جست‌وجوی استاد و پژوهشگر

• Musical Acoustics
• Physical Modeling of Musical Instruments
• Wind Instrument Aerodynamics
• +۴ مورد دیگر

Professor Vasileios Chatziioannou serves as Faculty Member and PhD Supervisor within the Department of Music Acoustics at the University of Music and Performing Arts Vienna. His academic leadership spans the Faculty of Music, where he guides doctoral candidates through the Structured Doctoral Program Performing Matters. His institutional role encompasses supervision across Ethnomusicology, Music in Society, and Musicology programs. His research focuses on the physics of musical instruments, with particular expertise in wind instrument aerodynamics and string-bow interaction mechanics. He employs advanced experimental techniques including particle image velocimetry (PIV) and high-speed motion capture alongside computational modeling. His work bridges theoretical acoustics with practical instrument design considerations, examining phenomena from tone hole geometry effects to friction dynamics in bowed strings. Analysis of his recent publications reveals a strong emphasis on energy-stable numerical methods for real-time simulation of musical instrument physics. His work consistently addresses the challenge of balancing computational efficiency with physical accuracy, particularly in modeling nonlinear phenomena like stick-slip friction and collision dynamics. The research trajectory shows increasing integration of experimental validation with computational models. While no specific scientific awards are documented in the provided materials, his sustained publication record in high-impact acoustics journals demonstrates significant scholarly contribution. His work regularly appears in specialized forums like the Journal of the Acoustical Society of America, particularly within special issues on musical instrument modeling. Professor Chatziioannou's supervision activities focus on computational and experimental approaches to musical instrument physics. His research program involves interdisciplinary collaboration between acousticians, instrument makers, and performers. Current projects include robotic replication of bowing techniques and 3D-printed instrument component optimization, reflecting his commitment to translating theoretical findings into practical applications for musicians and instrument designers.

Marcel Campenمشاهده پروفایل

استاد

• Computer Graphics
• Geometry Processing
• Surface Parametrization
• +۴ مورد دیگر

Marcel Campen is a Professor at Osnabrück University specializing in Computer Graphics and Geometry Processing. His research focuses on surface parametrization, quad mesh generation, and computational geometry. He has made significant contributions to the field of geometry processing, particularly in developing algorithms for quad layout generation, surface mapping, and mesh repair. His research interests span Computer Graphics, Geometry Processing, Surface Parametrization, Quad Mesh Generation, 3D Modeling, and Mesh Repair. Campen's work addresses fundamental challenges in representing and processing complex geometric shapes, with applications ranging from animation and simulation to reverse engineering and meshing. His research often combines theoretical insights with practical implementations, resulting in algorithms that are both mathematically sound and computationally efficient. Campen's publications demonstrate a strong focus on developing robust and efficient methods for geometry processing. His work on quad layout generation, parametrization techniques, and surface mapping has resulted in several award-winning papers, including Best Paper Awards at SGP 2021 and 2022. His research often bridges theoretical concepts with practical implementations, making his contributions highly influential in both academic and industrial settings. Best Paper Award (1st place) at SGP 2022 Best Paper Award at SGP 2021 Campen has made significant contributions to the field through his doctoral thesis on quad layout generation and numerous publications in top-tier conferences including SIGGRAPH, Eurographics, and SGP. His work on directional field synthesis, similarity maps, and bijective mappings has advanced the state of the art in geometry processing. He has also contributed to practical tools like libQEx for robust quad mesh extraction, demonstrating his commitment to making theoretical advances accessible to practitioners.

Armin Iskeمشاهده پروفایل

استاد

• Numerical Approximation
• Applied Mathematics
• Kernel Methods
• +۴ مورد دیگر

Prof. Dr. Armin Iske is a Full Professor of Numerical Approximation at the University of Hamburg's Department of Mathematics, within the Faculty of Mathematics, Computer Science and Natural Sciences. He holds a PhD from the University of Göttingen (1994) and habilitation from TU Munich (2002). His research focuses on numerical approximation, kernel-based methods, computational fluid dynamics, and medical imaging. He has held academic positions globally, including visiting roles at ANU (Australia) and the University of Leicester (UK). Research interests include scattered data approximation, adaptive particle methods for flow simulation, and high-dimensional data analysis. He has authored 118+ publications, including works on kernel interpolation, medical imaging reconstruction, and machine learning applications. He serves on editorial boards for journals like Advances in Computational Mathematics and Sampling Theory . His contributions span interdisciplinary projects, such as SFB/TRR 181 on energy transfer in atmosphere and ocean, and collaborations in nanotechnology for brain interfaces. His work bridges theoretical mathematics with practical applications in engineering and biosciences.

