جست‌وجوی استاد و پژوهشگر

• Computer Vision
• 3D Reconstruction
• Image Processing
• +۳ مورد دیگر

Margrit Gelautz is an Associate Professor at TU Wien, leading the research group on Image and Video Analysis & Synthesis. She holds a PhD from Graz University of Technology (1997) and conducted postdoctoral research at Stanford University's Radar Interferometry Group. Her work focuses on 3D reconstruction, stereo analysis, and applications in autonomous driving and robotics. She directs the Doctorate College 'TrustRobots' and has pioneered projects like the AlphaMatting.com evaluation system. Key roles include heading the 'Precision Livestock Farming' unit and serving as a university lecturer in Computer Vision. Education: PhD in Computer Science, Graz University of Technology (1997) Postdoctoral Research, Stanford University (1999–2001) Research Interests: 3D scene reconstruction, stereo analysis, image matting, autonomous driving systems, and robotics. Her group develops tools for 3D film production, novel view synthesis, and real-time depth estimation using structured light. Current projects include CarVisionLight (autonomous driving vision systems) and Precise3D (cost-efficient 3D scanning workflows). Grants & Awards: Interactive TV Award 2009 Microsoft Research Stipend 2007 Funding for 10 PhD positions in the 'TrustRobots' Doctorate College Labs & Teams: Her research group collaborates on EU initiatives like 3D-ConTourNet and maintains the AlphaMatting.com platform. Projects span dynamic vision sensors, event-based stereo systems, and semantic SLAM for robotics.

Thibault Lejembleمشاهده پروفایل

دانشیار

• 3D Point Cloud Analysis
• Pattern Recognition
• Curvature Estimation
• +۲ مورد دیگر

Thibault Lejemble is an Associate Professor at EPITA specializing in 3D data analysis. His research develops efficient algorithms for processing large point clouds from LiDAR and photogrammetry, with applications in pattern recognition, reconstruction, and GPU-accelerated computing. Current investigations focus on curvature estimation, discrete topology, and relationships between mathematical morphology and topological data analysis. Publications showcase innovations in point cloud processing libraries, neural networks for edge detection, and differential estimators, reflecting consistent advancement in computational geometry and real-time 3D data analysis techniques.

José E. Chacónمشاهده پروفایل

استاد

• Nonparametric Statistics
• Kernel Smoothing Methods
• Density Estimation
• +۳ مورد دیگر

José E. Chacón is a Professor of Statistics in the Department of Mathematics at the University of Extremadura since 2022. Previously, he held roles at the University of Valladolid and other positions within Extremadura. His research focuses on nonparametric statistics, kernel smoothing methods for density estimation, and their applications in clustering. He is actively involved in initiatives like the NICDA Workshop and Statistics Reading Club. Education history details are not explicitly provided in the text. His research interests emphasize advanced statistical methodologies including density derivative estimation, nonparametric clustering, and applications in epidemiology and ecology. Notable contributions include work on Bayesian taut splines, mode-based symmetry estimation, and statistical modeling for animal home ranges. Recent publications (2020–2024) explore cutting-edge topics such as geodesic normal distributions, cross-validation techniques, and SIR modeling for pandemic data. His work bridges theoretical advancements with practical applications in health, ecology, and machine learning. Awards and grants are not explicitly mentioned. Chacón has advised no listed students but collaborates on interdisciplinary projects, including the special issue on Data Science for COVID-19. His research lab focuses on multivariate kernel smoothing and nonparametric methods.

• Biological Anthropology
• Primatology
• Human Evolution
• +۴ مورد دیگر

Dr. Ameline Bardo is an Honorary Research Fellow at the University of Kent's School of Anthropology and Conservation, specializing in Biological Anthropology and Primatology. She investigates the evolution of tool use, manipulation in primates, and the unique features of the human hand. Bardo holds a PhD from the National Museum of Natural History (Paris) and has conducted postdoctoral research via the Fyssen Foundation and as a Postdoctoral Research Associate. She manages the APE lab, focusing on Animal Postcranial Evolution. Her research integrates behavioral, archaeological, morphological, and biomechanical approaches to explore hominid hand evolution. Notably, her work challenges traditional assumptions about the human hand's uniqueness linked to stone tool-making. Key projects include the 'Evolution of Human Dexterity' and comparative primate studies. Publications span topics like hand morphology, Neanderthal manipulation, and Australopithecus sediba. Her work appears in journals such as Scientific Reports , Nature Ecology and Evolution , and Journal of Human Evolution .

