جست‌وجوی استاد و پژوهشگر

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.

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +۴ مورد دیگر

Olaf Steinbach is a University Professor (Univ.-Prof.) at the Institute of Applied Mathematics at Graz University of Technology. His academic career spans over three decades with continuous research activity from 1992 to the present, including publications scheduled for 2026. He serves as a project manager for several research initiatives including the Special Research Area (SFB) F90 Computational Electric Machine Laboratory, which runs from 2022 to 2026. Professor Steinbach's research interests primarily focus on Numerical Analysis and Computational Mathematics . His work centers around developing and analyzing advanced numerical methods, particularly Finite Element Methods (FEM) and Boundary Element Methods (BEM), for solving partial differential equations (PDEs) and optimal control problems. His research spans both theoretical aspects (such as error analysis, stability, and convergence) and practical applications (including electric machines, electromagnetics, and biomechanics). He has made significant contributions to space-time finite element methods, which treat time as an additional dimension in the discretization process, leading to more robust and efficient solvers for time-dependent problems. Analysis of his recent publications (2021-2026) reveals a strong focus on optimal control problems governed by partial differential equations, with particular emphasis on elliptic, parabolic, and hyperbolic PDEs. His work demonstrates a consistent pattern of developing robust numerical methods with rigorous error analysis, often incorporating regularization techniques to handle challenging constraints. The applications span computational electromagnetics (particularly electric machines), fluid dynamics, and wave propagation problems. His research increasingly incorporates advanced computational techniques including parallel computing and isogeometric analysis. Professor Steinbach has supervised numerous doctoral students and has been actively involved in organizing academic events, including summer schools on Boundary Element Methods. His collaborative network extends across multiple disciplines and institutions, reflecting the interdisciplinary nature of his work in computational mathematics. His research has been supported through multiple significant projects including DK-W1244 Doctoral Program on Partial Differential Equations, the EU CASOPT project on optimization of industrial devices, and the ongoing Special Research Area on Computational Electric Machine Laboratory. These projects demonstrate his leadership in establishing research frameworks that bridge theoretical mathematics with practical engineering applications. Professor Steinbach maintains an active research group within the Institute of Applied Mathematics, collaborating closely with researchers in computational engineering, electrical engineering, and biomechanics. His work on the Computational Electric Machine Laboratory represents a particularly strong interdisciplinary effort combining mathematical theory with electrical engineering applications.

Vikas Srivastavaمشاهده پروفایل

دانشیار

• Solid Mechanics
• Continuum Biomechanics
• Cell Mechanics
• +۷ مورد دیگر

Dr. Vikas Srivastava is an Associate Professor of Engineering and Director of the Graduate Program in Biomedical Engineering at Brown University's School of Engineering. His research focuses on solid mechanics, continuum biomechanics, and cell mechanics, with applications in materials under extreme environments and biomedical science. He leads the Srivastava Lab for Solid Mechanics and Biomechanics, which integrates computational models with experimental techniques to address interdisciplinary challenges. Dr. Srivastava holds a Ph.D. in Mechanical Engineering from MIT (2010) and previously held senior roles at ExxonMobil, including leadership in materials mechanics and deepwater drilling engineering. His academic career at Brown began in 2018, during which he has directed over 15 graduate students and secured notable funding. His research interests span mechanobiology, hydrogel-based drug delivery systems, AI-driven predictive modeling, and biomaterial innovations for cancer therapies. He has pioneered physics-informed neural networks for material characterization and developed novel hydrogels to enhance chemotherapy efficacy. Recent articles highlight advancements in polymer fracture modeling, machine learning for non-destructive evaluation, and predictive epidemiological modeling for pandemics. Dr. Srivastava has received the Dean’s Award in Bioengineering and was promoted to tenured Associate Professor in 2023. He actively mentors students through grants like the NSF Graduate Research Fellowship and leads initiatives in biomedical technology translation. The Srivastava Lab collaborates extensively across engineering, biology, and medicine to advance translational research in materials science and clinical applications.

