جست‌وجوی استاد و پژوهشگر

Gerhard Huiskenمشاهده پروفایل

استاد

• Analysis
• Differential Geometry
• Mathematical Relativity
• +۳ مورد دیگر

Gerhard Huisken is a Professor at the University of Tübingen and Director of the Mathematisches Forschungsinstitut Oberwolfach . His work spans Differential Geometry , Geometric Flows , and Mathematical Relativity . Education : Diploma (1982), PhD (1983), and Habilitation (1986) in Mathematics from Heidelberg University. His research focuses on geometric evolution equations, particularly mean curvature flow and inverse mean curvature flow , with applications to mathematical relativity and geometric inequalities . He has contributed to the understanding of singularities in curvature flows and developed surgical techniques for their analysis. His work on the Riemannian Penrose inequality and center of mass in isolated systems bridges geometry and physics. Selected publications highlight trends in geometric flows (mean curvature flow, Ricci flow), mathematical relativity (Penrose inequality, center of mass), and singularities in geometric PDEs. His collaborations with leading mathematicians like Simon Brendle and Tom Ilmanen reflect interdisciplinary impact. Scientific Awards and Honors : Fellow of the American Mathematical Society (2013) Clay Foundation Senior Fellowship (2013, 2007) Leibniz Preis from German Research Foundation (2003) Medal of the Australian Mathematical Society (1991) Member of the German Academy of Sciences Leopoldina (2004) He has held leadership roles including Dean of the Faculty of Mathematics at Tübingen University and directed major institutes like the Max Planck Institute for Gravitational Physics (2002-2013).

Vladimir Kazeevمشاهده پروفایل

استادیار

• Numerical Analysis
• Tensor Methods
• Partial Differential Equ to the data-driven solution of PDEs
• +۳ مورد دیگر

Vladimir Kazeev is an Assistant Professor at the Faculty of Mathematics, University of Vienna , where he has held a faculty position since 2019. He also held previous academic appointments as a Szegő Assistant Professor at Stanford University (2017–2019), a postdoctoral researcher at the University of Geneva (2015–2017), and research positions at ETH Zurich (2011–2015), Russian Academy of Sciences (2008–2011), and Moscow Institute of Physics and Technology (2009). His research focuses on adaptive, data-driven numerical methods for differential equations, nonlinear low-parametric approximation, and numerical linear algebra. His work intersects computational mathematics, tensor methods, and high-dimensional problem-solving, particularly in the context of partial differential equations (PDEs) and stochastic modeling. The 15 most recent publications reveal a strong emphasis on quantized tensor-structured methods for PDEs, low-rank approximations, and high-dimensional numerical analysis. His research spans theoretical advancements in tensor decomposition, practical applications in chemical reaction networks, and novel discretization techniques for multiscale and degenerate diffusion problems. Scientific awards include the prestigious ETH Medal for outstanding doctoral theses (2016) Russian Academy of Sciences Medal for outstanding student works in mathematics (2011) Advising and teaching activities include supervising Jason Zhu (Stanford, 2019) and Simon Etter (ETH Zurich, 2014), as well as teaching advanced courses in tensor methods, numerical analysis, and PDEs at the University of Vienna, Stanford University, and the University of Geneva. His service to the community includes peer review for 15+ journals and co-organizing minisymposia at SIAM meetings.

• Functional Analysis
• Complex Analysis
• Operator Theory
• +۷ مورد دیگر

Olivia Constantin is an Associate Professor in the Department of Mathematics at the Faculty of Mathematics. She has been actively publishing since 2008, with research contributions in functional analysis, complex analysis, and mathematical fluid dynamics. PhD in Mathematics (implied by academic position and research output) Her research primarily focuses on operator theory on function spaces, especially Fock and Bergman spaces, and the application of complex analysis to fluid flows. Key areas include Hankel operators, integral operators, embedding theorems, and the analysis of water waves and oceanic gyres. Her work combines deep analytical techniques with applications in fluid mechanics. Recent publications show a strong trend toward using complex-analytic methods to study irrotational flows, traveling water waves, and gyre dynamics, indicating an interdisciplinary approach bridging pure and applied mathematics. Her 2025 paper on velocity extrema in ocean gyre flows highlights ongoing contributions to geophysical fluid dynamics. She has led two research projects: Operatorenklassen (2012–2016) and Analysis of Operators on Spaces of Holomorphic Functions (2017–2019), demonstrating sustained research leadership. Active speaker at academic conferences, with 18 recorded scientific activities including talks from 2008 to 2023 Olivia Constantin collaborates with prominent mathematicians such as A. Aleman, J. A. Peláez, A.-M. Persson, and D. Kalaj. She has not received any explicitly mentioned scientific awards. There is no information about her advising students or managing labs or research teams.

