جست‌وجوی استاد و پژوهشگر

Stefanie Hahmannمشاهده پروفایل

استاد

• Computer Graphics
• Geometric Modeling
• 3D Shape Design
• +۵ مورد دیگر

Stefanie Hahmann is a Professor at Grenoble INP – Ensimag, France, and a member of the INRIA Anima team within Laboratoire Jean Kuntzmann. She has held visiting professorships at Freie Universität Berlin (2023–2024) and Universität Leipzig (2022–2023). Her academic journey includes a PhD in Computer Science from TU Kaiserslautern (1994), an Habilitation from Grenoble INP (2001), and earlier degrees from TU Braunschweig and University Paris VI. Her research focuses on computer graphics and geometric modeling , with emphasis on shape modeling, computational design, and fabrication. Key areas include sketch-based modeling, surface reconstruction, and multiresolution methods. She leads the ADAM2 project (EU Horizon 2020) on microstructure-based design and manufacturing. Education: Habilitation, Computer Science, Grenoble INP, 2001 PhD, Computer Science, TU Kaiserslautern, 1994 M.S., Applied Mathematics, University Paris VI, 1989 B.S., Mathematics, TU Braunschweig, 1986 Recipient of prestigious awards, including the Gay-Lussac Humboldt Prize (2022) and SMA Fellowship (2019) . She has organized major conferences like Shape Modeling International and served on editorial boards of Computer Aided Design and Computers & Graphics . Advising & Grants: Supervised over 14 PhD students, with grants including the FET-Open ADAM2 project (2020–2024). Active in academic governance, including chairing the Mathematical Modeling department at Ensimag (2013–2022). Labs/Teams: INRIA Anima team and Laboratoire Jean Kuntzmann (LJK), focused on geometric modeling and computational design.

Markus Steinbergerمشاهده پروفایل

دانشیار

• GPU Computing
• Computer Graphics
• Parallel Processing
• +۳ مورد دیگر

Markus Steinberger is an Associate Professor at Graz University of Technology (Austria), leading the GPU Computing and Visualization Group at the Institute for Computer Graphics and Vision. He holds an MSc and PhD in Computer Science from TU Graz, with a postdoc and multiple leadership roles in academic and industry labs, including NVIDIA and the Max Planck Institute. His research focuses on GPU scheduling, parallel computing, and high-performance visualization techniques. He has led teams at TU Graz, Huawei Technologies (Cloud Rendering Lab), and the Max-Planck-Center for Visual Computing. Education 11/2020: Habilitation in Practical Computer Science at TU Graz 10/2013: PhD in Computer Science (Dynamic Resource Scheduling on Graphics Processors) 10/2010: MSc in Telematics 10/2005: BSc in Telematics Research Interests Steinberger’s work centers on optimizing GPU computing for real-time rendering and parallel processing. He explores efficient resource scheduling, dynamic graph management, and high-performance visualization techniques, with applications in cloud rendering, scientific visualization, and interactive graphics. His methods emphasize scalability and real-world applicability in both academic and industrial settings. Awards Heinz Zemanek Prize (2016) – First Austrian recipient GI Dissertation Prize (2014) – Best dissertation in German-speaking computer science Multiple Eurographics Best Paper Awards (2021, 2020, 2014) HPEC Best Student Paper (2017) ACM CHI Honorable Mention (2014) Advising & Grants Steinberger has advised numerous students and led projects funded by EU initiatives and industry partnerships. His research has been recognized through grants supporting GPU scheduling, cloud rendering, and parallel computing. Labs & Teams Leads the GPU Computing and Visualization Group at TU Graz and directed the Cloud Rendering Lab at Huawei, focusing on scalable rendering solutions for distributed systems.