Georgios Kokkorisمشاهده پروفایل

دانشیار

• Plasma Processes
• Microfluidics
• Chemical Vapor Deposition (CVD)
• +۵ مورد دیگر

Georgios Kokkoris is an Associate Professor at the Department of Analysis, Design and Development of Processes and Systems , National Technical University of Athens (NTUA) , School of Chemical Engineering. His research focuses on plasma processes and microfluidic systems with applications in biomedical engineering, food preservation, and nanotechnology. Leader of the Plasma and Microfluidics Research Group Principal Investigator for projects: plasmAI (Intel-Merck), SALVAGE (HFRI), VortexLC (HORIZON), and LagoMeal (Fisheries Programme) Teaches Transport Phenomena I , Industrial Reactor Engineering , and Microfluidics at NTUA His research combines plasma technology for surface engineering with microfluidic device development , particularly in DNA amplification platforms and biomedical applications. Recent work emphasizes AI integration for plasma etching optimization and sustainable food processing solutions. Notable trends in his 15+ peer-reviewed articles (2019-2023) include: Advancements in cold atmospheric plasma (CAP) for enzyme inactivation in food products Development of PCB-based microreactors for rapid DNA diagnostics Studies on plasma-induced surface roughness across polymers and silicon Innovations in nanoparticle strain sensors for microfluidic flow analysis Applications of plasma nanotexturing for cardiac cell engineering Grants and collaborations span EU Horizon initiatives and national programs, with a focus on technology transfer and interdisciplinary applications.

• Computer Graphics
• Procedural Generation
• Real-time Rendering
• +۷ مورد دیگر

Pascal Guehl is a Part-Time Lecturer in Computer Graphics at the University of Strasbourg and Head of the new Master of Science and Technology in Extended Cinematography at École Polytechnique (LIX lab). He holds a PhD in Computer Graphics from the University of Strasbourg (2022) and has extensive experience in R&D roles across industry and academia. His core expertise spans procedural generation, real-time rendering, GPU computing, and virtual production technologies, with notable contributions to texture synthesis and semi-procedural modeling. Education highlights include a MRes from University Lyon 1 (2016), an Advanced Master from Arts & Métiers ParisTech (2005), and a master's in Physics from University Cergy-Pontoise (2000). His teaching spans over 15 years, covering C++ programming, real-time 3D graphics, and generative AI. Research focuses on interdisciplinary applications of computer graphics in film production, biology visualization, and defense systems. Key achievements include the semi-procedural texture synthesis framework (SIGGRAPH 2020) and leadership in ANR-funded projects like Animation Conductor (2023-2027). Awards include a Graphics Replicability Stamp and Honorable Mention for procedural texture work. Professional affiliations include ICube research lab (University of Strasbourg), where he co-supervises PhD students like Tara Butler, and collaborations with institutions like Telecom Paris, ENS Louis-Lumière, and Purdue University. Current projects emphasize extended cinematography tools and generative AI integration in visual effects pipelines.