• Machine Learning
• Medical Data Analysis
• Time-Series Data Analysis
• +۳ مورد دیگر

Dr. Weitong Chen is a Senior Lecturer at the School of Computer and Mathematical Sciences, University of Adelaide, and an ARC EC Industry Fellow at the Australian Institute for Machine Learning (AIML). He holds a PhD from the University of Queensland (2020), with prior roles as a Post-Doc Research Fellow and Associate Lecturer there. His research focuses on machine learning applications in medical data, particularly time-series analysis, semi-supervised learning, and IoT. He collaborates widely across academia, industry, and government, supported by multiple grants. His work emphasizes healthcare applications, adversarial robustness, federated learning, and unlearning mechanisms. Education: PhD in Machine Learning, University of Queensland (2020) Master's Degree, University of Queensland Bachelor's Degree, Griffith University Research Interests: Medical Data Analysis (e.g., EHRs, radiology) Time-Series Modeling (healthcare IoT, irregular data) Adversarial Machine Learning (backdoor attacks, robustness) Federated Learning (modality incompleteness, clustered frameworks) Data Privacy (unlearning, compliance) Grants & Collaborations: ARC EC Industry Fellowship Industry partnerships in healthcare and IoT Labs/Teams: Australian Institute for Machine Learning (AIML) Cross-disciplinary health tech collaborations

• Computational Mechanics
• Numerical Simulation
• Shape Optimization
• +۱۲ مورد دیگر

Stefanie Elgeti is Associate Professor and Private Lecturer at the Chair for Computational Analysis of Technical Systems (CATS), Faculty of Mechanical Engineering, RWTH Aachen University. She previously held a professorship in lightweight design at TU Vienna starting in 2019. Her research integrates computational mechanics with manufacturing process optimization, focusing on plastics extrusion, injection molding, and high-pressure die casting. Diploma in Mechanical Engineering, majoring in 'Manufacturing Techniques for Microsystems' PhD (2011): 'Free-Surface Flows in Shape Optimization of Extrusion Dies' Habilitation (2016): 'CAD-Conforming Finite Element Methods in Engineering Design' Her research centers on solving inverse problems in manufacturing through numerical simulation. She employs advanced techniques such as free-surface flow modeling, non-Newtonian material models, spline-based finite elements, and PDE-constrained shape optimization. Her group simulates entire process chains from filling to solidification and warpage prediction, enabling design optimization of cavities and cooling systems. The recent publications (2022–2024) reveal a strong trend toward integrating artificial intelligence—particularly physics-informed neural networks and Bayesian optimization—into traditional simulation workflows. There is increasing emphasis on warpage compensation, shape optimization of extrusion dies, and modeling of biomedical and environmental systems, showcasing a broadening scope from industrial manufacturing to interdisciplinary applications. She is actively involved in academic service, having served as vice-spokesperson of GAMM-Juniors (2013–2014) and currently co-chairing the ECCOMAS Young Investigator Group. While no formal awards are listed, her leadership roles and editorial contributions reflect significant recognition in the computational mechanics community. Prof. Elgeti advises students and leads multiple research initiatives at CATS, including work groups focused on production engineering, fluid-structure interaction, and INTERESST. Her team develops model hierarchies and digital twins for industrial processes, aiming to bridge simulation and real-world manufacturing through intelligent, adaptive systems.

• Mathematical Methods in Signal Processing
• Data Science
• Dynamical Systems
• +۵ مورد دیگر

Nada Sissouno is a Professor of Mathematics and Didactics of Mathematics at the Faculty of Electrical Engineering, Media and Informatics at Amberg-Weiden University of Applied Sciences since November 2023. She also serves as Vice Dean and Co-head of the Competence Center Grundlagen (CCG). Additionally, she maintains a position as a guest researcher at the Research Group: Applied and Numerical Analysis and Optimization and Data Analysis at the Technical University of Munich (TUM). Her educational background includes a Doctorate in Mathematics (Dr. rer. nat.) from TU Darmstadt (2007-2011) and a Diplom in Mathematics with a minor in psychology from TU Darmstadt (2000-2007). She has completed further education as a Diversity Manager in 2021 and holds certificates in teaching in higher education from the Bavarian Universities (2014-2016). Professor Sissouno's research focuses on mathematical methods in signal and image processing, data science, dynamical systems, numerical simulation, and approximation theory. Her work particularly emphasizes spline functions on domains, wavelets and frames, and evidence-based development of teaching methodologies. Her recent publications demonstrate strong expertise in mathematical imaging, phase retrieval problems, and approximation theory, with applications spanning ptychographic imaging, variational inpainting methods, and structural sparsity in multiple measurements. Her collaborative research bridges theoretical mathematics with practical applications in signal processing and image analysis, with a particular focus on developing robust numerical algorithms for complex data analysis problems. She has published in prestigious journals including Advances in Computational Mathematics, Journal of Fourier Analysis and Applications, IEEE Transactions on Signal Processing, and Inverse Problems. Referentin für Talentmanagement & Diversity at TUM (2022-2023) Deputy spokesperson of Research Associates' Council of the TUM (2019-2023) Gender equality officer of Department of Mathematics (2019-2022) Professor Sissouno teaches mathematics courses for engineering and computer science students, with a focus on making mathematical concepts accessible and relevant to practical applications. She has been involved in teacher training and the evidence-based development of teaching methodologies, demonstrating her commitment to both research excellence and educational innovation.