Kannan Ramchandranمشاهده پروفایل

استاد

• Information Theory
• Signal Processing
• Artificial Intelligence
• +۵ مورد دیگر

Kannan Ramchandran is the Gilbert Henry Gates Endowed Chair Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. He holds a Ph.D. from Columbia University (1993) and has been at UC Berkeley since 1999. His research focuses on information theory, signal processing, machine learning, and AI, with contributions to distributed coding, federated learning, and robust video transmission. He leads the Berkeley Audiovisual Signal Processing and Communication Systems (BASiCS) Lab and is affiliated with the Simons Institute and CLIMB. Awards include the IEEE Kobayashi Award (2017) and the NSF CAREER Award (1997). He teaches courses like EE 229A (Information Theory) and has advised numerous students in areas like coding theory and distributed systems. Education: B.E., City College of New York (1982) M.S., Columbia University (1984) Ph.D., Columbia University (1993) Research Interests: Distributed source coding, federated learning frameworks, robust communication systems, and AI-driven signal processing. His work bridges theory and practice, with impactful contributions to video compression, error-resilient systems, and modern machine learning paradigms. Key Contributions: Pioneered DISCUS (Distributed Source Coding using Syndromes), developed SAFFRON for group testing, and advanced federated learning methodologies. His lab’s work on spectrum-blind sampling and diffusion models for MRI reconstruction highlights ongoing innovation. Awards: IEEE Fellow (2004), Okawa Grant (2000), and multiple teaching awards. His research has been published in top venues like IEEE Transactions on Information Theory and Neural Information Processing Systems.

Şahin Serhat Şekerمشاهده پروفایل

استاد

• Electric Motor
• Induction Motor
• Vibration Signal
• +۵ مورد دیگر

Şahin Serhat Şeker is a Professor in the Department of Electrical Engineering at Istanbul Technical University (ITU), Istanbul, with continuous research activity since 1993. He maintains an active publication record through 2025, demonstrating ongoing academic engagement, and has accumulated 111 research outputs with 1084 citations and an h-index of 19. His research centers on power system analysis and electric motor diagnostics, specializing in vibration signal processing for fault detection in induction motors. He employs continuous wavelet transform for non-stationary signal analysis to identify bearing damage, aging processes, and fault signatures. Recent work integrates machine learning for renewable energy forecasting and power grid stability, reflecting a strategic shift toward AI-driven solutions for energy infrastructure challenges. Professor Şeker's 2023-2025 publications reveal a strong trend toward deep learning applications in transient instability prediction, solar irradiance forecasting, and photovoltaic system optimization. His work increasingly addresses real-world energy challenges in regions like Djibouti, focusing on grid stability and renewable integration through hybrid modeling approaches. He has supervised 13 students and secured research funding through two major projects: SENSOR VALIDATION AND FUSION FOR SYSTEM MONITORING (2018-2019): Developed sensor validation frameworks for industrial system monitoring Analysis of Ferroresonance in Power Systems Using Multiresolution Wavelet Analysis and Extraction of Nonlinear Properties (2013-2018): Investigated nonlinear phenomena in power grids through advanced signal processing

Steven Fentonمشاهده پروفایل

مدرس ارشد

• Low Power Systems
• Health Innovation
• Remote Health/Wound Monitoring
• +۶ مورد دیگر

Steven Fenton is a Senior Lecturer and Subject Area Leader in Engineering at the Department of Engineering & Technology, School of Computing and Engineering, University of Huddersfield. He holds a 1st Class Honours degree in Electronic & Information Engineering from the University of Huddersfield and has extensive industry experience in audio engineering, DSP, and embedded systems design. Current research focuses on low power systems, remote health monitoring, audio signal processing, and immersive audio technology. Active member of the Audio Engineering Society and Centre for Audio and Psychoacoustic Engineering. His work spans audio quality measurement , dynamic range optimization , and assistive technologies for the visually impaired. Recent publications highlight innovations in immersive audio mixing and ultra-low energy distributed monitoring systems. Scientific contributions include a Fellow of the Higher Education Academy and an h-index of 67. Available for PhD supervision in engineering and audio technology domains.

