جست‌وجوی استاد و پژوهشگر

• Pure Mathematics
• Model Theory
• Algebra
• +۴ مورد دیگر

David Evans is a Professor of Pure Mathematics at the Department of Mathematics, Imperial College London, within the Faculty of Natural Sciences. His research focuses on Model Theory and its interactions with Algebra and Combinatorics, particularly stability theory, Hrushovski constructions, and infinite permutation groups. He has advised numerous research students, including D. G. D. Gray, Reinhold Konnerth, and Marco Ferreira, among others. His work spans topics like automorphism groups of metric spaces, Ramsey properties of sparse graphs, and algebraic structures arising from combinatorial constructions. Evans has contributed significantly to understanding homogeneity in combinatorial structures and the simplicity of automorphism groups derived from Hrushovski's constructions. His affiliations include the Algebra and Algebraic Combinatorics research group and the Pure Mathematics section at Imperial. Key contributions include advancing the theory of omega-categorical structures, Ramsey theory applications, and algorithmic group theory.

• Probability Theory
• Stochastic Partial Differential Equations
• Mathematical Physics
• +۵ مورد دیگر

Martin Hairer is a Royal Society Research Professor of Pure Mathematics at EPFL and Imperial College London. His research focuses on stochastic analysis, stochastic partial differential equations (SPDEs), and their applications in mathematical physics and statistical mechanics. He holds academic positions at both institutions, contributing to the Faculty of Natural Sciences. Hairer earned his PhD in Physics from the University of Geneva (2001) and has held prestigious roles such as the Regius Professor of Mathematics at the University of Warwick (2014–2017) and the Chair in Probability and Stochastic Analysis at Imperial College London (2017–2022). His work bridges analysis, probability, and physics, with groundbreaking contributions to the theory of regularity structures for solving singular SPDEs. His research interests include renormalization techniques for stochastic PDEs, ergodic theory, and the analysis of critical phenomena in statistical mechanics. He has advised over 20 PhD students and postdoctoral researchers, many of whom now hold academic positions globally. Awarded the Fields Medal (2014), the highest honor in mathematics, Hairer has also received the Breakthrough Prize (2020), Onsager Medal (2024), and numerous fellowships. His contributions to stochastic analysis have advanced understanding of complex systems, including fluid dynamics and quantum field theory. Hairer’s professional engagements include editorial roles at leading journals (e.g., Inventiones Mathematicae , Communications in Mathematical Physics ), and leadership in international mathematical committees, such as the Abel Prize committee and the EMS Prize committee.

• Solid Mechanics
• Materials Science
• Metamaterials Design
• +۵ مورد دیگر

Prof. Dennis M. Kochmann is a Full Professor and Head of the Department of Mechanical and Process Engineering at ETH Zurich. His research focuses on theoretical, computational, and experimental solid mechanics, particularly linking microstructure to macroscopic material properties. He leads the Mechanics & Materials Lab, exploring architected metamaterials, instabilities across scales, and advanced modeling techniques like phase field and atomistic-to-continuum coupling. His work spans materials such as metals, ceramics, and active composites, with applications in additive manufacturing and energy absorption. Kochmann holds a Dr.-Ing. from Ruhr-University Bochum and has held academic roles at Caltech before joining ETH in 2017. His awards include the NSF CAREER Award, ERC Consolidator Grant, and multiple society recognitions. He serves on editorial boards and contributes to academic governance roles at ETH and international societies.