Varun Shankarمشاهده پروفایل

استادیار

• Scientific Machine Learning
• Scientific Computing
• High Performance Computing
• +۳ مورد دیگر

Varun Shankar is an Assistant Professor (tenure-track) at the Kahlert School of Computing (KSoC), University of Utah, and currently serves as Associate Director of the Master of Software Development (MSD) program. His research focuses on developing efficient machine learning models for scientific applications, blending scientific computing, machine learning, and high-performance computing. Previously, he held a non-tenure-track role as an Assistant Professor Lecturer in KSoC, emphasizing teaching and curriculum development for the MSD program. Varun earned his PhD in Computing (Scientific Computing) from the University of Utah and completed a postdoctoral fellowship in the Math Biology group at the Department of Mathematics, University of Utah. His research interests span scientific machine learning, numerical analysis, and computational methods for complex systems. Recent work emphasizes physics-informed neural networks (PINNs), hybrid models for PDE solving, and meshless discretization techniques. His publications reflect contributions to operator learning, RBF-FD methods, and computational biomechanics. In teaching, Varun has instructed courses in software development, mobile programming, and numerical analysis, emphasizing practical skills for software professionals. He has co-advised MS and PhD students across KSoC and Mathematics departments.

Keren Liمشاهده پروفایل

استادیار

• Distributed Learning
• Federated Learning
• Deep Learning
• +۷ مورد دیگر

Keren Li is an Assistant Professor in the Department of Mathematics at the University of Alabama at Birmingham (UAB), affiliated with the College of Arts and Sciences. She holds a PhD in Statistics from the University of Illinois at Chicago (2018), an MS in Mathematics from Louisiana State University (2004), and a BA in Mathematics from Nankai University (2001). Before joining UAB in 2022, she served as an Associate Scientist at UAB's Informatics Institute (2023–2024) and a Postdoctoral Fellow at Northwestern University’s NSF-Simons Center for Quantitative Biology and Department of Statistics and Data Science (2018–2022). Her research focuses on distributed learning, federated learning, deep learning, bioinformatics, and financial mathematics. Key projects include developing tools like DNAcycP (predicting DNA cyclizability) and RiboDiPA (analyzing ribosome profiling data). She also explores statistical methods for optimal design and generalized linear models. Her work bridges computational methods and real-world applications, with contributions to genomics, energy networks, and financial modeling. Her lab ( klilab ) emphasizes interdisciplinary collaboration and innovative algorithm development.

Eleni Vasilakiمشاهده پروفایل

استاد

• Bioinspired Machine Learning
• Neuromorphic Computing
• Computational Neuroscience
• +۳ مورد دیگر

Professor Eleni Vasilaki holds the Chair in Bioinspired Machine Learning at the University of Sheffield's School of Computer Science, where she serves as Head of the Machine Learning research group and member of the Complex Systems Modelling research group. She joined as Lecturer in 2009 and became Professor in 2016. Education includes: Bachelor's in Informatics and Telecommunications from University of Athens Master's in Microelectronics from University of Athens DPhil in Computer Science and Artificial Intelligence from University of Sussex Research focuses on developing novel machine learning techniques inspired by biological principles, particularly in reinforcement learning and reservoir computing methods. Her team collaborates with material scientists and engineers to design neuromorphic computing hardware. Publications demonstrate strong emphasis on neuromorphic computing, machine learning applications in neuroscience, reservoir computing systems, and computational modeling of biological processes. Recent work addresses device-agnostic modeling, physical neural networks, and stochastic computing platforms. Significant grants include: MARCH: Magnetic Architectures for Reservoir Computing Hardware (£936,815, Co-PI) ActiveAI - active learning and selective attention for robust AI (£953,584, Co-PI) Modeling probabilistic reinforcement learning in Drosophila (Google, £50,769, PI) CausalXRL: Causal explanations in Reinforcement Learning (£309,915, PI) Brains on Board: Neuromorphic Control of Flying Robots (£2,128,934, Co-PI) Leads the Machine Learning research group and collaborates with the Complex Systems Modelling group, focusing on brain-inspired computing architectures and neuromorphic hardware design.