Dario Vretenarمشاهده پروفایل

استاد

• Theoretical Nuclear Physics
• Energy Density Functional Theory
• Low-Energy Nuclear Effective Field Theory
• +۳ مورد دیگر

Dario Vretenar is a Professor of Physics at the Department of Physics, Faculty of Sciences, University of Zagreb (since 2001). He has held academic roles including Associate Professor (1997-2001) and Assistant Professor (1993-1997). His research focuses on theoretical nuclear physics, particularly energy density functional theory, nuclear weak interactions, and computational physics. He has held visiting positions at institutions such as Technical University Munich (Visiting Professor, 1999-2000) and University of Tokyo (2010). Education: Ph.D. in Theoretical Nuclear Physics (University of Zagreb, 1988); M.Sc. in Physics (University of Zagreb, 1982). Postdoctoral fellowships included Yale University (1990) and Alexander von Humboldt Fellowships at Technical University Munich (1991-1993, 1997). Research Interests: Algebraic structure models for high-angular momentum states Low-energy nuclear effective field theory Nuclear astrophysical applications Development of relativistic energy density functionals Publications: Over 210 peer-reviewed articles (h-index 43, 7,000+ citations), 8 books, and 100+ conference contributions. Notable works include studies on nuclear clustering, quantum phase transitions, and neutron skin structure. Awards: Croatian Academy of Science and Arts Award for Science and Mathematics (2002) Croatian National Award for Science (2003) Fellow of the Croatian Academy of Sciences and Arts (2012) Service Roles: President of the Croatian Science Foundation (2013–present) Chair of the Physics Committee, Agency for Science and Higher Education, Croatia (2009–present) Board Member of Euroschool on Exotic Beams (2012–present) Teaching: Courses include Quantum Physics, Nuclear Structure, and Nuclear Astrophysics at undergraduate and graduate levels. Supervised numerous students in theoretical nuclear physics.

Cesare Franchiniمشاهده پروفایل

استاد

• Quantum Materials
• Computational Physics
• First Principles Methods
• +۷ مورد دیگر

Cesare Franchini is a full Professor at the University of Vienna's Faculty of Physics, leading the Computational Materials Physics research group. His work focuses on theoretical understanding and computational modeling of quantum materials using first principles methods, particularly VASP. He maintains an active research program with numerous postdocs, PhD students, and collaborations across multiple institutions including the University of Bologna. Professor Franchini's research centers on quantum materials with many interacting degrees of freedom (lattice, spin, and electron orbital) that enable novel electronic and magnetic phases. His specific interests include metal-insulator transitions, polaron physics (electron-phonon interactions), non-collinear spin orderings, topological Dirac/Weyl phases, multiferroism, and superconductivity. He has increasingly incorporated machine learning data-driven tools and diagrammatic Monte Carlo techniques into his computational approaches. Analysis of his recent publications (2024-2025) reveals a strong focus on polaron physics across multiple material systems, with significant work on hematite, titanium dioxide, and quantum paraelectrics like KTaO3. His research increasingly integrates machine learning with traditional first-principles methods, particularly for studying hydrogen diffusion, surface science phenomena, and electronic structure calculations. There's also substantial work on single-atom catalysis and the application of advanced computational techniques to understand fundamental charge transport mechanisms in energy materials. Professor Franchini actively supervises numerous PhD students and postdocs, including Andrea Angeletti, Viktor Birschitzky, Lorenzo Celiberti, and several others working on diverse aspects of computational materials physics. He leads or participates in major research projects including TACO (Taming Complexity in Materials Modeling), DCAFM (Doctoral College Advanced Functional Materials), and the recently launched Spin-orbit entangled anharmonic polarons project. His group maintains strong collaborations with experimentalists at Charles University, Technical University of Vienna, and other international institutions.

Ilaria Perugiaمشاهده پروفایل

استاد

• Numerical methods for PDEs
• Finite element methods
• Discontinuous Galerkin methods
• +۶ مورد دیگر

