School of Engineering. His research focuses on rock fracture mechanics and its applications in various engineering contexts including nuclear waste repositories, geothermal energy systems, and underground construction. His expertise spans time-dependent rock failure mechanisms, fracture propagation models, and rock mechanics applications in energy storage and disposal systems. His work has direct applications in projects with Posiva Oy (nuclear waste repository), St1 Deepheat (geothermal energy), and mining operations with companies like First Quantum Minerals. Rinne's research integrates advanced numerical modeling with field applications, particularly in Finnish crystalline bedrock conditions. He has contributed significantly to understanding fracture initiation and propagation in rock masses under various stress conditions, with particular emphasis on long-term stability considerations for deep underground structures. His scholarly work demonstrates strong connections between theoretical fracture mechanics and practical engineering applications, with a focus on ensuring safety and reliability in rock engineering projects. His research has evolved from fundamental fracture mechanics studies to application-focused investigations addressing contemporary challenges in energy and waste management. Rinne has supervised doctoral research in rock mechanics and collaborates with researchers specializing in photogrammetry, virtual reality applications, and energy storage systems, creating a multidisciplinary approach to complex rock engineering problems.