• Numerical Analysis
• Partial Differential Equations
• Finite Element Method
• +۳ مورد دیگر

Dirk Praetorius is a Professor of Numerics of Partial Differential Equations (PDEs) at the Technische Universität Wien (TU Wien) , affiliated with the Institute for Analysis and Scientific Computing (ASC) within the Faculty of Mathematics and Geoinformation . He leads the research group on Numerics of PDEs and has held various leadership roles, including Institute Director (since 2020) and head of the Numerics research area. His work focuses on numerical methods for PDEs, including Finite Element Methods (FEM), Boundary Element Methods (BEM), adaptive algorithms, and computational micromagnetics. Education and Career: Praetorius earned his Diplom in Mathematics (2000) and PhD in Applied Mathematics (2003) from TU Wien, followed by a Habilitation in Numerical Analysis (2005). He has been a faculty member at TU Wien since 2005, progressing from Assistant Professor to full Professor in 2017. He has also held visiting positions at institutions such as the University of Jyväskylä and RICAM (Linz). Research Interests: His research spans numerical analysis, adaptive FEM/BEM, a-posteriori error estimation, matrix compression, and computational micromagnetics. He has contributed to modeling spin dynamics, magnetic skyrmions, and multiscale systems. His work emphasizes efficient algorithms for large-scale problems and optimal computational complexity. Awards and Editorial Roles: Praetorius received the TU Best Teacher Award (2021) and TU Best Lecture Award (2019). He serves as Senior Editor for Computational Methods in Applied Mathematics (CMAM) and on the editorial board of Applied Numerical Mathematics (APNUM) . He co-founded the outreach initiative TUForMath to promote mathematics education. Grants and Projects: He leads or co-leads several research projects funded by the Austrian Science Fund (FWF), including the collaborative SFB "Taming Complexity in Partial Differential Systems" (2017–2025) and international collaborations with Germany. His work addresses topics like functional error estimates, nonlinear PDEs, and computational design of magnetic devices. Labs and Teams: He contributes to the ASC Institute and coordinates interdisciplinary projects involving computational physics and engineering. His team develops software tools like MooAFEM and Commics for micromagnetic simulations.

Yves Bourgaultمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Numerical Methods
• Finite Element Methods
• +۳ مورد دیگر

Yves Bourgault is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa. He holds a MSc and PhD from Laval University. His research focuses on computational fluid dynamics, numerical methods, finite element techniques, and continuum mechanics modeling, with applications in cardiac electrophysiology and ecological systems. Dr. Bourgault has supervised several graduate students, including Edward Boey (co-supervised), Sana Keita, Saint-Cyr Koyagurebo-Ime, and Kak Choon Loy. His work integrates advanced numerical techniques to address complex problems in biomedical engineering, environmental science, and mathematical physics. Key methodologies include finite element methods, deferred correction schemes, and anisotropic mesh adaptation. His research group is part of the Applied Mathematics division at the University of Ottawa, emphasizing interdisciplinary applications. Recent work explores climate change impacts on ecological systems, cardiac tissue modeling using high-resolution MRI data, and robust numerical methods for reaction-diffusion equations. Publications span topics such as bidomain models for cardiac electrophysiology, fluid-structure interaction in heart mechanics, and mathematical modeling of fuel cells. His contributions bridge theoretical numerical analysis with real-world biomedical and environmental challenges.

• Computational Fluid Dynamics
• Microfluidics
• Biomedical Flows
• +۵ مورد دیگر

Professor Cüneyt Sert is a faculty member at the Middle East Technical University (METU), Ankara, affiliated with the Department of Mechanical Engineering within the College of Engineering. He holds a B.Sc. (1996) and M.Sc. (1998) from METU, and a Ph.D. (2003) from Texas A&M University. His research focuses on Computational Fluid Dynamics, Microfluidics, and numerical methods like hpFEM and Least-Squares FEM, with applications in biomedical flows and high-performance computing. He co-supervised PhD student Burcu Ramazanlı, whose work contributed to recent studies on non-Newtonian fluid models in cardiovascular systems. Teaching includes advanced CFD courses (e.g., ME 705), emphasizing incompressible flow simulations using MATLAB. His recent research explores mesh adaptation techniques and benchmarking of FEM approaches.

David Del Rey Fernándezمشاهده پروفایل

استادیار

• Numerical Simulation of PDEs
• Summation-by-Parts Methods
• Continuous and Discontinuous Galerkin Schemes
• +۲ مورد دیگر

David Del Rey Fernández is Assistant Professor and Pratt & Whitney Canada Chair in Industrial Artificial Intelligence in the Department of Applied Mathematics at University of Waterloo. His research develops efficient numerical algorithms for solving partial differential equations on high-performance systems. He holds a PhD from University of Toronto and previously worked at NASA Langley Research Center. Research focuses on robust numerical methods, mesh adaptation, and machine learning acceleration. His work includes entropy-stable schemes, summation-by-parts methods, and discretizations for compressible flows. Recent publications address Lyapunov-consistent discretizations and scalable reduced-order modeling.