Dennis Kochmannمشاهده پروفایل

استاد

• Solid Mechanics
• Materials Science
• Metamaterials
• +۴ مورد دیگر

Prof. Dennis Kochmann is a Full Professor of Mechanics and Materials and Head of the Department of Mechanical and Process Engineering at ETH Zurich. His research focuses on theoretical, computational, and experimental solid mechanics, particularly the link between microstructure and macroscopic material properties. Key interests include metamaterials, architected materials, and instabilities across scales. His group develops advanced computational techniques like phase-field modeling and atomistic-to-continuum coupling. Education: He holds a Dipl.-Ing. from Ruhr-University Bochum (2005), a Master's from UW-Madison (2006), and a Dr.-Ing. from Ruhr-University Bochum (2009). He has held academic roles at Caltech before joining ETH Zurich in 2017. Research emphasizes architected materials with controllable properties, such as nano/micro-truss networks and active composites. Applications span additive manufacturing, energy absorption, and nonlinear dynamics. His work bridges mechanics, materials science, and computational methods. Awards: NSF CAREER Award, ERC Consolidator Grant, Bureau Prize (IUTAM), Richard von Mises Prize (GAMM) Leadership: Head of Department (since 2023), former Head of Institute of Mechanical Systems (2018-2020) Editorial Roles: Associate Editor for Applied Mechanics Reviews , Archive of Applied Mechanics , and others Labs/Teams: Leads the Mechanics & Materials Lab, focusing on computational and experimental material studies. Collaborates on projects involving AI-driven materials design and metamaterials optimization.

• Inverse problems
• Uncertainty quantification
• Numerical Analysis
• +۶ مورد دیگر

Zhang Wenlong is an Assistant Professor in the Department of Mathematics at the Southern University of Science and Technology (SUSTech). His research focuses on inverse problems, uncertainty quantification, numerical analysis, and their applications in fields such as electrical impedance tomography and partial differential equations. He holds a B.Sc. in Information and Computational Science from Nanjing University (2007-2011), a Ph.D. in Computational Mathematics from the Chinese Academy of Sciences (2011-2014), and a dual Ph.D. from the DMA at École Normale Supérieure and ETH Zürich under Professors Zhiming Chen and Habib Ammari. Prior to his current role, he was a postdoc at SUSTech (2017–2019). His research areas include inverse problems, hybrid imaging methods, homogenization theory, and numerical methods for PDEs. He has contributed to advancements in convexification techniques for inverse problems, stochastic convergence analysis, and data-driven model reduction methods. His work often intersects applied mathematics, computational physics, and biomedical imaging. Zhang has taught courses in linear algebra, ordinary differential equations, and finite element methods at SUSTech since 2017. His publications span top journals such as SIAM Journal on Imaging Sciences, Inverse Problems, and Journal of Computational Physics, reflecting his expertise in interdisciplinary computational and applied mathematics.

Florian Fepponمشاهده پروفایل

استادیار

• Topology Optimization
• Optimal Design
• Homogenization
• +۶ مورد دیگر

Florian Feppon is an Assistant Professor in the NUMA division of the Department of Computer Science at KU Leuven. His research spans topology optimization, optimal design, homogenization, multiscale modeling, uncertainty quantification, data assimilation, and asymptotic analysis of partial differential equations, with applications in 3D printing, heat exchangers, photonics, and microfluidic devices. He completed his PhD in 2019 at École polytechnique under Grégoire Allaire and Charles Dapogny, funded by Safran, and a Master’s thesis in 2016 at MIT with Pierre Lermusiaux. From 2020 to 2022, he was a Hermann-Weyl postdoctoral instructor at ETH Zürich. His research interests include developing algorithms for nonlinear constrained optimization, such as the Null Space Optimizer , and analyzing subwavelength resonances in acoustic metamaterials, fluid-structure interactions, and homogenization of porous media. Scientific awards : Prix Paul Caseau 2019 (EDF, Institut de France), ECCOMAS PhD award 2019, and SMAI-GAMNI thesis award 2021. Grants : FWO Junior Research Project (2024-2028) on topology optimization of large-scale systems under uncertainty. Students : Javier Bevia-Ripoll and Dries Toebat, working on topology optimization with local and global constraints. Software : Lead developer of the open-source Null Space Optimizer for constrained optimization.