• Numerical Analysis
• Iterative Methods
• Nonlinear Equations
• +۷ مورد دیگر

Vicente Fco Candela Pomares is an Associate Professor in the Department of Mathematics at the Faculty of Mathematics, University of Valencia, Spain. His academic career has been centered on numerical analysis and computational mathematics, with a focus on iterative methods for nonlinear equations and multiresolution techniques. His research interests lie primarily in Numerical Analysis , especially iterative root-finding methods such as Halley, Chebyshev, and Steffensen-type algorithms. He has contributed significantly to the convergence analysis of these methods, particularly in Banach spaces and for ill-conditioned problems. His work extends to multiresolution analysis , wavelets , and image restoration , where he applies fractional regularization and nonlinear approximation frameworks. The trends in his recent publications show a sustained focus on derivative-free iterative methods , convergence theory , and applications in image processing . His work often bridges theoretical numerical analysis with practical computational challenges. He earned his PhD from the University of Valencia in 1988 under the supervision of Dr. Antonio Marquina Vila, with a thesis on a priori error estimators for iterative methods. He has collaborated extensively with researchers including Sergio Amat, Sonia Busquier, and Rosa Peris. Notable co-authors include Pantaleón D. Romero and Francesc Aràndiga. His publications appear in high-quality journals such as Journal of Computational and Applied Mathematics , Applied Mathematics and Computation , and SIAM journals. He is actively affiliated with the University of Valencia, as evidenced by his institutional email and ongoing publications. There is no indication of part-time status, retirement, or awards in the available data.

• Applied Mathematics
• Numerical Relativity
• Computational Astrophysics
• +۶ مورد دیگر

Isabel Cordero Carrión is an Associate Professor in the Department of Mathematics at the Faculty of Mathematics, Universitat de València. She is affiliated with two research groups: CompAC (Computational Astrophysics and Cosmology Group) and ANIMS (Numerical Analysis, Images, Multiresolution and Simulation). Institution: Universitat de València School: Faculty of Mathematics Department: Department of Mathematics Research Groups: CompAC, ANIMS Her research focuses on applied mathematics, particularly in numerical relativity and computational astrophysics. She works on the numerical evolution of Einstein's equations, relativistic hydrodynamics, and the development of stable and accurate numerical schemes for general relativistic systems. Her work bridges differential geometry, partial differential equations, and high-performance computing. The recent articles show a consistent trend in advancing numerical methods for general relativity, including formulations of the Einstein and hydrodynamic equations, implicit time integration, and spherical coordinate implementations. Her work emphasizes stability, constraint preservation, and computational efficiency in simulating relativistic systems. No scientific awards are listed in the provided text. She completed her Ph.D. in 2009 at the Universitat de València under the supervision of Dr. José María Ibáñez. While no formal students are listed, her collaborative work includes co-authorship with researchers such as Pablo Cerdá-Durán, Miguel-Ángel Aloy, and Jérôme Novak. She has not received any mentioned grants in the text. She is a member of the CompAC and ANIMS research groups, which focus on computational astrophysics, numerical analysis, and simulation of physical systems using advanced mathematical methods.

Frank Haußerمشاهده پروفایل

استاد

• Numerical Mathematics
• Scientific Computing
• Mathematical Modeling
• +۴ مورد دیگر

Prof. Dr. Frank Haußer is a Professor at Beuth University of Applied Sciences Berlin in the Department II Mathematics - Physics - Chemistry. He serves as the academic advisor for the Applied Mathematics program and teaches courses including Numerical Mathematics, Mathematical Methods of Digital Image Processing, and Computational Engineering. His consultation hours are held during semesters and by appointment, with availability both in-person and online. Haußer's research focuses on: Numerical mathematics and scientific computing Mathematical modeling with applications in MATLAB/Octave Machine learning for medical imaging and insect monitoring Digital image processing techniques for biomedical applications Partial differential equations and computational engineering methods He has authored a textbook on mathematical modeling with MATLAB/Octave and leads interdisciplinary projects at the intersection of mathematics and technology. Analysis of his 15 most recent publications shows strong emphasis on: Medical imaging algorithms (especially retinal OCT analysis) Nanostructure dynamics and material science Computational physics and finite element methods Machine learning applications in biology and medicine Advanced mathematical modeling techniques His work consistently bridges theoretical mathematics with practical engineering applications. Haußer actively supervises student research, including: Machine learning for insect classification and localization Medical image processing algorithms Computational methods in engineering Finite element analysis applications Optimization and simulation techniques He has guided over 30 bachelor's and master's theses since 2009. His research projects include: KInsekt (2020-2023): AI-based insect monitoring funded by BMUV BeCRF (2015-2017): Medical image quality validation funded by BMWi QM ROCT (2013-2015): Automated OCT quality management funded by IFAF These interdisciplinary collaborations involve institutions across Germany.

