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Francisco Eugenio GonzálezView profile

Professor

• Remote Sensing
• Image Processing
• Environmental Monitoring
• +3 more

Francisco Eugenio González is a Professor at the University of Las Palmas of Gran Canaria (ULPGC), where he also served as Dean of the Electrical Engineering School from 2004 to 2010. He holds B.S., M.S., and Ph.D. degrees in Electrical Engineering from ULPGC (1986, 1993, 2000). His research focuses on remote sensing image processing, particularly multispectral/hyperspectral methodologies for monitoring shallow-waters and inland ecosystems. His work includes pre-processing techniques (radiometric/geometric corrections), satellite data analysis from AVHRR, MODIS, Landsat, and others, and climate change impact assessments. He is an IEEE Senior Member (2006) and leads research at the Institute of Oceanography and Global Change (IOCAG/ULPGC). His publications span environmental monitoring, bathymetry, and algorithm development for satellite imagery. Education: B.S. Electrical Engineering, ULPGC (1986) M.S. Electrical Engineering, ULPGC (1993) Ph.D. Electrical Engineering, ULPGC (2000) Research Interests: Remote sensing pre-processing (radiometric/geometric corrections) Image segmentation and feature extraction Satellite sensor analysis (AVHRR, MODIS, Landsat, etc.) Climate change impacts on marine/coastal environments Key Awards: IEEE Senior Member (2006)

Javier Marcello RuizView profile

Associate Professor

• Remote Sensing
• Image Processing
• Satellite Communications
• +3 more

Javier Marcello Ruiz is an Associate Professor at the Universidad de Las Palmas de Gran Canaria (ULPGC), affiliated with the Department of Signal and Communications and the Institute of Oceanography and Global Change (IOCAG). He holds an M.S. in Electrical Engineering from the Technical University of Catalonia (UPC) and a Ph.D. from ULPGC. His research focuses on remote sensing image processing, satellite communications, and climate change analysis, leveraging data from multiple sensors and satellites. He has authored over 35 articles in high-impact journals and served as an Associate Editor for IEEE-JSTARS and MDPI-Remote Sensing. Research Interests: Remote sensing pre-processing (radiometric, geometric, and atmospheric corrections), image segmentation, feature extraction, classification, satellite sensor analysis (AVHRR, MODIS, Landsat, etc.), and coastal/land product generation. His work addresses climate change impacts on marine and coastal environments. Publications: Recent works include studies on seagrass mapping using satellite imagery and super-resolution techniques for Sentinel-2 data. Earlier contributions focused on oceanographic thermal tracking and multi-sensor fusion. Over 100 conference presentations highlight his active engagement in international research. Affiliations: Associate Editor of IEEE-JSTARS and MDPI-Remote Sensing journals; reviewer for 35+ publications. Led numerous research projects and contracts. Active in the Canary Space Center (INTA) from 1992–2000 as Head Engineer.

Lin YanView profile

Assistant Professor

• Topological Data Analysis
• Data Visualization
• Computational Topology
• +3 more

Dr. Lin Yan is an Assistant Professor in the Department of Computer Science at Iowa State University. She holds a Ph.D. in Computing from the University of Utah (2022), supervised by Prof. Bei Wang Phillips, and a BS/MS in Automation/Control Science from Shanghai Jiao Tong University. Previously, she was a postdoctoral fellow at Argonne National Laboratory (2022–2023). Her research focuses on topological data analysis, visualization, and machine learning, with applications in scientific data exploration and uncertainty quantification. Recent work emphasizes combining topology, statistics, and visualization to address challenges in large-scale data analysis. Education: Ph.D., Computing, University of Utah, 2022 M.S., Control Science and Engineering, Shanghai Jiao Tong University, 2013 B.S., Automation, Shanghai Jiao Tong University, 2010 Research Interests: Her work bridges computational topology and data science, with emphasis on developing algorithms for topology-based visualization, robust data compression, and uncertainty characterization. She explores applications in climate science, scientific computing, and machine learning. Techniques like merge trees, Morse-Smale complexes, and physics-informed tracking frameworks are central to her methodologies. Awards: IEEE VGTC Visualization Doctoral Dissertation Award (2024) Advising & Grants: While formal advisee lists are not provided, her recent publications indicate collaborative research with postdocs and peers. Grants and funding details are not explicitly stated in the text. Labs & Teams: Her research group likely engages in interdisciplinary projects with computational scientists and visualization experts, though specific lab names are not mentioned.

Timo DickscheidView profile

Professor

• Computer Vision
• Biomedical Imaging
• Neuroinformatics
• +3 more

Prof. Dr. Timo Dickscheid is a Professor at Heinrich Heine University Düsseldorf and Working Group Leader for "Big Data Analytics" at the Institute of Neuroscience and Medicine (INM-1) at Forschungszentrum Jülich. His research focuses on developing high-resolution human brain atlases at cellular resolution using methods from Computer Vision, Big Data Analytics, and Neuroinformatics. He leads the Helmholtz AI unit for the research field "Information" and contributes to the European research infrastructure EBRAINS within the Human Brain Project. His work investigates methods for reconstructing 3D brain models at individual cell resolution, developing machine learning techniques for microscopic image analysis, and creating data management systems for terabyte-scale imaging data. Research interests include: Computer Vision for microscopic image analysis Distributed data management for high-throughput imaging Web services for 3D brain data access Machine learning with limited training data His publications show consistent focus on neuroimaging, computational anatomy, and machine learning applications in neuroscience, with recent emphasis on 3D reconstruction, diffusion models, and atlas integration.

Fei DouView profile

Assistant Professor

• Machine Learning
• Federated Learning
• Wireless Sensor Networks
• +3 more

Fei Dou is an Assistant Professor in the School of Computing at the University of Georgia, which operates under the Franklin College of Arts and Sciences. He joined UGA in August 2023 and serves as Graduate Program Faculty. His office is located in the Boyd Research and Education Center. He holds a Ph.D. and M.S. in Computer Science and Engineering from the University of Connecticut, both completed in 2023. Dr. Dou's research spans several key areas of computer science with particular focus on: Machine learning systems : Federated learning, reinforcement learning, and self-supervised techniques Wireless sensing : Healthcare monitoring using WiFi signals and contactless sensing Networked systems : Underwater sensor networks and secure communication protocols Applied AI : Education technology, precision agriculture, and indoor positioning His recent publications (2021-2025) demonstrate strong emphasis on federated learning adaptations for resource-constrained environments, healthcare applications using novel sensing methodologies, and security solutions for underwater networks. A consistent theme involves developing efficient machine learning techniques that operate in challenging real-world conditions while preserving privacy and security.

Maarten C.J. HornikxView profile

Professor

• Computational Acoustics
• Building Acoustics
• Sound Propagation Modeling
• +3 more

Maarten C.J. Hornikx is a Full Professor of Building Acoustics at Eindhoven University of Technology (TU/e), serving as Vice-Dean of the Department of the Built Environment. He leads the Building Acoustics Chair within the Unit Building Physics and Services (BPS), specializing in computational modeling of sound propagation in built environments. His research focuses on applications such as sound reproduction in mixed reality platforms and numerical models for vegetation and meteorological effects on sound. He actively promotes open research software, coordinating projects like the H2020-ITN Acoutect and serving as a Marie Curie grant recipient. Education: MSc in Architecture, Building and Planning (Chalmers University of Technology, 2004) PhD in Applied Acoustics (Chalmers University of Technology, 2009) Research Interests: Computational acoustics and numerical modeling Sound propagation in outdoor and indoor environments Acoustic effects of vegetation and meteorology Open-source software development for acoustics Mixed reality sound reproduction Recent Trends in Publications: Recent work emphasizes open-source tools for acoustic simulation, neural network applications in sound prediction, and optimization of acoustic materials. Key themes include improving accuracy in room acoustic modeling and understanding environmental influences on sound propagation. Awards: Forum Acusticum Prize (2011) Grants & Projects: Marie Curie IF Grant (2009-2011) Marie Curie Career Integration Grant (2012) H2020-ITN Acoutect Project (coordinator) Burgermeetnetwerk Groene Hart Project (2022-2024) Labs & Teams: Leads the Building Acoustics research group at TU/e and collaborates with the 4TU.Built Environment Center. Active in organizing international workshops and courses on acoustics, including the Autumn School Series in Acoustics and EAA Summer School.

Francesco VarratoView profile

Lecturer

• Research Data Management
• Soft Matter Physics
• Colloidal Systems
• +3 more

Francesco Varrato is a Lecturer at the EDCH-ENS unit and a Research Data Management (RDM) Specialist at École Polytechnique Fédérale de Lausanne (EPFL). He holds a PhD in Numerical Physics and a Master’s in Physics from Pisa University. His current role focuses on advancing RDM practices, including F.A.I.R. principles implementation, data management plan development, and collaboration with EPFL services like ReO, DPO, and external stakeholders. His research interests span RDM, soft matter physics, and colloidal systems. Research Highlights: Varrato’s work bridges academic and applied research, with recent contributions to RDM frameworks and historical studies on colloidal gels and fractal systems. His publications address data governance, open-source adoption, and material science. Teaching: He teaches Hands-on with Research Data Management in Chemistry , emphasizing practical RDM skills. His role also involves organizing workshops and guiding researchers in data documentation. Collaborations: Engages with EPFL’s Data Champions network and external organizations to promote data-driven practices. His interdisciplinary approach combines physics expertise with modern academic support methodologies.

• Tissue Engineering
• Biofabrication
• Computer Vision
• +3 more

Dr. Hao Liu is a Researcher in the Department of Tissue Technology and Biofabrication at ETH Zurich's Institute for Biomechanics. His work intersects tissue engineering, biofabrication, and advanced computational techniques such as computer vision and 3D reconstruction. He focuses on interdisciplinary applications, including robotics and neural radiance fields (NeRF) for biomedical and robotic systems. Institution: ETH Zurich Role: Researcher (Staff of Professorship for Tissue Engineering and Biofabrication) Research interests span tissue engineering, biofabrication, and computational methods like 3D reconstruction, visual localization, and neural networks. His recent work emphasizes robust 3D scene understanding, camera pose estimation, and deep learning for multi-view stereo. Publications highlight advancements in relative pose estimation, neural radiance fields, and line-based geometric analysis, contributing to both robotics and biomedical engineering fields. He has no listed scientific awards, but his research demonstrates significant innovation in computational imaging and biofabrication. No advising or grant details are provided in the available texts. His affiliation with the Institute for Biomechanics suggests involvement in labs focused on biomechanical systems and biofabrication technologies.

Celal SoyarslanView profile

Associate Professor

• Material Science
• Solid Mechanics
• Additive Manufacturing
• +5 more

Celal Soyarslan is an Associate Professor in Nonlinear Solid Mechanics, specializing in advanced materials and computational mechanics. His research focuses on material science, additive manufacturing, metamaterials, and machine learning applications in engineering. He has contributed to studies on microstructural modeling, fracture mechanics, and homogenization techniques, with applications in aerospace, biomedical, and energy sectors. Key research interests include the development of coupled damage models for composites, optimization of 3D microstructures using generative methods, and the integration of physics-informed machine learning into multiscale simulations. His work often addresses challenges in material characterization, such as effective magnetic and thermomechanical properties of composites. Publications highlight innovations in additive manufacturing of NiTi metamaterials, physics-informed modeling of elliptic equations, and the study of auxetic biological materials. Collaborations span international institutions, emphasizing interdisciplinary approaches to material design and analysis.

Johannes LedererView profile

Professor

• High-Dimensional Statistics
• Deep Learning
• Machine Learning
• +1 more

Johannes Lederer is Professor of Mathematics of Data-Based Methods at the University of Hamburg's Department of Mathematics. His research bridges mathematics, computer science, and applications, focusing on high-dimensional statistics, deep learning foundations, and robust machine learning. Research interests cover theoretical aspects of deep learning including regularization techniques, statistical guarantees for neural networks, and adaptive methods for high-dimensional problems. Recent work establishes sample complexity requirements for deep networks and fairness benchmarks for computer vision algorithms. Publications demonstrate interdisciplinary approaches, with developments in time-series forecasting, privacy-preserving methods, and geometric deep learning. Active collaborations include international workshops on statistics' role in modern AI. Professional activities include editorial responsibilities and conference organization. Team management includes doctoral candidates and research groups exploring statistical learning and AI safety.

• Artificial Intelligence
• Computer Science
• Fuzzy Logic
• +5 more

Đorđe Obradović is affiliated with Singidunum University's Faculty of Informatics and Computing. He holds a PhD from the University of Novi Sad's Faculty of Technical Sciences in Electrical Engineering and Computing (2003–2011), following a Master's (1998–2003) and Bachelor's (1992–1997) in the same field. His research focuses on artificial intelligence, fuzzy logic, image processing, and geospatial systems. Recent work includes air quality assessment frameworks using fuzzy sets, medical image analysis, and GIS applications. He has authored/co-authored numerous journal articles and conference papers, contributing to domains like neural networks, environmental modeling, and computer vision. His books include Artificial Intelligence: Theory and Applications (Springer, 2021) and Intelligent Systems: Models and Applications (Springer, 2013). Collaborations span academic and industrial projects, emphasizing practical applications of computational methods in real-world scenarios. Publications highlight contributions to fuzzy geometry, road lane detection, and SLAM algorithms, with a strong emphasis on interdisciplinary applications. Obradović's work often integrates machine learning with spatial data analysis, reflecting his expertise in both theoretical and applied computer science. His current research trends align with computational solutions for environmental monitoring, medical diagnostics, and geospatial challenges. No formal awards or grants are explicitly mentioned, though his extensive publication record and book authorship underscore his academic impact. His involvement in Singidunum's informatics faculty suggests teaching and mentoring roles, though specific advisees are not listed here. Projects include software systems for GIS-based simulations, sensor networks, and educational platforms.

Mohammad Nayeem TeliView profile

Senior Lecturer

• Computer Vision
• Machine Learning
• Data Science
• +3 more

Mohammad Nayeem Teli is a Senior Lecturer in the Department of Computer Science at the University of Maryland, College Park. He holds a PhD and M.S. in Computer Science from Colorado State University and a B.E. in Electronics and Communications from National Institute of Technology, Srinagar. His research focuses on Computer Vision, Machine Learning, and Data Science, with notable projects in vaccine design using protein language models, memory-efficient diffusion models, assistive technology for visually impaired individuals, and robotic navigation in dynamic environments. He has taught courses such as Introduction to Machine Learning , Computer Vision , and Data Science , winning the Excellence in Teaching Award (2018-2019). His work includes collaborations with institutions like Harrisburg University and National Institute of Technology, Srinagar, where he previously served as an Assistant Professor. He is an IEEE Senior Member and has received awards like the Best Oral Presentation Award at IEMTRONICS 2021. His research spans vaccine design with protein models, medical imaging optimization, and assistive robotics. Current projects explore invertible diffusion models for 3D medical imaging and LiDAR-based navigation aids for the visually impaired. Publications highlight contributions to adversarial robustness in autonomous vehicles, face recognition challenges, and resource allocation in online gaming systems.

Douglas CochranView profile

Academic

• Remote Sensing
• Signal Processing
• Mathematics
• +4 more

Douglas Cochran is a Research Professor in the School of Mathematical and Statistical Sciences and an Emeritus Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU). He has held roles such as Program Manager for Mathematics at the U.S. Air Force and DARPA, and served as a Visiting Faculty Fellow at the ASU Barrett Honors College. His research focuses on mathematical aspects of remote sensing, particularly for national security applications, with expertise in signal processing, radar systems, and information theory. Education: Ph.D. Applied Mathematics (Harvard, 1990), S.M. Applied Mathematics (Harvard, 1986), M.A. Mathematics (UC San Diego, 1980), S.B. Mathematics (MIT, 1979). Key Roles: Program Manager (U.S. Air Force, DARPA), Consultant (Australian Defense Science), Editor (IEEE Transactions on Signal Processing, Sampling Theory in Signal and Image Processing). His research interests include radar signal processing, multi-channel detection, and information-geometric foundations of sensing systems. Recent work addresses topics like cyclostationary signal analysis, radar clutter cancellation, and eigenvalue distributions in statistical signal processing. Notable awards include the IEEE Fellowship (2015), U.S. Department of Defense Medal for Exceptional Public Service (2005), and multiple teaching excellence recognitions. He has led over 20 grants, including NSF-funded projects on mathematical methods in medical imaging and MURI initiatives on information theory for adaptive systems. Cochran has advised numerous students and contributed to industry through consulting roles at companies like Motorola and Raytheon. His academic service includes editorial roles and organizing IEEE conferences, reflecting his broad impact in academia and defense applications.

• Blind Signal Processing
• Bayesian Estimation
• Particle Filters
• +3 more

Ayşın Baytan Ertüzün is a Professor at Boğaziçi University, specializing in signal processing and statistical methodologies. Their research focuses on blind signal processing, Bayesian estimation techniques, particle filters, and seismological applications. They have contributed extensively to defect detection in textiles using advanced signal processing methods like wavelet transforms and independent component analysis, as well as geophysical signal analysis through Bayesian deconvolution approaches. Over 25 years of academic work includes pioneering work in non-Gaussian processes, time-varying autoregressive models, and particle filtering algorithms for complex signal environments. Their research bridges theoretical advancements with practical applications in manufacturing quality control and seismic data analysis. Key technical contributions include hybrid deconvolution methods for Bernoulli-Gaussian processes, subband domain subspace analysis for texture defects, and entropy-based 2D lattice filter structures. Their work often emphasizes robust statistical frameworks for non-stationary and non-Gaussian signal environments, with applications spanning from environmental forecasting (wind speed modeling) to medical signal analysis (respiratory sound coherence). Publications span from foundational 1990s lattice filter innovations to modern Bayesian approaches. Their methodologies are characterized by interdisciplinary fusion of signal processing theory with real-world industrial and geophysical challenges. No specific awards are listed in the provided materials, though their extensive publication record indicates sustained academic impact.

• Neural Data Science
• Systems Neuroscience
• Computational Neuroscience
• +3 more

Benjamin Adric Dunn is an Associate Professor in (Neural) Data Science at NTNU's Department of Mathematical Sciences. Formerly, he held positions as a PhD researcher and postdoc at NTNU's Kavli Institute for Systems Neuroscience, with prior education including an MS in Computational Engineering from Purdue University and a BS in Applied Mathematics from the University of Connecticut. His career also includes industrial experience as a Computational Methods Engineer at Pratt & Whitney, UTC. His research focuses on integrating mathematical and computational approaches to understand neural systems, particularly spatial cognition, grid cells, and topological data analysis in neuroscience. Key contributions include work on toroidal representations in grid cells, posture coding in neocortex, and cohomological feature extraction from neural data. Publications emphasize multidisciplinary methods bridging algebraic topology, statistical physics, and neuroscience. Teaching includes courses like TMA4255 (Applied Statistics) and TMA4285 (Time Series). Outreach activities highlight engagement in scientific communication, including media interviews about posture-coding neurons and grid cell research.