Wenjia Jingمشاهده پروفایل

دانشیار

• Analysis
• Partial Differential Equations
• Stochastic Homogenization
• +۳ مورد دیگر

Wenjia Jing is an Associate Professor at the Yau Mathematical Sciences Center, Tsinghua University in Beijing, China. He joined Tsinghua University as an Assistant Professor in 2016 and was promoted to Associate Professor in 2022. He also serves as an Adjunct faculty at the Yanqi Lake Beijing Institute of Mathematical Sciences and Applications since July 2021. His academic journey began with a B.S. in Theoretical and Applied Mechanics from Peking University in 2006, followed by a Ph.D. in Applied Mathematics from Columbia University in 2011 under the supervision of Guillaume Bal. His postdoctoral training included positions as an L.E. Dickson Instructor at the University of Chicago (2013-2016) mentored by Panagiotis E. Souganidis, and as a Postdoctoral Researcher at Ecole Normale Supérieure Paris (2011-2013) mentored by Habib Ammari and Josselin Garnier. Dr. Jing's research focuses on Analysis and Partial Differential Equations, with specific expertise in Stochastic Homogenization and Quantitative Estimates, Waves Propagation in Random Media, and other areas of Applied Analysis. His work bridges theoretical mathematics with applications in physics and engineering, particularly in understanding complex phenomena in heterogeneous and random media. His research has been supported by multiple grants including NSFC (China) grants and The Recruitment Program of Global Experts of China. Dr. Jing has made significant contributions to the field through his publications in top mathematical journals. His work shows a consistent focus on homogenization theory, partial differential equations in complex media, and quantitative analysis of mathematical models. He has developed novel approaches to understanding wave propagation, front dynamics, and effective properties in heterogeneous media. NSFC (China) Grant No. 11871300 (co PI, 2019-2022) The Recruitment Program of Global Experts of China (2019-2021) NSFC (China) Grant No. 11701314 (PI, 2018-2020) NSF (USA) Grant DMS-1515150 (PI, 2015-2016) Dr. Jing is also active in academic service, serving as an Associate editor for Frontiers of Applied Mathematics by AIMS. He has taught a variety of courses at both undergraduate and graduate levels, including Real Analysis, Linear Algebra, and specialized topics courses on homogenization theory and waves in random media.

• Applied Mathematics
• Partial Differential Equations (PDEs)
• Wave Propagation
• +۲ مورد دیگر

Sanghyeon Yu is an Associate Professor in the Department of Mathematics at Korea University, South Korea. His research focuses on applied mathematics, particularly in partial differential equations, wave propagation, metamaterials, and inverse problems. Education: PhD in Mathematics from KAIST, postdoctoral research at ETH Zurich, and a Bachelor's degree in Physics from Korea University. His work explores subwavelength resonators, plasmonics, and elastic stress concentrations through mathematical modeling and computational analysis. He has contributed to understanding wave interactions in bubbly crystals, PT-symmetric metamaterials, and stress localization in composite materials. Recent publications highlight his expertise in super-resolution imaging, homogenization theory, and topological wave phenomena. Collaborations with Prof. Habib Ammari and others dominate his research output. He teaches and conducts research at Korea University's Asan Science Building, with contact details including the email sanghyeon_yu@korea.ac.kr .

Antonio Attiliمشاهده پروفایل

مدرس

• Computational Fluid Dynamics
• Combustion
• Turbulence
• +۳ مورد دیگر

Dr. Antonio Attili is a Lecturer in Computational Reactive Flows at the University of Edinburgh's School of Engineering, specializing in Mechanical Engineering. His research focuses on turbulence modeling, combustion dynamics, and numerical simulation techniques. Key areas include direct numerical simulation (DNS) of reactive flows, large-eddy simulation (LES) frameworks, and data-driven models using machine learning approaches like GANs. He leads the Multiscale Thermofluids Research group, advancing methods for compressing DNS data and optimizing turbulence-chemistry interactions. Dr. Attili's work emphasizes thermodiffusive instabilities in hydrogen flames, soot formation in turbulent non-premixed flames, and adversarial training for super-resolution turbulence reconstruction. Recent studies explore Reynolds number effects on combustion models and the extrapolation capabilities of neural network-based approaches. His contributions bridge fundamental fluid mechanics with applied combustion engineering, addressing challenges in gas turbine emissions and aero-engine combustor design. Notable innovations include the ZipGAN framework for DNS data compression and predictive GAN models for turbulent combustion regimes. He collaborates internationally on DNS-driven analysis of flamelet models and soot-turbulence interactions. While no formal awards are cited, his prolific publication record (117+ outputs) underscores his influence in high-impact journals like Combustion and Flame and Flow, Turbulence and Combustion.