Sarah Eberle-Blickمشاهده پروفایل

مدرس ارشد

• Numerical Analysis
• Inverse Problems
• Partial Differential Equations
• +۷ مورد دیگر

Sarah Eberle-Blick is a Senior Lecturer at the Institute of Mathematics , Goethe University Frankfurt, Department of Computer Science and Mathematics. Her work bridges numerical methods, inverse problems, and wave propagation in elasticity. Research Focus : Numerical analysis of PDEs, monotonicity methods for inclusion detection, FEM-BEM coupling, multiscale seismic data processing. Key Contributions : DFG-funded projects on elastic wave reconstruction; development of stable integral formulations for acoustic and thermoelastic wave equations; implementation of 3D wave simulations. Article Trends : Recent papers emphasize inverse problems in linear elasticity, time-harmonic wave equations, and multiscale analysis using wavelets. Collaborative work spans geophysics, computational mechanics, and mathematical modeling. Projects : Includes monotonicity-based regularization, Lipschitz stability estimation, and FEM-BEM coupling with convolution quadrature. Contact: eberle@math.uni-frankfurt.de | Room 103, Institute of Mathematics, Frankfurt am Main, Germany.

Knut Martin Mørkenمشاهده پروفایل

استاد

• Approximation Theory
• Numerical Analysis
• Spline Functions
• +۴ مورد دیگر

Knut Martin Mørken is a Professor and Vice Dean for Education at the Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Oslo. His academic career spans both research and leadership in computational mathematics and science education reform. Roles: Vice Dean of Education (since 2019), Leader of the CCSE (Centre for Computing in Science Education), Former leader of the InterAct educational development project (2012–2019) Teaching: Co-instructor of MAT-INF1100 and INF-MAT5340 courses Research Interests: Approximation theory with focus on spline functions, B-splines, wavelets, and geometric modeling Integration of computational methods into mathematics and science education, spanning the CSE (Computers in Science Education) project and its successor, the CCSE (now a national Centre of Excellence in Education) Scientific Contributions: His work includes algorithm development for function approximation, stability analysis of wavelets on nonuniform triangulations, and interdisciplinary studies on mathematics-computing integration in education. Awards & Recognition: CCSE awarded Centre of Excellence in Education (SFU) by DIKU Collaborations: Active partnerships across departments and institutions, including collaborations with Lyche, Reif, Lockwood, Caballero, and Melvær in research and educational initiatives.

Bradford L. Chamberlainمشاهده پروفایل

عضو هیئت علمی

• Parallel programming languages
• Compilers for high-performance computing
• Scalable parallel algorithms
• +۳ مورد دیگر

Bradford L. Chamberlain is a Distinguished Technologist at Hewlett Packard Enterprise and an Affiliate Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. He is the founding technical lead of the open-source Chapel programming language, which targets productive parallel computing from laptops to supercomputers. Education: Ph.D. in Computer Science & Engineering, University of Washington, 2001 M.S. in Computer Science & Engineering, University of Washington, 1995 B.S. (with honors) in Computer Science, Stanford University, 1992 Research interests center on user productivity for high-performance computing, spanning programming languages, compilers, algorithms, and applications. His work emphasizes scalable parallel computation and the design of language abstractions—such as regions, distributions, and iterators—that enable both high productivity and high performance. Over the past two decades he has led the Chapel project from a small research effort to a thriving open-source language with nearly twenty full-time developers at HPE and numerous external collaborators. His recent publications explore locality-aware compiler optimizations, fabric-attached memory, and the expression of sophisticated parallel patterns like stencil tiling and data re-allocation. Teaching & service: He created and regularly teaches UW’s graduate parallel computation course (CSEP 524), has advised graduate students, and serves on departmental committees, fostering tight ties between the Allen School and HPE/Cray.