Ilaria Perugia is a University Professor (Univ.-Prof.) and Chair of Numerics of PDEs at the Department of Mathematics, Faculty of Mathematics, University of Vienna. She also serves as Deputy Head of the Research Platform Erwin Schrödinger International Institute for Mathematics and Physics. Her research focuses on numerical methods for partial differential equations with applications in computational physics and engineering. Professor Perugia's primary research interests include: Numerical methods for PDEs Finite element methods Discontinuous Galerkin methods Trefftz methods Virtual element methods Space-time methods Computational electromagnetics Wave propagation problems Nonlinear reaction-diffusion problems Her work spans theoretical analysis, algorithm development, and practical implementation of numerical methods for solving complex physical phenomena. Her recent publications demonstrate a strong focus on space-time methods, virtual element methods, and structure-preserving discretizations for wave equations, heat equations, and other PDEs. She has made significant contributions to the development of stable and efficient numerical schemes that preserve important physical properties of the underlying continuous problems, particularly in the context of wave propagation and computational electromagnetics. Professor Perugia leads a research group comprising several researchers and students including Mattia Corti, Matteo Ferrari, Monica Nonino, Andrea Scaglioni, Paul Stocker, Enrico Zampa, and Marco Zank. Her group actively collaborates on projects related to numerical analysis and scientific computing, with particular emphasis on developing novel discretization techniques for challenging PDE problems.

Süli Endreمشاهده پروفایل

استاد

• Free-discontinuity problems
• Computational modelling of fracture
• Quasi-continuum methods
• +۱۰ مورد دیگر

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.

Senad Bećirovićمشاهده پروفایل

استاد

• Artificial Intelligence in Education
• Digital Technologies in Education
• Intercultural Education
• +۳ مورد دیگر

Senad Bećirović serves as a full Professor at the University of Education Lower Austria (Pädagogische Hochschule Niederösterreich), having progressed from assistant professor (2014) to associate professor (2017) before attaining his current rank in 2023. His academic career spans teaching research methodology, pedagogy, and advanced statistics across undergraduate, graduate, and doctoral programs at international institutions, delivered through both online and in-person formats. Bećirović's research focuses primarily on Artificial Intelligence and digital technologies in education , with significant contributions to intercultural education , gifted education , and foreign language pedagogy . His work demonstrates a consistent trajectory toward understanding how emerging technologies reshape educational practices and outcomes. Through four books published by Springer and Nova Science and over 60 articles in top-tier journals, he has established himself as a leading voice in digital educational transformation. His recent publications (2023-2025) reveal a pronounced emphasis on AI applications in education, with multiple studies examining AI literacy, policy frameworks, and implementation strategies across European higher education contexts. This research demonstrates both theoretical depth and practical relevance to current educational challenges. Bećirović actively contributes to the scholarly community as editor and reviewer for numerous Q1 journals including Smart Learning Environments (Springer), Education and Information Technologies (Springer), and TESOL Quarterly (Wiley). He participates in multiple European Commission-funded initiatives such as ENRICH (Enhancing Teaching and Research through Innovative Digital Technologies) and Erasmus+ projects focused on peace learning and English education. His professional activities extend to international collaboration through membership in the European Network Ethical Use of AI and the American Educational Research Association (AERA). He frequently delivers keynote addresses at international conferences, sharing expertise on AI in education, digital transformation, and intercultural competencies.

Enrico Arrigoniمشاهده پروفایل

استاد

• Quantum Physics
• Condensed Matter Physics
• Theoretical Physics
• +۳ مورد دیگر

Enrico Arrigoni is a Professor at the Institute of Theoretical Physics - Computational Physics at Graz University of Technology (TU Graz). His research focuses on correlated quantum systems, many-body physics, and nonequilibrium dynamics, with applications to Mott insulators, quantum transport, and photovoltaic systems. He teaches courses such as 'Green's functions in Many-Particle Physics' and 'Atom Physics - Quantum Mechanics'. Recent work explores phonon effects in Mott systems, neural network approaches to quantum states, and impact ionization processes in photodriven materials. His methods include auxiliary master equation techniques and variational cluster approaches. Publications span topics like nonequilibrium steady states, quantum impurity models, and disordered systems. While no specific awards are listed, his contributions to theoretical physics and computational methods are evident through his prolific research output. Advising and grants details are not explicitly mentioned, though his involvement in graduate theses and research projects is implied via available master's and bachelor's thesis topics.

Michael J. Schlosserمشاهده پروفایل

استاد

• Combinatorics
• Number Theory
• Special Functions
• +۳ مورد دیگر

Michael J. Schlosser is a faculty member at the Faculty of Mathematics , University of Vienna. His research focuses on combinatorics, number theory, and special functions, with a particular emphasis on hypergeometric and q-series, elliptic extensions, and rook theory. He has authored numerous publications in collaboration with prominent mathematicians such as Victor Guo, Meesue Yoo, and Christian Krattenthaler. Research Interests : Combinatorics and hypergeometric series Elliptic functions and their applications Partition theory and supercongruences Matrix inversions and determinant evaluations Students : Josef Küstner (Ph.D., 2022) Christian Stump (Ph.D., 2008) Editorial Roles : Associate Editor, Journal of Mathematical Analysis and Applications Editorial Board Member, The Ramanujan Journal Editorial Board Member, Journal of Algebraic Combinatorics