Zhilin Liمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations
• +۵ مورد دیگر

Zhilin Li is a Professor in the Department of Mathematics at North Carolina State University. He is affiliated with the College of Sciences and specializes in numerical analysis, scientific computing, and partial differential equations. His research focuses on developing numerical methods for interface problems, irregular domains, and complex fluid dynamics systems. Education: PhD in Applied Mathematics from the University of Washington (1994). Research interests include: numerical methods for PDEs with free boundaries, finite difference/element methods, computational fluid dynamics (CFD), and biological flow simulations. He has contributed to advancing high-order compact schemes, immersed interface methods, and adaptive finite element techniques. Recent work emphasizes solving anisotropic diffusion problems, moving contact line dynamics, and multiphase flow challenges in engineering and biomedical contexts. His methods address accuracy and stability in complex geometries and discontinuous coefficients. Key contributions include the Immersed Interface Method (IIM) for interface problems and novel finite difference schemes for irregular domains. His research spans applications from petroleum engineering (wellbore stability) to neuroscience (neuroregeneration). Grants and collaborations are implied through his publications, though specific funding details are not listed here. He is actively involved in interdisciplinary projects combining mathematics with engineering and life sciences.

Eric Bradlowمشاهده پروفایل

استاد

• Bayesian computation
• Marketing research methods
• Psychometrics
• +۳ مورد دیگر

Eric Bradlow is the K.P. Chao Professor of Marketing at the Wharton School, University of Pennsylvania. He serves as Vice Dean of Analytics at Wharton and Chairperson of the Wharton Marketing Department. Additionally, he holds professorships in Economics, Education, and Statistics and Data Science. His research focuses on Bayesian methods, consumer behavior, and marketing analytics. Bradlow's academic journey includes a PhD and Master's in Mathematical Statistics from Harvard University, and a BS in Economics from the University of Pennsylvania. His work bridges academia and industry, emphasizing practical applications of statistical methodologies in marketing challenges such as loyalty programs, media effectiveness, and consumer decision-making. His recent research explores topics like retail media impact, online learning dynamics, and Bayesian modeling techniques. Notable contributions include analyzing cross-reward effects in loyalty programs and developing interpretable whole-brain decoders using neuroimaging data. He has also contributed to advancing statistical reporting standards in academic research. Bradlow's leadership roles include editorial work for journals like Marketing Letters and fostering interdisciplinary collaboration through analytics initiatives at Wharton. His research has been applied in diverse domains from healthcare outcomes to cultural evolution studies using big data.

• Statistics
• Applied Mathematics
• Systems Biology
• +۲ مورد دیگر

Dr. Hannes Matuschek is a Researcher in the Department of Applied Mathematics at the University of Potsdam, Germany, with office space 2.09.1.24 and contact email hannes.matuschek@uni-potsdam.de. He actively participates in the institute's academic events including working group seminars and colloquia. Research Interests: His work spans Statistics, Applied Mathematics, Systems Biology, and Biomechanics. Key contributions include statistical methodology for linear mixed models (addressing Type I error/power tradeoffs and interaction effects), smoothing spline ANOVA for eye movement analysis in reading, stochastic modeling of gene regulatory networks, and vector field manipulations on spherical domains. His research bridges theoretical frameworks with applications in cognitive science, systems biology, and fraud detection. Publication Trends: His 12 publications (2012-2019) reveal a trajectory from computational systems biology (stochastic biochemical kinetics tools like iNA) toward statistical methodology development. Early work focused on noise approximation in gene networks, evolving into eye-tracking analysis and vector field mathematics, demonstrating consistent integration of advanced statistics with domain-specific challenges across biological and cognitive sciences. Scientific Awards: No awards were mentioned in the provided materials. Advising and Grants: No information was provided regarding student supervision, grant funding, or research collaborations. Labs and Teams: He operates within the Applied Mathematics research group at the University of Potsdam, contributing to seminars and events in analysis and interdisciplinary applications as evidenced by his institutional presence.