• Algorithms
• Social Networks
• Machine Learning
• +۳ مورد دیگر

Antonis Matakos is a Postdoctoral Researcher in the Department of Computer Science at Aalto University, working in the group led by Adjunct Professor Aris Gionis. His research focuses on algorithms, social networks, and machine learning with an emphasis on topics like diversity maximization, matrix approximation, and randomized algorithms. He holds a Master's degree in Natural Sciences from the University of Ioannina (Panepistimio Ioanninon, awarded July 2017). Research Interests: His work bridges theoretical algorithm design with practical applications in social network analysis. Key areas include: Algorithmic fairness and diversification in data selection Graph algorithms for network optimization and connectivity Randomized methods for large-scale data approximation Applications of linear algebra in machine learning and network science Publications: His recent work explores topics such as multilinear Johnson-Lindenstrauss transforms (2025), fair column subset selection (2024), and leveraging matrix approximations for network analysis (2022). These contributions highlight a trend toward developing scalable, theoretically grounded solutions for modern data challenges. Collaborations: Active in international research networks, he has collaborated on projects analyzing signed networks, polarization dynamics, and social media impact. His work has been recognized with over 60 citations and featured in venues like KDD and IEEE TKDE. Labs/Teams: Currently part of the Gionis Research Group at Aalto, focusing on data science and network analysis.

Eric Brachmannمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Object Pose Estimation
• +۵ مورد دیگر

Dr. Eric Brachmann is a Researcher at Heidelberg University 's Visual Learning Lab (since 2017) and a Guest at Leibniz University Hannover (since 2019). He earned his Dr. rer. nat. in 2018 from TU Dresden (summa cum laude), preceded by a Diplom in media computer science (2012) and studies (2006–2012) at TU Dresden. Doctorate: TU Dresden (2018, summa cum laude) Diplom: TU Dresden (2012, passed with distinction) Education: Media and computer science (2006–2012) His research focuses on Computer Vision and Machine Learning , particularly 6D object pose estimation , camera localization , and neural-guided optimization . His work bridges classical geometric methods (e.g., RANSAC) with modern deep learning techniques, including differentiable optimization and reinforcement learning for pose estimation. His publications emphasize end-to-end learning , robust model fitting , and RGB-D image analysis , with applications in robotics and 3D scene understanding. Key contributions include DSAC, CONSAC, and neural extensions of RANSAC for efficient hypothesis sampling. 2018 : GI Dissertation Award nomination 2014 : ACCV Honorable Mention Demo Award 2012 : Enno Heidebroek Award for top graduate 2008–2012 : German National Academic Foundation scholarship 2008 : IBM Award for intermediate diploma As a co-organizer of ICCV and ECCV workshops, Eric drives collaboration in visual localization and 6D pose estimation . He has reviewed for CVPR, ICCV, NeurIPS, and TPAMI, earning recognition as an Outstanding Reviewer (CVPR 19, NeurIPS 19). He has held industry roles at IBM (2010–2011) and T-Systems (2008–2009). At TU Dresden and Heidelberg, he taught courses on computer vision and 3D world reconstruction , supervised theses, and developed practical seminars.

Benjamin Greweمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Neural Networks
• Neuroscience
• +۳ مورد دیگر

Prof. Dr. Benjamin Grewe is an Associate Professor at the Department of Information Technology and Electrical Engineering at ETH Zürich. His research focuses on the intersection of artificial intelligence, neuroscience, and neural networks, with a particular emphasis on cortical hierarchies, continual learning, and biologically plausible algorithms. He leads projects involving deep feedback control, synaptic connectivity analysis, and neural ensemble dynamics. Grewe teaches courses such as Learning in Deep Artificial and Biological Neuronal Networks and Reinforcement Learning Basics , integrating theoretical and applied perspectives. His work bridges computational models with biological insights, contributing to advancements in medical robotics, process control, and AI safety. His research interests span neural network architectures , continual learning , and biological neuronal systems . Recent projects explore synaptic plasticity in cortical microcircuits and the application of AI to surgical planning and industrial automation. Grewe’s publications reflect a multidisciplinary approach, addressing challenges in both technical and biological domains. No scientific awards are explicitly mentioned in the provided texts. His advising and grant activities remain unspecified in the available data. His lab, part of the Neural and Intelligent Systems group, focuses on developing biologically inspired algorithms and neural interfaces.

