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Laura WilsonView profile

Senior Lecturer

• Growth and development in vertebrates
• Geometric morphometrics
• Phenotypic evolution
• +5 more

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
• +2 more

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
• +5 more

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
• +4 more

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 FiumeView profile

Professor

• Computer Graphics
• Animation
• Fluid Simulation
• +3 more

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
• +2 more

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.

Pierre-Yves GousenbourgerView profile

Lecturer

• Interpolation on Manifolds
• Optimization on Manifolds
• Bézier Curves and Surfaces
• +5 more

Pierre-Yves Gousenbourger is a Teaching Assistant at the École polytechnique de Louvain (Louvain School of Engineering), part of the Catholic University of Louvain. He is affiliated with the ICTEAM research institute and supervises students in the Department of Electrical Engineering. His pedagogical approach includes the Apprentissage Par Projet (project-based learning) methodology. He holds a PhD (2020) and a Master's in Applied Mathematics from UCLouvain, both supervised by Pierre-Antoine Absil. His doctoral thesis, Interpolation and fitting on Riemannian manifolds , developed efficient algorithms for manifold-valued data fitting with applications in UAV control, medical imaging, and model reduction. His research focuses on interpolation, optimization, and imaging on Riemannian manifolds , emphasizing Bézier curves/surfaces for data approximation. Key areas include wind field estimation via covariance matrix fitting on SPSD manifolds, medical shape reconstruction (e.g., endometrial surfaces), and parametric model order reduction using manifold interpolation. His work integrates Riemannian geometry with computational efficiency, leveraging tools like exponential/logarithm maps. His publications consistently address data fitting on nonlinear manifolds , featuring Bézier-based methods, variational formulations, and applications spanning computer vision, medical imaging, and aerodynamics. Trends include denoising corrupted data, accelerating computational workflows, and generalizing Euclidean splines to manifolds. He collaborates with the RANSO and Dysco research groups and has worked with institutions including MIT, TU Chemnitz, and the University of Münster. He develops open-source numerical tools for manifold interpolation, distributed under the GNU GPL license.

• Reservoir Computing
• Machine Learning
• Neural Networks
• +4 more

Lyudmila Grigoryeva is a Professor at the University of St. Gallen. Her research focuses on reservoir computing, machine learning, and nonlinear dynamical systems. She has contributed to advancing theoretical frameworks for recurrent neural networks, including memory capacity analysis, approximation bounds, and time-series forecasting. Key collaborations include work with Juan-Pablo Ortega Lahuerta and Lukas Gonon on reservoir systems and stochastic processes. Research Themes: Infinite-dimensional reservoirs, geometric invariant learning, and parallel-in-time solvers. Applications: Macroeconomic forecasting, chaotic attractor learning, and PDE solving. Her recent work addresses foundational questions in reservoir computing, such as memory capacity evaluation and universal approximation properties. She has also explored interdisciplinary applications in econometrics and computational neuroscience.

• Measurement Technique
• GIS and Information Technologies in Planning
• Lidar
• +3 more

Ramazan Alper Kuçak is an Assistant Professor at the Department of Geomatics Engineering, Faculty of Civil Engineering at Istanbul Technical University. He holds a PhD in Geomatics Engineering from the same institution and has extensive experience as a Research Assistant at multiple universities, including Nigde University and Republic University. His research focuses on advanced geospatial technologies, including LiDAR, 3D modeling, and GIS applications in urban planning. Education: PhD in Geomatics Engineering (2016), Istanbul Technical University Master's Degree in Surveying Engineering (2013), Yildiz Technical University Bachelor's Degree in Surveying Engineering (2010), Yildiz Technical University Research Interests: His work emphasizes innovative applications of geomatics technologies, such as terrestrial laser scanning for heritage preservation, mobile mapping systems for smart cities, and comparative analysis of localization algorithms. He also explores low-cost sensor integration and open-source software for geospatial data analysis. Publications Trends: His recent articles highlight advancements in LiDAR sensor performance, 3D point cloud processing, and algorithmic improvements for geospatial data. He frequently publishes in domains like cultural heritage documentation, urban infrastructure analysis, and sensor fusion techniques. Professional Contributions: Dr. Kuçak has no documented scientific awards but has actively contributed to academic research through collaborative projects and teaching. His work experience spans research roles at multiple institutions, demonstrating a strong commitment to advancing geomatics engineering. Future Work: Ongoing research includes optimizing LiDAR integration with consumer electronics and enhancing point cloud registration algorithms for real-world applications.