Nurdal Watsujiمشاهده پروفایل

استادیار

• Electrical-Electronics Engineering
• Image Processing
• Signal Processing
• +۱ مورد دیگر

Nurdal Watsuji serves as Assistant Professor in the Department of Electrical and Electronics Engineering at Gaziantep University's Faculty of Engineering, specializing in Electrical-Electronics Engineering with research emphases on Image Processing, Signal Processing, and Digital Design. His academic foundation includes: Doctorate in Electrical and Electronic Engineering from Imperial College London (1987-1991) Degree in Electrical-Electronic Engineering from Middle East Technical University, Gaziantep (1982-1985) Licence in Electrical Engineering from Middle East Technical University, Gaziantep (1975-1981) Watsuji's research integrates theoretical signal processing with practical engineering applications, particularly in LED/OLED lighting systems and image analysis. His work demonstrates consistent innovation in optical efficiency optimization, compressed sensing algorithms, and wavelet-based texture classification, bridging electrical engineering principles with real-world implementation challenges in energy systems and computer vision. Publication analysis from 2003-2016 reveals evolving expertise from foundational wavelet techniques to advanced optical efficiency modeling in solid-state lighting. The recurring theme involves mathematical optimization of signal/image processing methods for specific engineering constraints, with significant contributions to nonconvex compressed sensing and LED reflector design. His graduate mentoring spans eight students: PhD candidates: İpek Atık (2016) on LED reflector optimization and Taner Ince (2012) on compressed sensing Master's students: Ugur Polat (2022) on energy analyzers, Gulden Akay Ozbay (2008) on hand recognition, Taner Ince (2006) on medical image processing, Recep Oz (2004) on 3D rendering, Omer Cengiz Celebi (2003) on volume rendering, and Bayram Kara (2003) on wavelet texture analysis This diverse portfolio reflects sustained engagement with computational methods across lighting systems, biomedical imaging, and computer graphics applications.

Nira Dynمشاهده پروفایل

استاد

• Geometric Modeling
• Subdivision methods
• Multivariate approximation theory
• +۴ مورد دیگر

Nira Dyn is a Professor of Applied Mathematics at Tel Aviv University's School of Mathematics, where she has established herself as a leading researcher in geometric modeling and approximation theory. Her academic career spans decades of contributions to subdivision methods and computational mathematics, with a consistent focus on both theoretical foundations and practical applications in computer graphics and image processing. Research Interests Professor Dyn's primary research areas include Geometric Modeling , Subdivision methods , and Multivariate approximation theory , with significant contributions to Computer-Aided Geometric Design (CAGD) and Image Compression. Her current work centers on Nonlinear subdivision schemes and the Approximation of set-valued functions , representing cutting-edge extensions of classical approximation theory to handle complex geometric structures and uncertain data. These interests form a cohesive research program that bridges pure mathematical analysis with computational applications, particularly in handling geometric data through innovative subdivision techniques. Publication Trends Analysis of her recent publications reveals a strong emphasis on advancing subdivision methodologies beyond linear frameworks, with increasing focus on nonlinear schemes capable of handling complex geometries and set-valued data. Her work demonstrates consistent progression from foundational subdivision theory toward practical applications in image compression and geometric modeling, with notable contributions to metric-based approximation techniques. The publications showcase interdisciplinary reach spanning mathematics, computer science, and engineering applications, while maintaining rigorous mathematical foundations in approximation theory. Professional Activities While specific advising relationships and grant information aren't detailed in the available materials, Professor Dyn's extensive publication record in top-tier journals indicates active research leadership. Her collaborations span multiple institutions and disciplines, reflecting the interdisciplinary nature of modern geometric modeling research. The absence of explicit laboratory or team information suggests her work may be primarily theoretical or conducted through collaborative networks rather than a dedicated physical research space.