Mark Ainsworthمشاهده پروفایل

استاد

• Numerical Approximation of Partial Differential Equations
• A Posteriori Error Estimation
• High Order Finite Element Methods
• +۵ مورد دیگر

Mark Ainsworth is a Francis Wayland Professor of Applied Mathematics at Brown University and holds a joint faculty appointment with Oak Ridge National Laboratory. He obtained his PhD from Durham University (1989) and has held prominent roles such as Director of the Centre for Numerical Algorithms and Intelligent Software (2011-2012). His research focuses on numerical analysis, particularly finite element methods for partial differential equations, a posteriori error estimation, and high-performance computing challenges like resiliency on exascale systems. Education: PhD in Mathematics, Durham University, 1989 BSc in Mathematics, Durham University, 1986 Research Interests: Numerical approximation of PDEs A posteriori error estimation and adaptive methods High order finite element methods Resiliency of numerical algorithms on emerging architectures Fractional PDEs and scientific data compression Awards: SIAM Fellow (2014) FIMA (2010) Whitehead Prize (2004) J.L. Lions Prize (2004) Fellow of Royal Society of Edinburgh (2003) Grants & Leadership: Co-PI for ARO MURI on fractional PDEs (2015-2020) Directed NAIS center (2011-2012), a £5M multi-institutional initiative Organized major international conferences on computational mathematics Labs/Teams: Collaborations include Oak Ridge National Lab and international research networks in numerical analysis and scientific computing.

C. Armando Duarteمشاهده پروفایل

استاد

• Computational Mechanics
• Structural Engineering
• Multiscale Modeling
• +۵ مورد دیگر

Prof. C. Armando Duarte is the Nathan M. Newmark Distinguished Professor in Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign (UIUC). He holds affiliations with the Computational Science and Engineering Program and the National Center for Supercomputing Applications (NCSA). His research focuses on computational mechanics, particularly the Generalized Finite Element Method (GFEM/XFEM), multiscale modeling, fracture mechanics, and hydraulic fracturing. He has authored/co-authored over 100 publications and co-edited books on computational methods. Education: Ph.D. (1996) and M.Sc. (1991) in Engineering Mechanics from the University of Texas at Austin and Federal University of Santa Catarina, respectively, and a B.Sc. in Mechanical Engineering from the Federal University of Pernambuco. Professional Roles: Professor at UIUC since 2015, previously at the University of Alberta and visiting roles at institutions in Brazil, Portugal, and the Netherlands. He serves on editorial boards of computational mechanics journals and chairs committees for professional societies like USACM and ASCE. Research Interests: Multiscale problems, computational fracture mechanics, meshfree methods, and multiphysics simulations. His work emphasizes 3D fracture analysis, hydraulic fracturing, and thermal gradient modeling. Awards: Recognitions include the Nathan Newmark Professorship, USACM Fellowship, and multiple best paper awards. His work has been cited over 9,000 times, with papers featured in top journals like Computer Methods in Applied Mechanics and Engineering . Teaching: Renowned for excellence in courses like Finite Element Methods, Structural Analysis, and Computational Plates and Shells. His teaching accolades span over two decades at UIUC.

Bettina Siebersمشاهده پروفایل

استاد

• Archaea
• Central Carbohydrate Metabolism
• Extremozymes
• +۱۳ مورد دیگر

Prof. Dr. Bettina Siebers is a full Professor at the University of Duisburg-Essen, Faculty of Chemistry, where she leads the research group Molecular Enzyme Technology and Biochemistry (MEB). Her work is embedded in the Zentrum für Medizinische Biotechnologie (ZMB) and focuses on hyperthermophilic archaea, central carbohydrate metabolism, and biotechnological applications of extremozymes. She is actively involved in funded research projects, including a major €1.4 million grant from the VolkswagenStiftung for sustainable bio-production. Research Interests: Her research spans archaeal biochemistry, systems biology, metabolic engineering, and enzyme technology. She investigates the unique metabolic pathways in archaea, particularly under extreme conditions, combining classical biochemistry with genomics, transcriptomics, and computational modelling. Her group explores transcription regulation, stress responses, and protein phosphorylation in archaea as simplified models for eukaryotic processes. Publication Trends: Her recent publications emphasize enzyme cascade optimization, extremozyme discovery, metabolic pathway modelling (e.g., Weimberg pathway), and archaeal gene regulation. The interdisciplinary work bridges microbiology, biotechnology, and systems biology, with strong applications in sustainable bioproduction and industrial biocatalysis. Scientific Recognition: Lead Principal Investigator, VolkswagenStiftung 'Biofabrik' Project (€1.4 million) Active publications in high-impact journals including Nature , Nature Communications , FEMS Microbiology Reviews , and Nucleic Acids Research Member of ZMB and contributor to DFG, EU, and NRW-funded research programs Advising and Grants: While specific students are not listed, her group includes postdocs and researchers such as Dr. Christopher Bräsen. She leads major funded projects, including the VolkswagenStiftung initiative to replace microplastics with microbial lipids. Her work is supported by national and international collaborations, particularly with TU Wien. She integrates fundamental research with translational biotechnological applications. Labs and Teams: The MEB group at the University of Duisburg-Essen comprises a dedicated team including a laboratory manager, secretariat, and researchers. The group is part of the ZMB and collaborates within the Faculty of Chemistry and beyond, focusing on extremophile biochemistry and sustainable biotechnology.