• Remote Sensing
• Computer Vision
• Machine Learning
• +3 more

Şaziye Özge Atik is an Assistant Professor at the Department of Geomatics Engineering, Faculty of Civil Engineering, Istanbul Technical University. Her research focuses on Remote Sensing and Computer Vision, with applications in machine learning, hyperspectral imaging, and urban remote sensing. She holds a PhD in Geomatics Engineering from Istanbul Technical University (2021) and has held academic positions including Doctorate Lecturer (2023–present) and Lecturer roles at Gebze Technical University (2020–2023). She was an Academic Visitor at Twente University’s ITC EOS Department in 2018. Her academic achievements include winning the IEEE GRSS Türkiye Best Thesis Competition (2022). She has published extensively on topics such as satellite imagery analysis, UAV-based geospatial technologies, and machine learning applications in remote sensing. Her work integrates deep learning, object-based image analysis, and geospatial data processing to address environmental and urban challenges. Research interests span spectral-spatial data fusion, optimal band selection in hyperspectral imaging, and automated cartographic generalization. She has contributed to projects on coastal zone management, urban vegetation classification, and emergency evacuation modeling using geospatial algorithms like Dijkstra’s pathfinding.

• Applied Geophysics
• Geophysical Engineering
• Non-Destructive Testing
• +4 more

Orhan Apaydın is a Researcher at the Department of Geophysical Engineering, Istanbul Technical University. He holds a PhD in Geophysical Engineering (2023), an MSc with Thesis in Geophysical Engineering (2020), and a BSc in Geophysical Engineering (2014). His research focuses on applied geophysics, particularly Ground Penetrating Radar (GPR), non-destructive testing, and deep learning applications in geophysical imaging. He has contributed to studies on reinforced concrete structure analysis, buried object detection using AI models like Faster R-CNN and YOLOv5, and portable measurement systems for through-obstacle imaging. Education: PhD in Geophysical Engineering (2023), Istanbul Technical University MSc in Geophysical Engineering (2020), Istanbul Technical University BSc in Geophysical Engineering (2014), Istanbul Technical University Research Highlights: His work integrates GPR with advanced computational methods to address challenges in structural integrity assessment and subsurface imaging. Recent studies include developing deep learning algorithms for object detection in GPR data and optimizing portable measurement systems for field applications. Awards: 3rd Best Presentation at the 5th Virtual Student Conference (Society of Exploration Geophysicists, 2021) Grants/Advising: No specific grants or advisee information is available in the provided texts. His current role as a Research Assistant involves academic duties within the department.

Gabriel WaltonView profile

Professor

• Rock Mechanics
• Numerical Modeling
• Geotechnical Engineering
• +4 more

Gabriel Walton is a Professor and Associate Department Head in the Department of Geology and Geological Engineering at the Colorado School of Mines. He holds a Ph.D. and B.A.Sc. in Geological Engineering from Queen’s University (2014 and 2011, respectively). His research focuses on rock mechanics, numerical modeling of slopes and underground works, rockmass characterization, and applications of geophysics and remote sensing. He leads the Geomechanics Lab ( geomech.mines.edu ), emphasizing in situ monitoring, machine learning for geohazard prediction, and geotechnical modeling. Key research areas include: Brittle rock behavior under stress-relaxation and creep Rockfall hazard assessment and mitigation strategies Numerical simulation of rockmass analogs using bonded block models Integration of LiDAR and photogrammetry for slope stability analysis His work bridges theoretical rock mechanics with practical engineering solutions, addressing challenges in mining, tunneling, and natural hazard management. Recent studies explore post-fire sediment dynamics and the influence of mineralogical properties on coal strength. He has pioneered machine learning approaches for automated feature extraction in rock slope monitoring datasets. Walton’s contributions have advanced understanding of pillar-support interaction in mines and the validation of geophysical systems for tunneling. His research consistently integrates laboratory experiments with field-scale observations to improve predictive models for geotechnical systems.

Elizabeth SackettView profile

Senior Lecturer

• Materials Science
• Metallurgy
• Mechanical Properties
• +3 more

Dr. Elizabeth Sackett is a Senior Lecturer in Materials Science and Engineering at Swansea University's Materials Research Centre, collaborating closely with Tata Steel UK on steel metallurgy and processing. She holds a PhD in Materials Science and Engineering from the US Air Force-sponsored research on Ti685 dwell fatigue. Her research focuses on mechanical properties of materials, nanoindentation, residual stress, and strain mapping. She teaches modules on advanced metallic systems, including microstructure analysis and steel metallurgy. Affiliations: Materials Research Centre, Swansea University Roles: Athena SWAN lead for Engineering Research interests span fatigue, creep, and corrosion, with over 30 international industry collaborations. Recent work includes studies on chromium-based coatings for packaging steel, titanium alloy fatigue, and additive manufacturing materials. Supervised numerous PhD/EngD/MSc students in steel optimization, hydrogen embrittlement, and material characterization. Key Collaborations: Tata Steel UK Expertise: Nanoindentation, residual stress analysis, steel metallurgy

Viktor LarssonView profile

Assistant Professor

• Computer Vision
• Machine Learning
• 3D Geometry Estimation
• +4 more

Viktor Larsson is an Assistant Professor at the Centre for Mathematical Sciences, Lund University, specializing in Computer Vision and Machine Learning. Previously, he worked as a PostDoc and senior researcher at ETH Zurich's Computer Vision and Geometry group under Marc Pollefeys. His research focuses on geometric estimation problems in 3D computer vision, including Structure-from-Motion, visual localization, SLAM, and integrating machine learning into classical pipelines. Key research interests include robust estimation techniques, hybrid feature usage, and algorithmic efficiency. He actively supervises PhD/MSc students and has secured grants for impactful projects. Recent work explores data-driven alternatives to hand-crafted heuristics in vision tasks. Notable achievements include a docent qualification (2024), Best Paper Honorable Mention (CVPR 2024), and Best Student Paper (ICCV 2021). His articles span topics like RANSAC optimization, neural scene encoding, and eigenvalue-based localization methods. He also serves on conference committees for CVPR, 3DV, and ECCV, contributing to academic discourse through tutorials and plenary talks.

Yingzhen YangView profile

Assistant Professor

• statistical machine learning
• deep learning
• optimization for machine learning
• +9 more

Yingzhen Yang is an Assistant Professor at Arizona State University's School of Computing and Augmented Intelligence. His research focuses on statistical machine learning, deep learning, optimization, and theoretical computer science. He has held industrial internships at Microsoft Research and Hewlett-Packard Labs, and has contributed to projects in computer vision and graphics. Ph.D. from the University of Illinois Research interests include theory of deep learning, subspace learning, sparse representation, and optimization. His work bridges machine learning theory and applications, with notable contributions to L0-Sparse Subspace Clustering and neural architecture search. Recent publications highlight advancements in graph neural networks, transformer optimization, and medical image classification. His work on L0-SSC earned a Best Paper Finalist award at ECCV 2016. Awards: ECCV 2016 Best Paper Finalist, 2010 Carnegie Dean's Tuition Fellowship Advises students in the Statistical Deep Learning Lab, focusing on topics like model compression and AutoML. Collaborates on synthetic data generation and robust neural architecture search. Labs/Teams: Leads the Statistical Deep Learning (SDL) Lab, fostering research in theory-driven machine learning applications.