Laura Wilsonمشاهده پروفایل

مدرس ارشد

• Growth and development in vertebrates
• Geometric morphometrics
• Phenotypic evolution
• +۵ مورد دیگر

Dr. Laura Wilson is a Senior Lecturer and Visiting Senior Fellow in the School of Biological, Earth & Environmental Sciences at the University of New South Wales. Her academic work spans multiple institutions through her visiting fellowship, focusing on evolutionary biology and morphological studies of vertebrates. Based in Room G14 of the Samuels Building on UNSW's Upper Campus, she maintains an active research program that bridges computational methods with biological questions. Dr. Wilson's research interests center on growth and development in vertebrates, with particular expertise in geometric morphometric methods to study phenotypic evolution. She also specializes in statistical analysis of shape and 3D modeling, with theoretical applications to evolutionary developmental biology. Her work examines how morphological traits evolve across species and through developmental time, with a special focus on mammals including bats, marsupials, and rodents. She has established herself as a leading researcher in applying quantitative methods to evolutionary questions. Analysis of Dr. Wilson's recent publications reveals a strong interdisciplinary focus integrating geometric morphometrics with evolutionary biology to understand phenotypic diversity. Her work examines cranial evolution, skeletal development, and specialized adaptations like echolocation in bats. She frequently employs 3D modeling and statistical approaches to compare morphological features across species and through evolutionary time. Her research often involves international collaborations examining morphological patterns across diverse vertebrate taxa. Current service on editorial boards for PeerJ, Mammalian Biology, Palaeontologica Electronica, MorphoMuseuM (M3), and American Journal of Physical Anthropology Council Member, European Society for Evolutionary Developmental Biology (EED) Dr. Wilson actively mentors PhD students, including Camilo Lopez-Aguirre. Her research has been supported by multiple grants enabling extensive comparative studies across diverse taxa. She collaborates with international teams across Europe, Asia, and the Americas on projects ranging from bat echolocation to marsupial evolution. Her work has significant implications for understanding evolutionary processes and developmental constraints in vertebrates. Dr. Wilson is involved in several academic initiatives including the UNSW Women in Maths & Science Champion program, promoting diversity in STEM fields. Her laboratory work focuses on advanced morphometric techniques and 3D modeling of skeletal structures across diverse vertebrate taxa, with applications to evolutionary questions and developmental biology.

• Data Science
• Geometric Data Analysis
• Topological Data Analysis
• +۲ مورد دیگر

Washington Mio is a Professor in the Department of Mathematics at Florida State University, specializing in data science, geometric and topological data analysis, and their scientific applications. His research bridges theoretical mathematics and practical implementations, focusing on complex data structures and geometric domains. Dr. Mio's primary research interests include developing methods for analyzing high-dimensional data using topological approaches, metric space analysis, and functional data on geometric domains. He works extensively on problems related to shape discrimination, persistent homology, and optimization techniques in metric spaces. His recent publications demonstrate a strong focus on topological data analysis, geometric statistics, and computational topology. Key trends include the development of unified frameworks for persistent homology, methods for analyzing functional data on geometric domains, and applications in materials science and biology. The research shows consistent innovation in mathematical approaches to complex data challenges.

• Geoinformatics
• Geodesy
• Remote Sensing
• +۵ مورد دیگر

Christoph Dold is a Researcher affiliated with the Institute of Cartography and Geoinformatics at Leibniz University Hannover. His work focuses on terrestrial laser scanning, 3D city modeling, and geodata fusion. He contributed to Prof. Brenner's Volkswagen Foundation-funded research group, specializing in automatic registration of laser scanner data. Key research interests include automated processing of point clouds, planar structure analysis, and integration of airborne and terrestrial data. His 2010 monograph on layer-based registration methods is a seminal work in the field. He received a best paper award in 2007 for work on laser scan orientation algorithms. His publications span peer-reviewed journals and conferences like ISPRS and SilviLaser, addressing urban environment modeling, 3D reconstruction, and sensor fusion techniques. Current affiliations include the Faculty of Civil Engineering and Geodetic Science, where he contributes to advancements in geoinformatics and geodesy.