Oleg Davydovمشاهده پروفایل

استاد

• Meshless Finite Difference Method
• Finite Element Method
• Scattered Data Fitting
• +۴ مورد دیگر

Professor Oleg Davydov holds a Professorship for Numerical Analysis at the Department of Mathematics, University of Giessen, Germany. His research focuses on developing advanced numerical methods with strong theoretical foundations and practical applications. He maintains an active research program with numerous recent publications and international collaborations. Position: Professor of Numerical Analysis Institution: University of Giessen, Department of Mathematics Contact: Heinrich-Buff-Ring 44, 35392 Giessen, HRZ Room 117 Email: oleg.davydov@math.uni-giessen.de Homepage: https://oleg-davydov.de/ Professor Davydov's research interests center around meshless numerical methods, approximation theory, and computational mathematics. His primary focus areas include: Meshless Finite Difference Method - Developing robust meshless techniques that avoid the need for structured grids Finite Element Method - Particularly Bernstein-Bézier finite elements and specialized approaches for complex geometries Scattered Data Fitting - Creating efficient algorithms for approximating data on irregular domains Approximation Theory - Investigating theoretical properties of splines, radial basis functions, and other approximation tools His research has resulted in several software packages including mFDlab (Meshless Finite Difference Method), BBFEM (Bernstein-Bézier Finite Elements), and TSFIT (Two-Stage Scattered Data Fitting), demonstrating the practical implementation of his theoretical work. Analysis of Professor Davydov's recent publications shows a consistent focus on improving meshless methods, particularly in stencil selection, error analysis, and applications to complex problems. His work spans both theoretical developments (like error bounds and optimal approximation orders) and practical implementations (for fluid dynamics, manifold learning, and interface problems). A notable trend is the increasing sophistication of adaptive techniques and the handling of challenging geometries. Professor Davydov has supervised several doctoral students to completion, including: Gaelle Andriamaro Fabien Rabarison Abid Saeed Wee Ping Yeo His research group appears to maintain active collaborations with institutions worldwide, as evidenced by his extensive co-authorship network. The group focuses on developing both theoretical foundations and practical implementations of numerical methods, with particular attention to problems involving irregular domains, singularities, and complex geometries. Students in his group would gain experience in both theoretical analysis and software development for numerical methods.

Vicente Alarcon-Aquinoمشاهده پروفایل

استاد

• Cybersecurity
• Network Monitoring
• Anomaly Detection
• +۷ مورد دیگر

Dr. Vicente Alarcon-Aquino is a Professor in the Department of Computing, Electronics, and Mechatronics at Universidad de las Americas Puebla (UDLAP), Mexico. He received his Ph.D. and D.I.C. degrees in Electrical and Electronic Engineering from Imperial College London in 2003. He previously served as department head from October 2012 to June 2018 and spent a research stay at King's College London in 2017. Dr. Alarcon-Aquino is a Senior Member of IEEE, a Level I member of the Mexican National System of Researchers (SNI), and a Fellow of the Mexican Academy of Sciences. His educational background includes: Ph.D. and D.I.C. in Electrical and Electronic Engineering, Imperial College London, UK (2003) Dr. Alarcon-Aquino's research focuses on cybersecurity, network monitoring, anomaly detection, wavelet analysis, and machine learning. His work spans theoretical foundations to practical applications in network security, with significant contributions to intrusion detection systems, cryptographic techniques, and machine learning approaches for security applications. He has developed innovative methods combining wavelet transforms with neural networks for various security and signal processing applications. His scholarly contributions include over 180 research articles in refereed journals and conference proceedings, a book on MPLS networks, and numerous citations. As an editor, he serves as Associate Editor for IEEE Access Journal and as Academic & Section Editor for PeerJ Computer Science, focusing on Security & Privacy. Notable professional recognitions include: Senior Member of IEEE Mexican National System of Researchers (SNI Level I) Member of the Mexican Academy of Sciences Dr. Alarcon-Aquino has supervised over 70 theses, including 8 Ph.D. dissertations, 15 Master's theses, and more than 37 Bachelor's theses. His supervision spans topics including network intrusion detection, information security, encryption algorithms, wavelet-based signal processing, neural networks, EEG signal processing, and biometric cryptosystems. He has hosted international research students from institutions including the Polytechnic University of Madrid and Kiel University of Applied Sciences. His research group at UDLAP focuses on developing advanced security solutions for modern network environments, with particular emphasis on applying machine learning techniques to cybersecurity challenges. Current projects include blockchain-based security solutions, federated learning approaches for intrusion detection, and advanced anomaly detection systems for IoT environments.