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Raymond O. WellsView profile

Professor

• Complex Analysis
• Wavelet Analysis
• Differential Geometry
• +3 more

Raymond O. Wells is a Distinguished Professor of Mathematics at the School of Computer Science & Engineering, Constructor University. Previously, he held roles as Professor of Mathematics and Vice President for External Affairs at Jacobs University Bremen, and served as Professor Emeritus at Rice University. His academic journey includes a BA in Mathematics from Rice University (1962) and a PhD from New York University (1965). His research focuses on complex analysis, wavelet theory, differential geometry, and the history of mathematics. Notable contributions include foundational work on complex manifolds, cohomology in theoretical physics, and applications of wavelet analysis in signal processing. He has authored influential books such as *Differential Analysis on Complex Manifolds* and *Wavelet Analysis: The Scalable Structure of Information*. Wells has received prestigious awards including the Guggenheim Fellowship (1974–1975) and Humboldt Senior Scientist Award. Beyond academia, he has served as President of the Boulder Philharmonic Orchestra and the Boulder Ensemble Theatre Company. His career reflects a blend of mathematical rigor and interdisciplinary engagement, spanning academic leadership, research innovation, and cultural contributions.

Basile SauvageView profile

Professor

• Computer Graphics
• Procedural Textures
• Geometry Processing
• +5 more

Basile Sauvage is a Professor of Computer Science at the University of Strasbourg and Head of the Computational Geometry & Computer Graphics team at the ICube laboratory. He leads the CS group at the IREM of Strasbourg and holds executive roles in French academic societies (SIF, AFIG) and CNRS research groups. His research focuses on procedural textures, rendering, geometry processing, and virtual reality applications. He has supervised multiple PhD students and led projects like ANR ReProcTex and ASTex. Education: PhD in Applied Mathematics (2005, INP-Grenoble), accredited to supervise research (2018), and certified for higher education teaching (2018). Research interests include texture synthesis, radiance modeling, and multiresolution analysis. Recent work emphasizes real-time procedural textures and distortion correction in graphics. His publications span Eurographics, Siggraph Asia, and Pacific Graphics, with awards including the Best Paper Award at JFIG 2023 and a Graphics Replicability Stamp. He collaborates on scientific mediation projects, such as the AI exhibition at Le Vaisseau. Teaching roles include managing the CS department at the University of Strasbourg’s UFR Mathématique-Informatique. He advises on education policy as part of the Vice-President’s mission for AI-era education. Current PhD supervision includes Arthur Delon (co-advisor) and Romain Fournier. He actively promotes computer science outreach through unplugged activities like 'The Busy Cook' educational module.

• Numerical Analysis
• Optimization on Manifolds
• Riemannian Geometry
• +3 more

Ronny Bergmann is an Associate Professor at the Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU) since March 2021. Previously, he held research positions at TU Chemnitz (2018–2021) and TU Kaiserslautern (2013–2018), where he completed his habilitation in 2018. He earned his PhD in 2013 from Universität zu Lübeck under the supervision of Jürgen Prestin. His research focuses on numerical optimization on Riemannian manifolds, geometric computing, and manifold-valued data analysis. Key contributions include the development of the Manifolds.jl and Manopt.jl packages in Julia, which provide frameworks for optimization and data analysis on manifolds. He also works on applications in imaging, inverse problems, and geometric inverse methods. Recent work emphasizes non-smooth optimization on manifolds, Fenchel duality in non-Euclidean spaces, and geometric algorithms for shape analysis. His publications span journals like SIAM Journal on Scientific Computing , Journal of Optimization Theory and Applications , and Applied and Computational Harmonic Analysis . Bergmann teaches courses such as TMA4125 Mathematics 4N and actively promotes reproducible research through tools like Quarto. He collaborates on projects involving Lie groups, mesh denoising, and geometric deep learning.

• Environmental Science
• Air Quality Modeling
• Machine Learning Applications
• +3 more

Dimple Pruthi is a Research Fellow in the Gangarosa Department of Environmental Health at Emory University. Her work focuses on advanced computational methods for air quality prediction, integrating machine learning, satellite data analytics, and environmental modeling. She specializes in PM2.5 forecasting, wildfire impact analysis, and sustainable pollution monitoring systems. Her research interests include developing hybrid machine learning models for environmental applications, analyzing transient weather effects on pollution, and optimizing neuronal networks for air quality indices. She has extensively studied Delhi's air quality dynamics and California wildfires' particulate matter emissions. Key trends in her publications (2016–2025) reveal a focus on satellite-based PM2.5 prediction using GOES-R data, low-cost sensor systems, and fractal/wavelet analysis for pollutant variability. She combines computational techniques with environmental science to address urban pollution challenges and climate change impacts. No scientific awards or grants are explicitly listed. Her advising record is not provided, but her work contributes to environmental health policy through predictive modeling frameworks.

Qingyang ZhuView profile

Research Fellow

• Air Pollution
• Climate and Health
• Mental Health
• +3 more

Qingyang Zhu is a Postdoctoral Fellow in the Gangarosa Department of Environmental Health at Emory University. His research focuses on machine learning-based air pollution modeling, climate change impacts, and mental health outcomes. He holds a Livingston Fellowship (2022) and has a PhD in Environmental Health Sciences from Emory University (2023). Education: BS, Southeast University MS, Emory University PhD, Emory University (2023) Research interests emphasize integrating machine learning with environmental data to address public health challenges, including air pollution exposure, wildfires' health impacts, and cognitive/mental health outcomes. Key methodologies include satellite remote sensing, epidemiological cohort studies, and spatiotemporal modeling. Recent work highlights wildfire contributions to PM2.5 exposure and mental health crises, warehouse expansion's environmental justice implications, and pandemic-related air quality shifts. His studies frequently employ innovative machine learning models for pollution prediction and health impact assessments. Scientific Awards: Livingston Fellow (2022) Advising and Grants: While no specific grants or advisees are listed, his collaborative work suggests involvement in interdisciplinary environmental health projects. Active participation in laboratory/field teams focused on air quality and health surveillance is implied through publication topics.

David CazierView profile

Professor

• Virtual Reality
• Computer Graphics
• Computational Geometry
• +4 more

David Cazier is a **Professor at the University of Strasbourg** and a **Researcher at the ICube Laboratory**, specializing in geometric and graphical computing. He also teaches computer science at the **IUT of Haguenau**. His research focuses on three main axes: multiresolution geometric models, real-time interaction in virtual reality, and tools for adapting such models to simulations and visualization. His work bridges theoretical advancements in geometric modeling with practical applications in medical imaging, crowd simulation, and augmented reality. **Research Interests**: Cazier’s expertise includes virtual reality (VR) immersion, real-time interaction techniques, and the development of efficient geometric models for complex simulations. His projects often involve hierarchical meshes, topological changes during deformations, and applications in surgical AR. Key areas include multiresolution representations, collision detection in dynamic environments, and non-manifold geometry processing. **Publications**: His recent work emphasizes medical applications (e.g., surgical AR with topological changes) and foundational contributions to geometric data structures (e.g., CGoGN, CPH). He explores user interaction challenges in the digital divide era and develops tools for crowd simulation using deformable agents. **Collaborations and Projects**: Active in collaborations with the ICube Lab’s IGG team, Cazier contributes to platforms like CGoGN and InVirtuo. His research spans national and European projects, including IHU, ANR, and CNRS initiatives. He supervises internships on geometric models for VR applications and interdisciplinary projects combining geometry with medical and industrial challenges.

• Nonparametric statistics
• Causal inference
• Survival analysis
• +3 more

Ted Westling is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on nonparametric statistics, causal inference, survival analysis, and shape-constrained inference. He holds a PhD in Statistics from the University of Washington and a BS in Mathematics from Stanford University. His work emphasizes methodological advancements in causal inference, particularly in observational studies and survival analysis. Notable contributions include developments in nonparametric causal effect estimation, machine learning integration for treatment-specific survival curves, and isotonic regression techniques for monotone functions. Westling's recent publications explore topics such as debiased covariate-adjusted regression, clustered observational studies, and statistical robustness in causal hypothesis testing. His research bridges theoretical statistics with applied problems in healthcare, epidemiology, and social network analysis. He is actively involved in interdisciplinary collaborations, addressing challenges in global health, vaccine efficacy evaluation, and emergency medical services demand forecasting. His methodological innovations aim to enhance the reliability and applicability of statistical methods in real-world settings.

Markus SteinbergerView profile

Associate Professor

• GPU Computing
• Computer Graphics
• Parallel Processing
• +3 more

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 RamchandranView profile

Professor

• Information Theory
• Signal Processing
• Artificial Intelligence
• +5 more

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 ŞekerView profile

Professor

• Electric Motor
• Induction Motor
• Vibration Signal
• +5 more

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 FentonView profile

Senior Lecturer

• Low Power Systems
• Health Innovation
• Remote Health/Wound Monitoring
• +6 more

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
• +7 more

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
• +6 more

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 HaußerView profile

Professor

• Numerical Mathematics
• Scientific Computing
• Mathematical Modeling
• +4 more

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-BlickView profile

Senior Lecturer

• Numerical Analysis
• Inverse Problems
• Partial Differential Equations
• +7 more

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.