Karim Achouriمشاهده پروفایل

استادیار

• Electromagnetism
• Photonics
• Metasurfaces
• +۳ مورد دیگر

Karim Achouri is an Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering (STI) and the Advanced Electromagnetism and Photonics Laboratory (LEAP) . He also serves in the Teaching Service of STI (SMT-ENS). His research focuses on metasurfaces, electromagnetic modeling, and optical computing. Location: ELG 237, EPFL Contact: karim.achouri@epfl.ch Research Interests: Metasurface design and scattering control Optical forces and torques in nanophotonics Machine learning applications in electromagnetic systems Nonlinear and non-Hermitian metasurfaces Spatial symmetry analysis in photonic structures Phase manipulation for optical computing Teaching: Courses in optical engineering, applied electromagnetics for metamaterial design, and advanced photonics. Publications: Recent work emphasizes metasurface robustness, topological phase control, multipolar expansion methods, and nonlinear optical devices. Articles span electromagnetic theory, nanophotonics, and computational modeling. Doctoral Students: Mentoring Mahmoud Ahmed, Hossein Allahverdizadeh, and Mustafa Yücel.

• Mathematical Modelling
• Scientific Computing
• Multiscale Modelling
• +۷ مورد دیگر

Dr. Jonathan Whiteley is an Associate Professor and Deputy Director of Graduate Studies in the Department of Computer Science at the University of Oxford. He is also a Governing Body Fellow at Linacre College. His research focuses on mathematical modelling and scientific computing applied to multiscale, multiphysics problems in physiology and biology. He holds a BA, MA, and DPhil from the University of Oxford. His key research interests include computational biology, biomechanics, and health informatics. He has contributed to studies on cartilage mechanics, cardiac electrophysiology, and tissue engineering, employing advanced numerical methods and finite element analysis. His work integrates computational models with experimental data to address challenges in biomedical engineering and regenerative medicine. Selected publications highlight his expertise in multiphase modelling, model reduction techniques, and the application of mathematical frameworks to biological systems. Collaborations with researchers in medicine and engineering underscore his interdisciplinary approach. Dr. Whiteley has advised multiple graduate students, including Antonietta Ambuehl, Douglas Bruce, and others. His research has been supported by projects involving tissue engineering, computational fluid dynamics, and cardiac tissue simulations. He contributes to the academic community through teaching, supervisory roles, and software development for scientific computing. Labs/Teams: His work is affiliated with computational biology and biomedical engineering initiatives at the University of Oxford, focusing on translating mathematical models into practical biomedical solutions.

Laurence Brassartمشاهده پروفایل

دانشیار

• Micromechanics
• Multiscale Modeling
• Constitutive Modeling
• +۵ مورد دیگر

Professor Laurence Brassart is an Associate Professor and Tutorial Fellow at Christ Church College, University of Oxford, within the Department of Engineering Science. She graduated with a Dipl. Eng (2007) and PhD (2011) from Université catholique de Louvain, followed by postdoctoral roles at Harvard University (Fellow of the Belgian American Educational Foundation) and UCLouvain (Chargé de Recherche FNRS). From 2015 to 2019, she was a Senior Lecturer at Monash University. Her research focuses on micromechanics , multiscale modeling , and constitutive modeling of polymers, soft materials, and energy materials. Key areas include multiphysics couplings (diffusion, chemical reactions), computational mechanics , and homogenization theory . Her current projects emphasize the chemo-mechanics of biodegradable polymers , rubberlike materials , and energy storage materials . Recent publications highlight her work on force-biased chemical degradation in rubbery networks , micromechanical modeling of polymer chains , and mechanics of liquid crystal inclusions in soft matrices. These studies span materials science , biomedical engineering , and solid mechanics . Fellow of the Belgian American Educational Foundation (Harvard postdoc) Newton International Fellowship Royal Commission for the Exhibition of 1851 Research Fellowship Marie Curie Fellowship She advises a team of DPhil students, including Gordon Kumar , Lucas Mangas Araujo , and Jie Zhu , and collaborates with postdoctoral researchers like Zhouzhou Pan and Huanming Chen . The Brassart Group integrates continuum mechanics and thermodynamics to develop theoretical and computational frameworks, with emerging experimental capabilities.