Julien Fageotمشاهده پروفایل

پژوهشگر

• Sparsity
• Random Processes
• Approximation Theory
• +۷ مورد دیگر

Julien Fageot is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in the AudioVisual Communications Laboratory within the School of Computer and Communication Sciences. He previously held postdoctoral positions at Harvard University, McGill University, and EPFL. His educational background includes: Ph.D. in Electrical Engineering at EPFL (2012-2017) M.Sc. Mathematics, Vision, and Learning in ENS Paris-Saclay, France (2011) M.Sc. in Probability and Statistics at Université Paris Orsay, France (2009) École Normale Supérieure, Section Mathématiques, Paris, France (2007-2012) Dr. Fageot's research lies at the intersection of high-level mathematics and data sciences, focusing on mathematical properties of advanced processing tools for sparse signal reconstruction and synthesis. His expertise spans sparsity, random processes, approximation theory, splines, convex optimization, functional analysis, and signal/image processing. He explores probability theory (sparse stochastic processes), optimization theory (sparsity-promoting spline reconstruction), and applications in signal processing (inverse problems, segmentation, detection, CNNs). His publication record demonstrates a clear progression from theoretical foundations of stochastic processes to practical applications in biomedical imaging. Recent work shows increasing focus on machine learning applications while maintaining strong mathematical rigor, particularly in developing sparse representations for medical image analysis. His scientific achievements have been recognized with: Best Paper Award at the MIDL Conference (2019) EPFL Best Doctorate Award (2018) Outstanding PhD Thesis Distinction in Electrical Engineering, EPFL (2017) Education Award from the Life Science Department, EPFL (2013) Dr. Fageot actively mentors students, currently supervising PhD candidate Adrian Jarret and having previously guided Thomas Debarre and Shayan Aziznejad to completion. He has supervised numerous master's theses on spline-based reconstruction and biomedical image analysis. His research is supported by Swiss National Science Foundation grants including the Postdoc.Mobility fellowship for 'Mathematical Models for Analog Data Sciences: the Continuous Way' (2020) and the Early Postdoc.Mobility fellowship for 'Probabilistic and Variational Methods for Sparse Signals' (2018). As a key member of EPFL's AudioVisual Communications Laboratory, he collaborates with Prof. Martin Vetterli, Prof. Michael Unser, and Prof. Christian Genest, bridging theoretical mathematics with practical applications in signal processing and data science.

Thanh Tranمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Methods for Partial Differential Equations (PDEs)
• Stochastic Differential Equations
• +۲ مورد دیگر

Professor Thanh Tran is a faculty member at the School of Mathematics & Statistics, University of New South Wales (UNSW Sydney). His research focuses on computational mathematics, particularly numerical methods for deterministic and stochastic partial differential equations (PDEs), with applications in physical sciences, engineering, and magnetic materials. He has secured multiple competitive research grants, including ARC Discovery Projects and international collaborations. Education: PhD from UNSW Sydney Research Grants: ARC Discovery Projects (2012-2026), UNSW Goldstar Awards (2011, 2015), Go8-Germany Joint Research Scheme (2013) Teaching: Courses include Stochastic Differential Equations (MATH3361/5361), Mathematics for Actuarial Studies (MATH1251), and Applied Analysis (MATH3051) His work on numerical methods spans preconditioning techniques, error estimation, adaptivity, and finite element-boundary element coupling. Recent publications emphasize stochastic PDEs and nonlinear evolution equations, with applications in magnetohydrodynamics and chemotaxis models. Scientific awards highlight his contributions to interdisciplinary mathematics, particularly through collaborative projects on magnetic memory materials and random boundary problems. He actively supervises PhD and Honours students in computational mathematics, focusing on Maxwell-Landau-Lifshitz-Gilbert equations and shape calculus.

Ullika Scholzمشاهده پروفایل

پژوهشگر

• Applied Mathematics
• Fluid Dynamics
• Computational Engineering
• +۵ مورد دیگر

Ullika Scholz is a Ph.D. candidate at the Institute of Applied Mathematics (ACoM) within the Faculty of Mathematics, Computer Science and Natural Sciences at RWTH Aachen University, Germany. Ph.D. candidate (03/2020–present) M.Sc. in Mathematics (Free University of Berlin, 2019) B.Sc. in Mathematics (Free University of Berlin, 2014) Her research focuses on dimensionally reduced equation systems for incompressible free surface flows, extending moment approximations and model cascades for shallow flow. She works with PDE systems derived via vertical polynomial expansions, incorporating dispersive effects beyond hydrostatic pressure assumptions. Her scientific work intersects applied mathematics, computational fluid dynamics, and numerical analysis. She has participated in international conferences such as ICCE 2024 and MMKT IV, presenting research on shallow moment equations and spline basis functions for kinetic theory models. Rosa-Luxemburg-Foundation Scholarship recipient (2013–2019)