جست‌وجوی استاد و پژوهشگر

• Inverse Problems
• Dynamic Tomography
• Parameter Identification
• +۵ مورد دیگر

Anne Wald is an Assistant Professor at the University of Göttingen since 2020, specializing in inverse problems and imaging. She previously worked as a postdoc in Saarbrücken and Helsinki (2017-2020) after earning her PhD in Mathematics from Saarland University (2017) and M.Sc. in Mathematics (2012) along with a Diploma in Physics (2013). Education: PhD in Mathematics (2017), Saarland University M.Sc. in Mathematics (2012), Saarland University Diploma in Physics (2013), Saarland University Her research focuses on inverse problems involving dynamic imaging , parameter identification , and motion compensation , particularly in nano-CT and hyperelastic materials. She applies advanced mathematical frameworks like Lebesgue-Bochner spaces and Tikhonov regularization. Current projects include: SFB 1456 Project B06: Compensation of motion and modeling inexactness in nano-CT and local tomography GRK 2756 Project A4: Multiscale rheological inverse problems and active processes in cells Anne's publications highlight work on time-dependent inverse problems , sequential subspace optimization , and anomaly detection . She develops algorithms for dynamic imaging and parameter estimation under uncertainty.

Majken Caroline Looms Zibarمشاهده پروفایل

دانشیار

• Hydrogeophysics
• Geophysical methods
• Ground penetrating radar
• +۵ مورد دیگر

Majken Caroline Looms Zibar is an Associate Professor in the Department of Geosciences and Natural Resource Management at the University of Copenhagen's Faculty of Science. Her research focuses on applying advanced geophysical methods to solve hydrological problems, particularly in the unsaturated zone. She has established herself as a leading expert in ground penetrating radar applications for subsurface characterization. Her research interests span multiple areas of hydrogeophysics including cross-borehole electrical resistance tomography, ground penetrating radar applications, and cosmic-ray neutron detection. Dr. Looms Zibar specializes in visualizing water infiltration in unsaturated zones, estimating unsaturated hydraulic parameters at meter-scale, characterizing porosity variations in geological formations, and monitoring soil moisture dynamics. Her work bridges geophysics and hydrology to address critical water resource management challenges. Analysis of her recent publications reveals a strong focus on advancing geophysical methods for environmental applications, particularly in soil moisture monitoring using novel approaches like cosmic-ray neutron sensing and gamma-ray spectrometry. Her research increasingly integrates multiple geophysical techniques to characterize subsurface heterogeneity across different spatial scales, with growing emphasis on agricultural applications and water resource management. Appointed member of Hydrology Section's Hydrogeophysics Technical Committee under the American Geophysical Union since 2011 Associate Editor of Vadose Zone Journal from 2014-2019 Principal organizer of the 5th International COSMOS workshop in Copenhagen in 2016 Dr. Looms Zibar has secured significant research funding, including projects funded by Geocenter Denmark, Region Hovedstaden, and international programs like Marie Curie Innovative Training Networks. She has supervised 21 Bachelor's projects, 15 Master's theses, and 8 PhD dissertations, while serving on 8 external PhD assessment committees. Her research group actively collaborates with institutions across Europe and maintains strong connections with water management authorities in Denmark. Her laboratory work focuses on developing and refining cross-borehole geophysical methods, particularly ground penetrating radar applications for challenging geological environments like clayey tills. The research group maintains field sites for long-term monitoring of hydrological processes and has developed specialized equipment for environmental geophysics applications, including novel crosshole GPR systems and gamma-ray spectrometry setups.

• Speech Processing
• Biomedical Image Processing
• Image Processing
• +۷ مورد دیگر

Chandra Sekhar Seelamantula is a Professor in the Department of Electrical Communication Engineering at the Indian Institute of Science (IISc), Bangalore, leading the SPECTRUM LAB. His research spans signal and image processing, computational imaging, and AI applications in healthcare, with expertise in Speech Processing, Biomedical Image Processing, Compressed Sensing, and Neuromorphic Imaging. He teaches core courses including Digital Signal Processing and Time-Frequency Analysis. His educational background includes: Postdoctoral fellow, Ecole polytechnique fédérale de Lausanne (2006-2009) Ph.D., Indian Institute of Science, Bangalore (2005) B.E., Osmania University College of Engineering, Hyderabad (1999) Professor Seelamantula's work focuses on advancing Sampling Theory and solving Inverse Problems in Computational Imaging through AI-driven approaches. His research bridges theoretical foundations with practical applications in Digital Healthcare, particularly in Biomedical Image Analysis and Neural Signal Processing, contributing to innovations in medical diagnostics and assistive technologies. His scientific awards and honors include: Outstanding Editorial Board Member Award 2022, IEEE Transactions on Image Processing Digital Health prize at NBEC 2018 Prof. Priti Shankar Teaching Award 2013 Intellectual Ventures Invention Awards (2010, 2011) IBM India Research Labs Research Student Fellowship Award (2001-2004) Multiple IEEE student paper contest prizes Undergraduate honors including Srinivasa Ramanujam Gold Medal (1997-1999) He has advised graduate students and secured research support through prestigious fellowships. His professional service includes leadership roles in IEEE Signal Processing Society chapters and technical committees. He directs the SPECTRUM LAB at IISc, which pioneers research in computational imaging, neuromorphic systems, and AI-driven healthcare solutions.

• Image Processing
• Signal Processing
• Data Compression
• +۴ مورد دیگر

Dr. Guang Deng serves as an Adjunct Associate Professor in the College of Engineering at La Trobe University, where he has maintained academic appointments since 1994. His technical expertise bridges communications engineering, signal processing, and advanced image analysis with practical applications in medical devices and computer vision systems. Academic Background: BSc from Sun Yat-Sen University MEng from Chinese Academy of Sciences PhD from La Trobe University Dr. Deng's research program centers on generalized linear image processing and statistical signal processing , with significant contributions to lossless image compression algorithms and noise reduction techniques specifically engineered for cochlear implant devices. His methodology combines theoretical mathematical frameworks with practical hardware implementation constraints, particularly evident in his recent work on fixed-point acceleration methods for resource-limited systems. His publication trajectory reveals consistent innovation in image filtering techniques, evolving from foundational work on discrete Laplacian operators to contemporary deep learning applications in marine imaging. Key thematic developments include the progression from traditional signal processing to hybrid approaches incorporating machine learning, with growing emphasis on real-world constraints like embedded system limitations and illumination variability in agricultural imaging. Funded Research Initiatives: Virtual Speech Pathologist (National ICT Australia, 2013-2016) ARC Centre of Excellence in Electromaterial Sciences (Australian Research Council, 2009-2013) Dr. Deng maintains active research collaborations across engineering and life sciences domains, particularly evident in his cross-disciplinary work on plant phenotyping systems and medical device signal processing. His current research demonstrates increasing focus on edge computing applications and biometric security systems alongside his longstanding image processing expertise.

Mårten Sjöströmمشاهده پروفایل

استاد

• Multi-Dimensional Signal Processing
• System Modelling and Identification
• Image and Video Processing
• +۹ مورد دیگر

Mårten Sjöström serves as a Professor at Mid Sweden University and acts as Node Coordinator for the InfraVis project. His academic work centers on advanced signal processing and visualization technologies within the university's research infrastructure. His primary research domains include Multi-Dimensional Signal Processing and System Modelling and Identification, with significant applications in Image and Video Processing and Multi-media Communications. Current investigations focus on Multi-Scopic 3D and Light Field Technology—encompassing capture, processing, coding, and visualization—where he addresses inverse problems through machine and deep learning methodologies. Additional expertise spans Computer Vision, Photogrammetry, Immersive Video Technologies, Quality of Experience (QoE), and Human Visual Perception, demonstrating interdisciplinary integration of signal processing with perceptual modeling. As InfraVis Node Coordinator, he engineered a specialized 3D visualization tool for analyzing halogen crystal structures under extreme pressure conditions. This system enables interactive exploration of incommensurately modulated structures during molecular dissociation in bromine allotropes, providing critical insights into complex atomic transitions and phase changes that conventional methods cannot resolve.

Axel Munkمشاهده پروفایل

استاد

• Mathematical Statistics
• Biosciences
• Biophysics
• +۱۰ مورد دیگر

Prof. Axel Munk is a University Professor at the Institute for Mathematical Stochastics , Georg-August-University of Goettingen, and serves as the director of the Felix Bernstein Institute for Mathematical Statistics in the Life Sciences (FBMS) within the Faculty of Mathematics and Computer Science. His work bridges mathematical statistics with biosciences and biophysics, focusing on advanced statistical methodologies for complex data analysis. Research Interests: Shape analysis, stochastic models in biology, ion channel dynamics, optimal transport theory, and super-resolution microscopy. Awards: Institute of Mathematical Statistics (IMS) Fellow (2016). Collaborations: Involved in DFG Collaborative Research Centers (e.g., SFB 755, SFB 860) and Priority Programs (e.g., SPP 1590). Software Contributions: Developed packages like stepR , J-SMURF , and icmcstat for ion channel analysis and multiscale methods.

Mrinal Senمشاهده پروفایل

استاد

• Geophysics
• Seismic Imaging
• Machine Learning
• +۵ مورد دیگر

Mrinal Sen is a Professor at The University of Texas at Austin, holding the Shell Companies Foundation Centennial Chair in Geophysics. He is affiliated with the Jackson School of Geosciences, specifically the Department of Earth and Planetary Sciences and the Institute for Geophysics. Sen is a pioneer in machine learning applications for seismic imaging and has developed methods combining physics-based models with computational techniques. He was named the SEG’s 2024-2025 Distinguished Instructor Short Course (DISC) lecturer for his course on physics and data-driven seismic analysis. Education: PhD from University of Hawaii at Manoa, M.Sc. from IIT Dhanbad. His research focuses on seismic wave propagation, inversion, and uncertainty quantification, with emphasis on unconventional reservoirs and physics-assisted machine learning. He led the development of the Jackson School’s machine learning certificate program in geosciences. Research Interests: Exploration geophysics, seismology, full waveform inversion (FWI), Bayesian methods, and high-performance computing. His work bridges cutting-edge computational techniques with traditional geophysical principles. Key Achievements: Virgil Kauffman Gold Medal (SEG, 2018), SEG Honorary Membership, Distinguished Educator Award (UT Austin). Recent Work: Hybrid quantum neural networks for seismic inversion, starting-model-independent FWI algorithms, and trans-dimensional Bayesian inversion. Global Outreach: DISC course presentations across 8+ locations including Houston, Norway, France, India, and virtual platforms. Grants & Labs: Active in research funded by industry and academic partnerships, leading the Sen Research Group focused on seismic imaging and machine learning integration.

Thomas Eibertمشاهده پروفایل

استاد

• High-Frequency Engineering
• Electromagnetic Modeling
• Antenna Design
• +۳ مورد دیگر

Prof. Thomas Eibert holds the position of Full Professor of High-Frequency Engineering at the Technical University of Munich (TUM), part of the TUM School of Computation, Information and Technology. His academic journey includes a doctorate from the University of Wuppertal (1997), research at the University of Michigan (USA), and roles at Deutsche Telekom's Technology Center and the Fraunhofer Institute. Specializing in high-frequency electromagnetic processes, his research focuses on novel antenna technologies, electromagnetic modeling algorithms, and field transformation techniques. Key contributions include advancements in near-field imaging, inverse source solutions, and microwave sensor systems. Notable awards include the Best Measurement Paper Award at EuCAP (2020, 2019), the ESA Antenna Workshop's Best Innovative Paper (2013), and the VDE ITG Literature Prize (2008). His work bridges theoretical developments with practical applications in automotive antennas, radar systems, and environmental impact analysis of antenna performance. Current research emphasizes UAV-based measurement systems and low-frequency stabilization methods for electromagnetic simulations. Education: Doctorate in Electrical Engineering (University of Wuppertal, 1997) Professional Roles: Head of Fraunhofer Institute's Antennas and Scattering Department (2002–2005), Professor at University of Stuttgart (2005–2008) Research Groups: Active in the Department of Electrical Engineering, collaborating on projects involving radar imaging, antenna design, and electromagnetic field transformations. Publications span journals such as IEEE Transactions on Antennas and Propagation, focusing on topics like phase retrieval, decoupling networks, and frequency-selective surfaces. His lab develops practical measurement frameworks for large-scale antenna systems and environmental resilience testing.

• Applied Signal Processing
• Inverse Problems
• Greedy Algorithms
• +۵ مورد دیگر

Florian Bossmann is an Associate Professor at the College of Mathematics, Harbin Institute of Technology (HIT), specializing in Applied Signal Processing with a focus on algorithms and applications. His research encompasses inverse problems, greedy algorithms, sparsity, and compressed sensing, with applications in seismic exploration, ptychography, and video processing. He holds a PhD in Mathematics from the University of Göttingen (2013) and a Diplom in Mathematics from the University of Duisburg-Essen (2009). Current projects include NSFC-funded seismic data interpolation and a start-up grant for applied signal processing research. He teaches courses like 'Signal and Image Processing' and 'Calculus for Civil Engineering'. Supervising Master/PhD students in signal processing, he emphasizes collaboration and innovation. His lab, the ASP Group, explores cutting-edge methods in multidimensional data reconstruction and algorithm design. Prominent publications focus on impedance inversion, neural network applications in geoscience, and object reconstruction techniques. He has led collaborative research initiatives with institutions like the Fraunhofer Institute and the Helmholtz Center for Environmental Health.

• Particle Physics
• Astrophysics
• Dark Matter Detection
• +۷ مورد دیگر

Anastassopoulos Vassilis is a Professor at the University of Patras, affiliated with the Department of Electronics and Computers within the School of Natural Sciences. His research spans particle physics, astrophysics, and applied technologies such as remote sensing and medical imaging. He leads experimental efforts in axion detection via the CAST helioscope and explores dark matter signatures in geophysical and astrophysical contexts. His work integrates cutting-edge techniques like neural networks for image processing and decision fusion algorithms for data analysis. Research interests focus on dark matter axions, solar physics, and interdisciplinary applications including land cover classification using SAR imagery, thermal intoxication detection, and super-resolution imaging. He contributes to collaborations such as the Proton EDM experiment and the pEDM project, aiming to uncover new physics beyond the Standard Model. His expertise bridges theoretical models with experimental validation, addressing questions in fundamental physics and applied engineering. Key areas include: particle physics experiments (axion searches, proton EDM), environmental monitoring via remote sensing (coastal change detection, ship identification), biomedical imaging (OCT analysis, thermal facial features), and algorithm development (neural networks, inverse problems). His work frequently employs advanced computational methods and interdisciplinary approaches.

• Deep Generative Models
• Uncertainty Quantification
• Neural Data Compression
• +۳ مورد دیگر

Stephan Mandt is an Associate Professor of Computer Science and Statistics at the University of California, Irvine. He leads research at the intersection of deep generative modeling, uncertainty quantification, neural data compression, and AI-driven scientific discovery. Previously, he directed the machine learning group at Disney Research and held postdoctoral positions at Princeton and Columbia University. His work is supported by NSF, DARPA, IARPA, DOE, Disney, Intel, and Qualcomm. He has received the NSF CAREER Award, UCI Mid-Career Excellence Award, and German Research Foundation's Mercator Fellowship. Education: PhD in Theoretical Physics from the University of Cologne (German National Merit Scholarship recipient). Served as Program Chair for AISTATS 2024, General Chair for AISTATS 2025, and Action Editor for JMLR and Transactions on Machine Learning Research. Active in organizing tutorials at NeurIPS, AAAI, and UAI. Research Interests: Focuses on generative AI foundations, including diffusion models, variational autoencoders, and neural compression techniques. Explores applications in climate science, physics, and chemistry. Current projects include AI for scientific discovery, resource-efficient learning algorithms, and uncertainty-aware neural networks. Grants & Awards: Recognized as a Chan Zuckerberg Investigator and AI Resident. Holds the Kavli Fellowship from the U.S. National Academy of Sciences. His research bridges machine learning theory with interdisciplinary scientific challenges, emphasizing scalable and interpretable algorithms. Labs & Teams: Leads the Mandt Lab at UCI, supervising PhD students and postdocs in generative AI and scientific machine learning. Collaborates with institutions like ETH Zurich (as Guest Professor) and the Chan Zuckerberg Initiative.

Ramchandra Rimalمشاهده پروفایل

استادیار

• Machine Learning
• Statistical Network Models
• Classification and Clustering
• +۳ مورد دیگر

Dr. Ramchandra Rimal is an Assistant Professor in the Department of Mathematical Sciences at Middle Tennessee State University (MTSU), specializing in Data Science. He holds a PhD from the University of Central Florida (2020), an MS from UCF (2017), and a BS from Tribhuvan University (2009). His research focuses on Machine Learning, Statistical Network Models, and Classification/Clustering, with applications across diverse fields such as healthcare, finance, and biological sciences. He develops novel methodologies to solve real-world problems, including predictive modeling for stock markets, neurocognitive analysis, and network topology estimation. Dr. Rimal has published in top journals like the Journal of the Royal Statistical Society and Journal of Machine Learning Research , with notable contributions to stochastic block models, density deconvolution, and deep learning frameworks. His work bridges theoretical advancements with practical applications in data-driven decision making. Awards: 2019 UCF Graduate Research Excellence Award, 2018 UCF Teaching Assistant Award Teaching: Courses include Probability & Statistics, Applied Predictive Modeling, and Data Science graduate programs at MTSU. He actively presents at conferences such as the Symposium on Data Science and the Southeastern International Conference on Combinatorics. His research group collaborates on interdisciplinary projects, emphasizing both computational innovation and domain-specific relevance.

• Numerical Analysis
• Inverse Problems
• Imaging
• +۲ مورد دیگر

Per Christian Hansen is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark. His research focuses on numerical analysis, algorithms for inverse problems, imaging, tomography, and matrix computations. He has held academic positions since 1996 and has contributed to computational uncertainty quantification through projects like CUQI funded by Villum Fonden. His research interests span inverse problems in imaging and tomography, uncertainty quantification, and numerical linear algebra. Recent publications demonstrate advancements in plasma physics, computational imaging, and open-source software development. Hansen has received funding for major projects including the Computational Uncertainty Quantification for Inverse problems (CUQI) initiative from Villum Fonden (2019-2025).

Arnold D. Kimمشاهده پروفایل

استاد

• Wave Propagation
• Scattering Theory
• Imaging
• +۷ مورد دیگر

Arnold D. Kim is a Professor of Applied Mathematics at the University of California, Merced, where he is a founding member of the Department of Applied Mathematics. His research focuses on interdisciplinary applications of wave propagation and scattering, with emphasis on imaging, spectroscopy, remote sensing, photonics, and plasmonics. He develops mathematical models using differential/integral equations, asymptotic analysis, and scientific computing. Current research projects include synthetic aperture radar imaging, boundary integral equation methods, plasmonic meta-structures, acoustic binding forces, and data science applications in social justice and gamification. His work spans computational physics and biomedical optics, addressing challenges such as subsurface target detection, inverse scattering problems, and polarization-based imaging techniques. Collaborations include projects with Chrysoula Tsogka on SAR imaging, Camille Carvalho on boundary integrals, Sayantani Ghosh on plasmonics, and Maia Powell on social media analysis. His office is located in the Arts & Computational Sciences Building, Room 347. Kim's research trends reflect a blend of theoretical advancements (e.g., asymptotic methods for layer potentials) and applied innovations (e.g., imaging algorithms for lossy media). Notable contributions include modeling broadband cloaking with plasmonic structures and analyzing mouse-tracking data for gamification insights. His recent publications emphasize high-resolution imaging techniques and spectral classification methods for radar applications.

Mark Gockenbachمشاهده پروفایل

استاد

• Numerical Analysis
• Inverse Problems
• Partial Differential Equations
• +۳ مورد دیگر

Mark Gockenbach serves as Professor and Department Chair of the Department of Mathematical Sciences within the College of Arts & Sciences at the University of Delaware. He joined UD in 2020 after spending 21 years at Michigan Technological University, where he served as department chair for the final 14 years of his tenure. His academic career demonstrates a strong commitment to both administrative leadership and scholarly research in mathematical sciences. Professor Gockenbach's research primarily focuses on numerical analysis with special emphasis on inverse problems. His scholarly work spans singular value expansions, Tikhonov regularization techniques, elastography inverse problems, and numerical methods for partial differential equations. His publication record shows consistent productivity from 2010 through 2020, with particular concentration on mathematical approaches to inverse problems with applications in medical imaging and elasticity. His work bridges theoretical mathematics with practical applications in scientific computing and medical diagnostics. The analysis of his recent publications reveals a strong thematic focus on developing and analyzing numerical methods for solving inverse problems. His work frequently addresses stability issues, error estimation, and parameter selection in regularization techniques. The research shows progression from theoretical foundations of singular value decompositions to practical applications in medical imaging, particularly in tumor identification through elastography techniques. His scholarly output demonstrates both depth in mathematical theory and relevance to real-world applications. While specific awards are not mentioned in the available information, Professor Gockenbach's sustained research productivity and leadership roles indicate significant professional recognition. His extensive publication record in reputable journals reflects standing within the mathematical community. As Department Chair, Professor Gockenbach oversees all departmental operations while maintaining his research program. Though his administrative duties have reduced his teaching load, he previously taught a wide range of courses including calculus, linear algebra, differential equations, complex variables, and numerical methods for partial differential equations. His textbook publications suggest a strong commitment to mathematical education alongside his research activities.