• Geoinformatics
• Cartography
• Cadastral Systems
• +۴ مورد دیگر

David Siriba is a researcher at the Institute of Cartography and Geoinformatics within the Faculty of Civil Engineering and Geodesy at Leibniz University Hannover. His expertise centers on spatial data integration and fusion, with specialized focus on cadastral systems and land administration infrastructure development. He completed his doctoral studies as a DAAD fellow at the Institute of Cartography and Geoinformatics (ikg), where his research addressed critical challenges in conflation of provisional cadastral and topographical datasets. His academic foundation combines advanced geospatial methodologies with practical applications in land administration systems. Dr. Siriba's research portfolio demonstrates exceptional depth in geospatial data quality enhancement and integration. Key focus areas include: Spatial data conflation techniques for heterogeneous datasets Geometric accuracy improvement of legacy cadastral records Automatic georeferencing of non-geospatial cadastral maps Linear feature alignment algorithms for inconsistent spatial datasets Development of survey-accurate national spatial data infrastructures His work bridges theoretical geoinformatics with real-world land administration challenges, particularly in developing contexts like Kenya where he has conducted significant field research. Analysis of his publication record (2009-2012) reveals a consistent trajectory in advancing cadastral data quality through innovative methodologies. His research demonstrates increasing sophistication in spatial transformation techniques—from foundational positional accuracy assessments to advanced thin plate splines transformations and vector potential field alignments. The work shows strong emphasis on African land administration contexts while developing universally applicable geospatial integration frameworks. Dr. Siriba conducted his research under the DAAD fellowship support at Leibniz University Hannover's Institute of Cartography and Geoinformatics, collaborating extensively with Professor Monika Sester's research group. His work remains influential in geospatial data integration methodologies despite the publication gap after 2012, suggesting potential transition to industry applications or policy development roles.

Eugene Fiumeمشاهده پروفایل

استاد

• Computer Graphics
• Animation
• Fluid Simulation
• +۳ مورد دیگر

Eugene Fiume is a Professor and Dean of the Faculty of Applied Sciences at Simon Fraser University (SFU). He holds a Ph.D. in Computer Science from the University of Toronto (1986), an M.Sc. from the same institution (1982), and a B.Math (Hons.) in Computer Science from the University of Waterloo (1981). His research focuses on computer graphics, animation techniques, and educational applications of technology. He leads the Tangent Lab, exploring advanced topics in fluid simulation, facial animation, and physics-based motion retargeting. His work spans interdisciplinary areas, including fluid dynamics, mesh processing, and game development. Notable contributions include VEMPIC (particle-based fluid simulation), animator-centric tools like Jali for lip synchronization, and vocal animation frameworks for expressive speech. He has held leadership roles in academic administration and has contributed to industry-research collaborations through his deanship. Publications emphasize innovation in animation systems, real-time rendering, and procedural modeling. His research has been applied in gaming (e.g., Cyberpunk 2077 facial animations) and medical imaging, reflecting a blend of theoretical and applied computer science.

• Riemannian Geometry
• Inverse Spectral Problems
• Geometry of Lie Groups
• +۲ مورد دیگر

Carolyn S. Gordon is a Professor of Mathematics at Dartmouth College, holding the Benjamin Cheney Professorship in Mathematics. She is affiliated with the Department of Mathematics, where her research focuses on Riemannian Geometry, inverse spectral problems, and the geometry of Lie groups. Education: B.S., Purdue University M.S., Purdue University Ph.D., Washington University Research Interests: Her work explores the spectral properties of geometric structures, including isospectral manifolds, transplantation techniques, and symplectic rigidity in nilmanifolds. She investigates how geometric features influence spectral invariants and vice versa, with applications to global analysis and geometric flows. Works in Progress: Current projects include asymptotic expansions of heat kernels on orbifolds, inverse spectral results on even-dimensional tori, isoscattering obstacles and potentials, and spectral analysis of normal homogeneous metrics. Grants & Advising: No grants or student advisees are explicitly listed in the provided materials. She contributes to the Mathematics Department’s research initiatives and collaborates with international scholars. Labs/Teams: Active in collaborative research groups within differential geometry and spectral theory, but no specific lab or team names are mentioned.