جست‌وجوی استاد و پژوهشگر

• Geomatics Engineering
• Remote Sensing
• Civil Infrastructure Monitoring
• +۳ مورد دیگر

Mostafa Arastounia is an Assistant Professor of Geospatial Sciences at Kennesaw State University (KSU). He holds a PhD in Geomatics Engineering from the University of Calgary (Canada) and an MSc in Geo-Information Science from the University of Twente (Netherlands). He is licensed as a Professional Engineer in Geomatics in British Columbia and holds a Project Management Professional (PMP) certification. His research focuses on automated processing of LiDAR and Unmanned Aircraft (UA) data for infrastructure monitoring, including railroads, tunnels, and electrical substations. He develops algorithms for object recognition, 3D modeling, and workflow optimization in geospatial applications. He serves as a Topic Editor for Remote Sensing Journal and is a reviewer for journals like IEEE Transactions on Geoscience and Remote Sensing and Sensors . His teaching includes undergraduate courses in surveying, civil engineering, and geography at KSU. His work bridges geomatics, remote sensing, and civil engineering to enhance infrastructure resilience and maintenance through advanced data analytics. Key research areas include automated mapping of civil infrastructure, LiDAR-based asset recognition, UAS data workflows, and interdisciplinary applications of geospatial technologies. His peer-reviewed publications span journals like ISPRS Journal of Photogrammetry and Infrastructures , with a focus on advancing automation in geospatial data processing.

Francesco Galuppiمشاهده پروفایل

استادیار

• Complex Algebraic Geometry
• Tensor Decompositions
• Applied Algebraic Geometry
• +۳ مورد دیگر

Francesco Galuppi is an Assistant Professor at the University of Warsaw, within the Faculty of Mathematics, Informatics and Mechanics. His research focuses on complex algebraic geometry, tensor decompositions, and their applications to signature tensors of paths, bridging theoretical mathematics with applied sciences like data analysis and machine learning. He leads the Sonata grant 'Tensor rank and its applications to signature tensors of paths' funded by the National Science Center, Poland. His work explores geometric approaches to tensor structure, emphasizing interactions across disciplines. Key topics include secant varieties, tensor rank, and symmetry analysis of signature tensors. Galuppi has organized workshops such as 'Apolarity in Varsavia' and co-organized conferences like the SIAM minisymposium 'Tensors in Applications.' His recent activities include talks on path signatures, eigenpoint schemes, and defectivity of Segre-Veronese varieties, reflecting his commitment to advancing algebraic geometry’s practical applications. He actively engages in academic communities, delivering seminars and collaborating internationally. His research narrative includes grants, interdisciplinary collaborations, and contributions to thematic semesters like AGATES, focusing on tensors in diverse fields from quantum physics to optimization.

Frédéric PASCALمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Adaptive Radar Detection
• +۵ مورد دیگر

Frédéric Pascal is a Full Professor at CentraleSupélec, part of the University of Paris-Saclay, and a member of the Laboratoire des Signaux et Systèmes (L2S). His research focuses on statistical signal processing, machine learning, and robust estimation techniques, with applications in radar detection, covariance matrix estimation, and information geometry. He has held roles including Coordinator of AI activities at CentraleSupélec and Head of the "Signals and Statistics" group at L2S. His academic journey includes a PhD from University Paris X – Nanterre (2006) and an HDR (2012) from University Paris-Sud. His work emphasizes adaptive signal processing in non-Gaussian environments, with contributions to robust covariance estimation, M-estimators, and applications in radar systems and biomedical signal processing. He has authored over 100 journal/conference papers and serves as an Associate Editor for IEEE Transactions on Signal Processing and Elsevier Signal Processing. Current research interests include AI transparency, data-driven methods for industry 4.0, and health-related signal analysis.

• Nonlinear Control Theory
• Geometric Control
• Optimal Control
• +۳ مورد دیگر

Frédéric JEAN is a Professor and Director of the Unité de Mathématiques Appliquées (UMA) at ENSTA Paris. His expertise lies in nonlinear control theory and geometric control, with applications in neurophysiology modeling. He holds an HDR (Habilitation à Diriger des Recherches) and advises doctoral students across multiple domains. Research Interests: Frédéric’s research focuses on geometric control theory, sub-Riemannian geometry, optimal control, and nonholonomic systems. His work bridges theoretical advancements with practical applications in neurophysiology and aerospace engineering. Recent studies include trajectory planning for planetary landings and robust control strategies under uncertainty. Teaching: He teaches courses on applied differential geometry, dynamical systems analysis, and control systems at ENSTA Paris. