جست‌وجوی استاد و پژوهشگر

Mingtao Xiaمشاهده پروفایل

مدرس

• Mathematical Modeling
• Numerical Methods
• Kinetic Theory
• +۳ مورد دیگر

Mingtao Xia is a Courant Instructor at the Courant Institute of Mathematical Sciences, New York University (2023–present). He holds a PhD in Applied Mathematics from UCLA (2019–2023) and a B.S. in Information and Computing Science from Peking University’s School of Mathematical Sciences (2015–2019). His research focuses on mathematical modeling, numerical methods, and machine learning applications in kinetic theories, stochastic systems, and unbounded-domain differential equations. He has contributed to interdisciplinary areas including biological systems, environmental science, and epidemic control through computational methods. Education: PhD in Applied Mathematics, UCLA, 2019–2023 B.S. in Information and Computing Science, Peking University, 2015–2019 His work integrates advanced numerical techniques like spectral methods and Wasserstein-distance frameworks with real-world problems such as gene regulation dynamics, solar wind analysis, and ozone pollution impacts. Recent publications emphasize efficient algorithms for high-dimensional systems and uncertainty quantification in stochastic models. While no awards are listed, his research demonstrates strong contributions to mathematical modeling across diverse domains. Advising and grant details are currently unavailable. His affiliation with the Courant Institute positions him at the forefront of computational mathematics and its applications in interdisciplinary research.

Patrizio Campisiمشاهده پروفایل

استاد

• Digital Signal Processing
• Image Processing
• Biometrics
• +۱۲ مورد دیگر

Patrizio Campisi is a Full Professor in the Section of Applied Electronics at the Department of Engineering, Roma TRE University, Rome, Italy. He leads cutting-edge research in digital signal and image processing with applications to secure multimedia communications and biometrics. He has held visiting positions at the University of Toronto, Beckman Institute (UIUC), and École Polytechnique de Nantes, and has been a Marie Curie Fellow (2010–2014). His educational background includes a Laurea (summa cum laude) in Electronic Engineering from Sapienza University of Rome and a Ph.D. in Electrical Engineering from Roma TRE University. His research focuses on secure biometric recognition (signature, keystroke, EEG, vein), digital watermarking, blind image deconvolution, HDR imaging, and privacy-preserving technologies. He has contributed significantly to template protection, multimodal biometrics, and forensic image analysis. His work bridges engineering, computer science, and security, with strong applications in mobile authentication, border control, and social media safety. The recent publications reflect a strong trend in biometrics, image forensics, and privacy-enhancing technologies, with a focus on real-world applications in mobile systems, healthcare, and law enforcement. Key themes include secure authentication, de-identification, and computational imaging. IEEE Second International Conference on Biometric Systems 2008 Best Student Paper Award IEEE Biometric Symposium 2007 Best Paper Award IEEE International Conference on Image Processing 2006 Best Student Paper Award Marie Curie Fellow (2010–2014) NATO-CNR Advanced Fellowship (2003) IEEE Senior Member Prof. Campisi has supervised numerous students and leads the BioMedia4n6 lab. He has secured major EU grants including H2020 projects AMBER, COSMOS, and ENCASE. His editorial leadership includes serving as Editor-in-Chief of IEEE Transactions on Information Forensics and Security (2018–2020) and Chair of the IEEE Information Forensics and Security Technical Committee (2017–2018). He is actively involved in research networks such as COST Actions on de-identification and biometrics-forensics integration, and has contributed to EU policy via the BEST Thematic Network on biometrics and fundamental rights. His lab, BioMedia4n6, focuses on biometrics, multimedia forensics, and privacy-aware systems.

Tony F. Chanمشاهده پروفایل

استاد

• Mathematical Image Processing
• Computer Vision
• Computational Brain Mapping
• +۱۴ مورد دیگر

Tony F. Chan is currently President and Professor of Mathematics and Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He holds the title of Professor Emeritus in the Department of Mathematics at the University of California, Los Angeles (UCLA), where he previously served as Professor with joint appointments in Computer Science and Bioengineering. He was Dean of the Division of Physical Sciences at UCLA (2001–2006) and Assistant Director at the National Science Foundation (NSF) for Mathematics and Physical Sciences (2006–2009). President, HKUST Professor, Mathematics & Computer Science and Engineering, HKUST Professor Emeritus, Mathematics, UCLA Assistant Director, NSF (2006–2009) Dean, Division of Physical Sciences, UCLA (2001–2006) His research interests are centered around mathematical image processing, computer vision, computational brain mapping, and numerical algorithms. He has made seminal contributions to variational methods, total variation regularization, level set methods, and multiscale computational techniques. His work bridges pure mathematics with applications in biomedical imaging, VLSI design, and scientific computing. His recent publications focus on image segmentation, inpainting, brain surface mapping, and nonlocal filtering. These works demonstrate a strong trend toward geometric and variational models for image analysis, with increasing emphasis on medical and biological applications such as neuron tracking and cortical mapping. One of the most cited mathematicians (ISI Highly Cited) Chan has mentored over 25 PhD students and 15 postdoctoral fellows, contributing significantly to the training of next-generation researchers in applied mathematics and computational science. He has led major research initiatives including the Institute for Pure & Applied Mathematics (IPAM) and has been involved in numerous professional services at national and international levels. His work has been supported by major funding agencies including the NSF. He leads the Image Processing Group at UCLA and has been instrumental in advancing interdisciplinary research at the intersection of mathematics, engineering, and neuroscience.

• Metamaterials
• Electromagnetic and Microwave Applications
• Terahertz Technology
• +۷ مورد دیگر

Cumali Sabah is a faculty member in the Department of Electrical and Electronics Engineering at the Middle East Technical University Northern Cyprus Campus (METU NCC), within the College of Engineering. His research is centered on advanced electromagnetic systems, particularly metamaterials and their applications in microwave and terahertz frequency regimes. Research Interests: His primary fields include Metamaterials, Electromagnetic and Microwave Applications, Terahertz Technology, Wave Propagation & Scattering, Antennas, Multilayer Structures, and Metamaterial-Based Sensing. He has pioneered work on fishnet metamaterials, chiral structures, polarization rotators, and absorbers, with applications in sensing and communication systems. The analysis of his recent publications reveals a strong focus on the design, simulation, and experimental validation of metamaterial structures for terahertz and microwave applications. Key themes include dual-band and multiband operation, polarization independence, low-loss configurations, and novel resonator geometries such as split-ring and omega-shaped elements. His work also extends to solar system applications, particularly in photovoltaic panel optimization through sun tracking and cooling. Scientific Contributions: Pioneering work on free-standing and waveguide-based metamaterials Development of THz polarization rotators using chiral metamaterials Innovative designs of multiband and low-loss metamaterial absorbers Contributions to electromagnetic parameter retrieval methods for bianisotropic slabs Advising and Grants: While specific details about students or funded grants are not provided in the text, his extensive publication record and research leadership suggest active involvement in graduate supervision and research projects. He has collaborated with international researchers from Germany, Turkey, and other institutions, indicating strong academic networks. Labs and Teams: Although specific lab affiliations are not mentioned, his research likely involves experimental and computational work in electromagnetics and metamaterials, possibly within dedicated labs at METU NCC’s Electrical and Electronics Engineering Department.

Hans-Peter Seidelمشاهده پروفایل

استاد

• Computer Graphics
• Neural Rendering
• High Dynamic Range Imaging
• +۷ مورد دیگر

Hans-Peter Seidel is a leading researcher in computer graphics at the Max Planck Institute for Informatics, part of the Max Planck Society. His work focuses on advancing the frontiers of image synthesis, neural rendering, and computational photography, with a strong emphasis on high dynamic range imaging, inverse rendering, and perception-aware graphics techniques. His research interests span a broad spectrum of computer graphics and vision, including neural radiance fields, Monte Carlo denoising, image deblurring, and visual perception modeling. He has made significant contributions to real-time rendering, HDR image generation, and uncertainty-aware AI for scientific applications. His lab collaborates closely with experts in rendering, perception, and machine learning, pushing the boundaries of what is possible in digital image creation and manipulation. The most recent publications reveal a strong trend toward integrating deep learning with traditional graphics pipelines, particularly through differentiable rendering, neural fields, and adversarial training. His work frequently appears in top venues such as SIGGRAPH, ACM Transactions on Graphics, and Computer Graphics Forum, reflecting sustained impact and innovation in the field. Hans-Peter Seidel has not been publicly associated with any formal scientific awards in the provided text. However, his extensive publication record and leadership at a premier research institute underscore his influential role in the academic community. While there is no explicit mention of student advising or grant funding in the provided material, his collaborative publications with junior researchers suggest active mentorship. His work is likely supported by institutional funding from the Max Planck Society, enabling long-term, high-risk research in computer graphics and AI. He is part of a vibrant research group at the Max Planck Institute for Informatics, specializing in computer graphics. The team works on cutting-edge problems in rendering, perception, and machine learning, often bridging the gap between theoretical innovation and practical applications in virtual reality, computational photography, and scientific visualization.

Marco Giomettoمشاهده پروفایل

استادیار

• Environmental Engineering
• Wind Engineering
• Smart Cities
• +۴ مورد دیگر

Marco Giometto is an Assistant Professor of Civil Engineering and Engineering Mechanics at Columbia University, affiliated with the Columbia School of Engineering and Applied Science. His research focuses on fluid dynamics and turbulence, particularly in environmental flows, atmospheric boundary layers, and wind engineering. He employs supercomputing and physics-data-driven methods to advance understanding of natural systems and urban infrastructure resilience. Education: PhD in Civil Engineering (Braunschweig TU University and University of Florence, 2014); PhD in Mechanical Engineering (École Polytechnique Fédérale de Lausanne, 2016, EDME Award recipient). Experience: Postdoctoral roles at UBC and the Center for Turbulence Research (Stanford/NASA Ames); Amazon Visiting Academic (2020–present). Research Interests: Environmental Engineering, Wind Engineering, Smart Cities, Sustainable Infrastructure, and Computational Mechanics. His work bridges fundamental fluid dynamics with applied challenges in urban sustainability and climate resilience. Awards/Grants: NSF CAREER Award (2024), NSF Physical & Dynamic Meteorology Program grant (2024), Army Research Office DURIP Award (2023). Collaborations include Amazon Prime Air and field campaigns like CoURAGE. Lab & Outreach: Leads the Environmental Flow Physics Laboratory, advancing computational tools and field experiments. Teaches at Columbia and mentors students like Gurpreet Hora (PhD Teaching Fellow).

Raphaël Pestourieمشاهده پروفایل

استادیار

• Scientific Machine Learning
• Inverse Design
• Electromagnetism
• +۷ مورد دیگر

Raphaël Pestourie is an Assistant Professor in the School of Computational Science and Engineering at Georgia Tech (2023–present). He holds a PhD in Applied Mathematics (Harvard University, 2020) and an MBA from ESSEC. His research focuses on scientific machine learning and inverse design in electromagnetism, combining AI with physics-based models for engineering optimization. Notable contributions include fast approximate PDE solvers, surrogate models for metasurface design, and end-to-end optimization frameworks. **Education**: PhD in Applied Mathematics, Harvard University (2020) Secondary field in Computational Science & Engineering, Harvard MBA, ESSEC Masters in Nanosciences (Paris Saclay), Engineering (École Centrale Paris), and Statistics (Harvard) **Research Interests**: His work bridges applied mathematics and AI to solve large-scale engineering problems. Key areas include: Inverse design of metasurfaces for optical applications Scientific machine learning for PDE-based optimization Hybrid models combining theory-driven and data-driven approaches End-to-end optimization of electromagnetic systems **Awards & Support**: Funded by MIT-IBM Watson AI Lab, DARPA, Simons Foundation, and others Arthur Sachs Fellow (French Fulbright Commission) Member of Harvard's Graduate School Leadership Institute **Advisees & Collaborations**: Advises PhD/MSc students at Georgia Tech and collaborates with MIT, Yale, and UC Berkeley researchers Current projects include metasurface design for chemical detection and imaging **Teaching**: Created new courses: CSE 8803 (Scientific Machine Learning) and CSE 8801 (Linear Algebra/Probability/Stats)

Martijn Anthonissenمشاهده پروفایل

استادیار

• Computational Illumination Optics
• Numerical Mathematics
• Scientific Computing
• +۵ مورد دیگر

Martijn Anthonissen is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). He is a core member of the Computational Illumination Optics group, focusing on numerical methods for optical system design, freeform optics, and inverse problems in illumination engineering. His work combines advanced mathematical models with computational techniques to develop efficient optical systems for applications like LED lighting, automotive headlights, and laser technology. Education: Martijn earned his mathematics degree at TU/e and participated in the Japan Prizewinners Program, working at Hitachi in Tokyo. He completed his PhD on numerical combustion before transitioning to computational optics. He has held visiting positions at institutions worldwide, including Yale University and the Weierstrass Institute. Research: His research emphasizes solving Monge-Ampère equations and generated Jacobian equations to design freeform optical surfaces. Key areas include hyperbolic PDEs, Liouville's equation in geometrical optics, and Lie algebraic methods for aberration analysis. Collaborations with industry partners aim to translate theoretical advances into practical optical solutions. Teaching: He teaches courses in numerical linear algebra, mathematical modeling, and scientific computing, contributing to TU/e's teacher training programs. His educational materials, including YouTube videos on numerical methods, are widely used. Publications: Over 50 peer-reviewed articles since 2013, including high-impact work in Journal of the Optical Society of America , Journal of Computational Physics , and Optics Express . Current projects include neural network approaches for PDEs and optimization of gradient-index lenses. Labs/Teams: Leads the Computational Illumination Optics group, which has active PhD and industrial research programs. The group's interdisciplinary approach bridges mathematics, physics, and engineering.

Nuutti Hyvönenمشاهده پروفایل

استاد

• Inverse Problems
• Electrical Impedance Tomography
• Numerical Analysis
• +۳ مورد دیگر

Nuutti Hyvönen is a Professor and Head of Department at the Department of Mathematics and Systems Analysis, Aalto University, School of Science. His research focuses on inverse problems, electrical impedance tomography, and numerical analysis with applications in biomedical imaging and engineering. He holds a Doctor of Science (Technology) degree and has extensive experience in developing mathematical methods for imaging and reconstruction algorithms. His work emphasizes improving the accuracy and robustness of tomographic techniques, particularly in handling modeling errors and electrode configurations. Key research areas include: Inverse problems for partial differential equations Electrical impedance tomography (EIT) and its biomedical applications Bayesian experimental design and uncertainty quantification Numerical methods for nonlinear imaging problems Recent publications highlight advancements in: Optimal electrode positioning Series reversion methods in Calderón problems Feasibility of EIT for monitoring intracerebral hemorrhages Edge-enhancing reconstruction algorithms He has contributed to over 79 peer-reviewed articles, with notable work on monotonicity-based reconstruction methods and stochastic Galerkin finite element approaches. His research bridges mathematical theory and practical applications in medical imaging, engineering, and computational science.

Christina Kuttlerمشاهده پروفایل

استاد

• Biomathematics
• Deterministic Mathematical Models
• Bacterial Communication
• +۳ مورد دیگر

Prof. Christina Kuttler holds the position of Professor in the Department of Mathematics at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology. Her research focuses on biomathematics, particularly deterministic mathematical models of bacterial communication and diffusion processes, aiming to understand intracellular regulatory mechanisms. She has been at TUM since 2008, following postdoctoral work at the University of Tübingen and the Helmholtz Center Munich. Her academic background includes a doctorate in mathematics (2000) from the University of Tübingen, with further studies in physics and computer science. Awards include the Erwin Schrödinger Prize (2007) and the Tübingen Doctoral Prize (2000). Research interests span mathematical modeling in biology, with publications on quorum sensing, bacterial biofilms, and cancer dynamics. Notable works include models of immunotherapy effects on lung cancer and stochastic approaches in biological systems. She teaches courses such as Mathematical Modelling and Mathematics for Engineers, and collaborates on projects like modeling cerebral hemorrhage in premature infants. Her work bridges applied mathematics, computational biology, and interdisciplinary research.

Robert Douglasمشاهده پروفایل

مدرس

• Applied Mathematical Analysis
• Optimal Mass Transfer
• Weather Front Formation
• +۴ مورد دیگر

Robert Douglas is a Lecturer in the Department of Mathematics at Aberystwyth University since February 2000. His research focuses on applied mathematical analysis, particularly in optimal mass transfer, weather front formation, and relaxation spectrum recovery. He holds a PhD in applied mathematical analysis from Bath University, specializing in nonlinear analysis of vortices in ideal fluid flow. His career includes positions as a Research Fellow at the Universities of Cambridge and Reading, and a Visiting Scientist at the UK Meteorological Office. Key research interests include proving rigorous results using measure theory and convex analysis, with notable contributions to the Cullen-Norbury-Purser conjecture and polar factorization theory. Collaborations include work with H.R. Whittle Gruffudd on relaxation spectra and G.R. Burton on uniqueness conditions for polar factorizations. His work spans 1994–2019 with 9 peer-reviewed articles, addressing topics like semigeostrophic equations, viscoelastic fluid dynamics, and inverse problems. His research contributes to UN Sustainable Development Goals through advancements in mathematical modeling of environmental systems. Dr. Douglas actively participates in the Analysis cluster of the Wales Institute for Mathematical and Computational Sciences. His expertise combines theoretical rigor with applications in geophysical fluid dynamics and materials science.

• Inverse Problems
• Image Processing
• Optimization
• +۵ مورد دیگر

Markus Grasmair is a Professor at the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). His research focuses on inverse problems, mathematical methods in image processing, and optimization. He has contributed to nonsmooth variational methods and Lavrentiev regularization for ill-posed problems. Education: Habilitation in Mathematics (University of Vienna, 2011), PhD in Mathematics (University of Innsbruck, 2006), MSc in Mathematics (University of Innsbruck, 2003). Markus's research spans mathematical imaging, biomedical applications, and PDE-based modeling. He has developed regularization techniques for inverse problems, including Lavrentiev and L1 methods, and applied these to medical imaging and cervical cancer risk stratification. His work integrates sparse optimization, shape analysis, and data-driven approaches. Recent publications highlight interdisciplinary trends, combining inverse problems with medical imaging (X-ray calibration, cervical cancer prediction) and extending variational methods to PDEs, multiscale modeling, and graph-based systems. Key subfields include monotone operators, matrix factorization, and elastic shape analysis. He teaches courses in optimization and has held academic positions at the University of Vienna, Catholic University of Eichstätt-Ingolstadt, and University of Innsbruck. Contact: markus.grasmair@ntnu.no. Current Role: Professor (NTNU) Past Roles: Substitute Professor (Catholic University of Eichstätt-Ingolstadt, 2012-2013), Assistant Professor (University of Vienna, 2009-2012)

• Computer Animation
• Evolutionary Algorithms
• Computational Science
• +۳ مورد دیگر

Ari Sarafopoulos is a Senior Lecturer at Bournemouth University, specializing in computer animation and evolutionary computation. His work bridges computational methods with artistic applications, focusing on iterated function systems (IFS) and material simulation. Key research areas include evolutionary algorithms, fractal modeling, and computational design. Recent publications address material point methods for elastoplastic simulation and procedural shape generation. He has presented work at major conferences including ACM SIGGRAPH and IEEE Congress on Evolutionary Computation.

• Ground Penetrating Radar
• Civil Engineering
• Structural Health Monitoring
• +۷ مورد دیگر

Amir Alani is a Professor at the University of West London with extensive expertise in Ground Penetrating Radar (GPR) applications for infrastructure and environmental monitoring. His research spans civil engineering, geophysics, and remote sensing, with particular focus on non-destructive testing methodologies for structural health assessment. His research interests center on Ground Penetrating Radar applications across multiple domains including structural health monitoring of bridges and tunnels, tree root mapping and assessment, airport pavement inspection, and flood forecasting systems. Dr. Alani has pioneered innovative approaches using polarimetric GPR systems for tree trunk inspection and developed enhanced data processing frameworks for mapping tree root systems in urban environments. His work integrates GPR with other technologies like InSAR (Interferometric Synthetic Aperture Radar) for comprehensive infrastructure monitoring. Analysis of Dr. Alani's recent publications reveals a strong trend toward integrating multiple sensing technologies for infrastructure monitoring, with significant work on applying GPR to environmental challenges like tree health assessment and urban flood management. His research demonstrates a consistent focus on practical applications of geophysical methods to solve real-world engineering problems, particularly in urban infrastructure contexts. Dr. Alani has been actively involved in advising PhD students and securing research grants, though specific details of these activities are not fully documented in the available publications. His research group appears to collaborate extensively with international partners across Europe. His laboratory work focuses on GPR technology development and application, with particular emphasis on tree root mapping systems and infrastructure health monitoring. The research team has developed specialized processing frameworks for GPR data analysis, including geostatistical approaches and deep learning techniques for root detection.

Naonori Uedaمشاهده پروفایل

استاد پژوهشی

• Machine Learning
• Artificial Intelligence
• Physics-Informed Deep Learning
• +۵ مورد دیگر

Naonori Ueda is a Research Professor and Deputy Director at RIKEN Center for Advanced Intelligence Project. He also serves as a Visiting Fellow at NTT Communication Science Laboratories, Research Supervisor for Mathematical Information Platform at Japan Science and Technology Agency (JST), and Visiting Professor at Kobe University's Graduate School of System Informatics. His distinguished career spans academia, government research institutions, and industry collaboration, with significant contributions to advancing artificial intelligence and machine learning applications across multiple scientific domains. Dr. Ueda's research interests focus on the intersection of machine learning, artificial intelligence, and physical sciences. He specializes in physics-informed deep learning approaches that integrate governing physical equations with neural network architectures. His work spans geophysical data analysis, remote sensing applications, computational seismology, and environmental monitoring systems. He has pioneered methods for crustal deformation modeling, earthquake prediction, tsunami inundation forecasting, and satellite imagery analysis using advanced machine learning techniques. His research demonstrates how AI can solve complex scientific problems by bridging the gap between data-driven approaches and physical domain knowledge. His publication record reveals a strong trend toward applying machine learning to solve real-world geophysical and environmental challenges. His recent work shows increasing sophistication in physics-informed neural networks that incorporate domain-specific knowledge into deep learning architectures. The publications span high-impact journals like Nature Communications, demonstrating the interdisciplinary significance of his work. His research consistently focuses on practical applications of AI for disaster prevention, environmental monitoring, and scientific discovery. Fellow of IEICE (Institute of Electronics Information and Communication Engineers) Member of Japan Prize field review committee Selection Committee Member for Brilliant Female Research Award (The Jun Ashida Award) Member of Kyoto Prize Selection Committee Dr. Ueda has secured substantial research funding through multiple government-sponsored projects including RIKEN Pioneering Project 'Prediction Science,' JST AIP Acceleration Research projects on weather prediction and drug discovery, and AMED-funded medical research initiatives. His leadership extends to serving as Sub-project Director for Japan's Moonshot R&D Project. He actively mentors researchers through his roles at RIKEN, NTT, and various academic institutions, fostering the next generation of AI scientists. As Deputy Director of RIKEN Center for Advanced Intelligence Project, Dr. Ueda leads one of Japan's premier AI research initiatives. He also serves on the Advisory Board of Kobe University's Mathematical and Data Science Center and Kyoto University's Graduate School of Informatics. His leadership extends to coordinating the AI Seminar at Osaka Industrial Association and supervising the Keihanna 'Edison Society' at the International Institute for Advanced Studies, demonstrating his commitment to bridging academic research with industrial applications.