Mira Schedensackمشاهده پروفایل

استادیار

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +۳ مورد دیگر

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.

• Medical Microbiology
• Antibiotic Resistance
• Bacteriophage Therapy
• +۲ مورد دیگر

Professor Mark Enright is a Professor of Medical Microbiology at Manchester Metropolitan University's Department of Life Sciences, specializing in genomic epidemiology of antibiotic-resistant pathogens and bacteriophage therapy development. His work bridges clinical microbiology with advanced genomic techniques to combat multidrug-resistant infections. His educational background includes: PhD in Medical Microbiology, University of Aberdeen (1995) MSc in Biochemistry, University of Dundee (1990) BSc (Hons) in Biological Sciences, University of Stirling (1989) Enright's research focuses on antibiotic resistance mechanisms in Gram-positive and Gram-negative pathogens, with groundbreaking work on bacteriophage-based therapeutics targeting Staphylococcus aureus , Pseudomonas aeruginosa , and Klebsiella pneumoniae . His laboratory employs whole-genome sequencing and core-genome MLST to track pathogen evolution, biofilm formation, and horizontal gene transfer. Recent projects include developing phage cocktails against cystic fibrosis-related infections and characterizing depolymerases for capsular polysaccharide degradation. Analysis of his 2015-2025 publications reveals consistent focus on genomic epidemiology of healthcare-associated pathogens, with increasing emphasis on phage therapy applications. His work demonstrates methodological evolution from traditional typing to advanced genomic analyses, particularly in tracking international MRSA transmission and developing phage-based biofilm disruption strategies for wound infections. His major recognitions include: Royal Society University Research Fellowship (2000-2008) European Society for Clinical Microbiology Young Investigator Award (2002) As Editor of the International Journal of Antimicrobial Agents and Section Editor for FEMS Microbiology Letters , Enright shapes antimicrobial research discourse. He serves on editorial boards of Antibiotics and PeerJ , and reviews for Science and Nature . His grant review expertise for Wellcome Trust, MRC, and EU programs informs global research funding priorities. Formerly a Visiting Professor at University of Bath (2011-2014), he maintains strong industry-academia collaborations in antimicrobial development. His Molecular Microbiology teaching integrates cutting-edge research into curriculum, while his John Dalton Building laboratory develops novel approaches to combat antimicrobial resistance through phage genomics and biofilm disruption technologies.

• hp-Finite Element Method (hp-FEM)
• Fractional Diffusion Equations
• Boundary Element Method (BEM)
• +۳ مورد دیگر

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.

Dana Cobzasمشاهده پروفایل

دانشیار

• Computer Vision
• Medical Imaging
• Image Segmentation
• +۷ مورد دیگر

Dana Cobzas is an Associate Professor at the MacEwan University in the Department of Computer Science , with adjunct appointments at the University of Alberta. Her academic journey includes a PhD in Computer Science (University of Alberta, 2004) MSc in Mathematics (Babes-Bolyai University, 1998) BSc in Mathematics (Babes-Bolyai University, 1997) . Her research focuses on imaging and computer vision , particularly mathematical models for medical image processing . Key areas include Medical image segmentation and registration 3D modeling from uncalibrated images Sparse classification for population studies Dynamic vision (tracking and modeling) Medical applications in neuroimaging and oncology . She has developed advanced techniques like deep learning-integrated level set methods and FEM-based segmentation. Scientific recognition includes: NSERC Discovery Grant (2015, 2010) Best Vision Paper at IEEE ICRA 2005 Best Student Paper at Vision Interface 2003 . She is actively involved in teaching and mentoring , with experience supervising senior students’ independent studies and contributing to collaborative projects in robotics and biomedical engineering .

• Partial Differential Equations
• High Performance Computing
• Discontinuous Galerkin Methods
• +۳ مورد دیگر

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.