• Control Systems
• Lithium-Ion Battery Modeling
• Electrical Engineering
• +۵ مورد دیگر

Stephen Duncan is a Professor in the Department of Engineering Science at the University of Oxford and a Fellow of St Hugh's College. His expertise lies in control systems engineering, battery technology, and industrial automation. He holds an MA in Physics from the University of Cambridge and advanced degrees (MSc and PhD) in Control Systems from Imperial College London. His research focuses on advanced battery modeling, including lithium-ion battery dynamics, electrochemical processes, and control strategies for energy storage systems. He also explores applications in synchrotron beam control, additive manufacturing, and metal forming processes. Notable contributions include bridging microstructural and macroscopic battery models, optimizing cross-directional industrial systems, and developing novel control algorithms for complex engineering challenges. Recent work emphasizes battery fast-charging optimization, fault detection in large-scale battery packs, and thermal management strategies. His interdisciplinary approach integrates mathematical modeling, experimental validation, and industrial applications. Professor Duncan serves as Associate Editor for the IEEE Transactions on Control Systems Technology . His academic contributions span over 100 publications across control systems, energy storage, and manufacturing engineering. Current activities include collaborations with industry partners to advance battery management systems and sustainable energy solutions.

Jack Xinمشاهده پروفایل

استاد

• Applied Mathematics
• Computational Mathematics
• Machine Learning
• +۴ مورد دیگر

Jack Xin is a Chancellor's Professor at the University of California, Irvine, specializing in Applied and Computational Mathematics. His research focuses on machine learning, deep learning, optimization, and fluid dynamics, with applications in neural networks, turbulence modeling, and computational biology. He leads interdisciplinary projects involving stochastic algorithms, particle methods, and numerical analysis of partial differential equations. His work integrates advanced mathematical techniques with computational tools to address challenges in data science, combustion theory, and disease modeling. Notable contributions include developing efficient attention mechanisms for transformers, analyzing turbulent burning velocity laws, and creating particle-based algorithms for chemotaxis systems. Jack Xin has secured grants such as the NSF ATD program for robust spatio-temporal forecasting and collaborative research on deep learning algorithms. His research emphasizes computational efficiency, theoretical rigor, and real-world applications in fields ranging from epidemiology to climate science. His team explores cutting-edge methods in compressed sensing, neural network quantization, and graph-based models, aiming to bridge gaps between mathematical theory and practical machine learning systems.

Kersten Schmidtمشاهده پروفایل

پژوهشگر

• Computational Mathematics
• Partial Differential Equations
• Numerical Analysis
• +۵ مورد دیگر

Kersten Schmidt is a Researcher at the Department of Mathematics, Technische Universität Darmstadt, specializing in Numerical Analysis and Scientific Computing. His work focuses on mathematical modeling of wave phenomena, including electromagnetism, acoustics, and coupled systems, with applications in photonics and engineering. He holds a PD (postdoctoral qualification) and has contributed to advancements in finite element methods, homogenization techniques, and boundary layer analysis. Research Interests Partial differential equations modeling wave propagation Numerical methods: Finite Element Methods (FEM), boundary element methods, high-order discretizations Asymptotic analysis and homogenization for multiscale problems Applications in acoustics, electromagnetism, and plasmonics Modelling of thin structures and interface conditions Publications Recent work includes studies on: Acoustic-structure interaction optimization Homogenization techniques for periodic structures Impedance boundary conditions for viscous acoustics Nonlinear eigenvalue problems in waveguide systems Efficient numerical methods for photonic crystals and plasmonic particles Affiliations Member of the Numerical Analysis and Scientific Computing group at TU Darmstadt Collaborations with institutions including École Polytechnique (France), University of Zagreb (Croatia), and TU Wien (Austria)