Endre Süliمشاهده پروفایل

استاد

• Numerical Analysis of Partial Differential Equations
• Finite Element Methods
• Computational Modeling of Fracture
• +۴ مورد دیگر

Endre Süli is Professor of Numerical Analysis at the University of Oxford, where he has maintained a distinguished academic career since 1985. He currently serves as Fellow and Tutor in Mathematics at Worcester College and Supernumerary Fellow at Linacre College. His progression at Oxford includes University Lecturer in Numerical Analysis (1985-1996), Reader in Numerical Analysis (1996-1999), and Professor of Numerical Analysis (1999-present). Süli completed his B.Sc. in Mathematics at the University of Belgrade (1974-1978), followed by an M.Sc. in Mathematics (1978-1980). As a British Council Visiting Student, he studied at Reading University and Oxford University in 1983/84, earned his Ph.D. from the University of Belgrade in 1985, and received his M.A. from Oxford University in the same year. Professor Süli's research centers on numerical analysis of nonlinear partial differential equations with applications across multiple scientific domains. His work spans free-discontinuity problems and computational modeling of fracture; finite element methods; Navier-Stokes-Fokker-Planck systems; adaptive algorithms with a-posteriori error control; implicitly constituted material models; and discontinuous finite element methods. His research bridges theoretical mathematics with practical computational approaches for complex physical phenomena. Recent publications (2024-2025) demonstrate Süli's continued leadership in numerical analysis, with focus areas including fractional calculus, stochastic PDEs, and advanced finite element techniques. His work shows strong interdisciplinary connections between mathematical analysis, fluid dynamics, and materials science, addressing challenging problems in polymeric fluids, porous media, and capillary flow modeling. Professor Süli's distinguished career has been recognized with numerous prestigious honors: Invited Speaker at the International Congress of Mathematicians, Madrid (2006) Fellow of the Institute of Mathematics and its Applications (2007) Foreign Member of the Serbian National Academy of Sciences and Arts (2009) Fellow of the European Academy of Sciences (2010) IMA Service Award (2011) SIAM Fellow (2016) Member of the Academia Europaea (2020) London Mathematical Society Naylor Prize and Lectureship (2021) Fellow of the Royal Society (2021) As an educator, Süli has received the Oxford University Teaching Excellence Award (2009) and the Mathematical Institute Teaching Award (2013). He has supervised numerous PhD students and postdoctoral researchers throughout his career, though specific names aren't documented in the available materials. His research has been supported by various grants enabling work on computational methods for partial differential equations. Süli maintains active service to the mathematical community through editorial boards and professional organizations. Professor Süli is affiliated with the Numerical Analysis research group and the Oxford Centre for Nonlinear PDE at the Mathematical Institute. These research centers provide a collaborative environment for theoretical and applied work on partial differential equations. His research often involves interdisciplinary collaborations with physicists, engineers, and computational scientists to develop and analyze numerical methods for complex physical phenomena.

Johannes Brandstetterمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Geometric Deep Learning
• +۵ مورد دیگر

Johannes Brandstetter is an Associate Professor at the Institute for Machine Learning at Johannes Kepler University Linz (JKU) where he leads the "AI for data-driven simulations" research group. He is also Co-founder and Chief Scientist at Emmi AI, bridging academic research with industrial applications in AI-driven physics simulation. Brandstetter earned his PhD after working at CERN's CMS experiment on Higgs boson physics. In 2018, he transitioned to machine learning, joining Sepp Hochreiter's research group in Linz. From 2021-2023, he worked at the Amsterdam Machine Learning Lab under Max Welling and Microsoft Research, developing expertise in Geometric Deep Learning and neural surrogates for partial differential equations. He returned to JKU in October 2023 to establish his own research group. His research spans Machine Learning, Deep Learning, and Physics-Informed Machine Learning with focus areas including Neural PDE solvers, Computational Fluid Dynamics, and Climate Modeling. Brandstetter believes AI is poised to revolutionize industrial-scale simulations, potentially saving thousands of compute hours across engineering domains. His work integrates computer vision, numerical simulation, and engineering components to advance data-driven approaches. Recent publications reveal a strong trend toward foundation models for scientific applications, particularly in atmospheric modeling (Aurora), geometric deep learning, and neural surrogates for complex physical systems. His interdisciplinary work spans computer vision, climate science, computational physics, and engineering, demonstrating the versatility of his research approach. Principal Investigator for "AlKa-DL: Alpine karst spring discharge prediction" (FWF-funded, 2024-2027) Principal Investigator for Cluster of Excellence "Bilateral Artificial Intelligence" (FWF-funded, 2024-2029) Co-PI for "Fast, efficient and flexible CFD simulation through generative AI" (FFG-funded, 2025-2026) As an educator and researcher, Brandstetter actively engages with the scientific community through invited talks at major conferences including presentations on "Closing the Gap Between Scientific Foundation Models and Real-World Applications" (March 2025) and "Scientific Machine Learning for Science and Engineering" (February 2025).

Martin Hollerمشاهده پروفایل

استاد

• Mathematics of data science
• Machine learning
• Generative models
• +۷ مورد دیگر

Martin Holler is a Professor at the Institute of Mathematics and Scientific Computing at the University of Graz, Austria, where he leads the research group Applied Mathematics and Machine Learning . His work bridges theoretical mathematics with practical applications in imaging and machine learning. Research Focus: His primary research areas include the mathematics of data science, variational methods in imaging, dynamic and multi-modality inverse problems, and biomedical imaging. He has made significant contributions to model-based regularization techniques, particularly with Total Generalized Variation (TGV) approaches for image and video reconstruction. Publication Trends: Over the past decade, Holler's research has evolved from traditional variational methods for image reconstruction toward increasingly sophisticated machine learning approaches. His recent work (2021-2023) focuses on integrating deep learning with variational methods, particularly for motion separation in medical imaging and learning-informed parameter identification in partial differential equations. His publications demonstrate a consistent thread of applying rigorous mathematical frameworks to solve practical problems in medical imaging and computer vision. Mathematics of data science and machine learning Generative models in machine learning Variational methods in imaging Dynamic and multi-modality inverse problems Model-based regularization Biomedical imaging Image and video decompression Technical Leadership: Holler has developed several open-source software packages implementing advanced reconstruction algorithms, particularly for multi-modal imaging problems. His GitHub repositories show active maintenance and development of these tools, which have been cited in the medical imaging community.

Salençon Jeanمشاهده پروفایل

پژوهشگر ارشد

• Continuum Mechanics
• Soil Mechanics
• Composite Materials
• +۴ مورد دیگر

Jean Salençon is a prominent academic and researcher in mechanics and civil engineering. He currently holds the position of Senior Fellow at the Institute for Advanced Study, City University of Hong Kong since 2016. Previously, he served as Visiting Distinguished Professor (2014–2016), Chair Professor-at-large (2011–2014), and President of the Académie des sciences (2009–2010). His academic career includes roles as a Professor of Mechanics at the École polytechnique (1982–2005) and Head of the Department of Mechanical and Material Sciences at the École nationale des ponts et chaussées (1980–1985). Salençon’s research focuses on continuum mechanics , soil mechanics , yield design theory , and composite materials . His work bridges theoretical advancements with practical applications in geotechnical engineering and structural analysis. Notable contributions include probabilistic approaches to yield design, stability analysis of earthworks, and macroelement modeling of foundations under seismic loads. Key Awards: Trevithick Prize (1990), Montyon Prize (1978), Member of the Académie des sciences (1988), and multiple honorary memberships in international academies. Leadership Roles: Rector of the CISM (2004–2012), Member of the Bureau IUTAM (2000–2004), and President of the Académie des sciences (2009–2010). Salençon has authored seminal textbooks such as Handbook of Continuum Mechanics (2001) and Yield Design (2013), contributing significantly to global engineering education and research.

• Computational Mathematics
• Numerical Analysis
• Finite Element Methods
• +۳ مورد دیگر

Prof. Joachim Schöberl is a faculty member at TU Wien's Faculty of Mathematics and Geoinformation, leading the Scientific Computing and Modelling research group. His academic career includes roles as a university professor (Univ.Prof.) with engineering and technical doctorates (Dipl.-Ing., Dr.techn.). Research focuses on advanced numerical methods, including finite element methods, computational fluid dynamics, and partial differential equations. He has pioneered high-order schemes for fluid-structure interaction, shell mechanics, and electromagnetic simulations. Notable contributions include the NGSolve finite element library and innovative approaches to curvature approximation in discrete geometry. Recent work emphasizes nonlinear elasticity modeling, fractional diffusion problems, and shape optimization for biomembranes. His team collaborates on projects like metascreen upscaling, micromorphic continuum models, and eddy current simulations in laminated materials. Prof. Schöberl advises PhD students researching mixed finite element methods, fractional operators, and computational mechanics. His lab develops open-source tools for high-performance scientific computing.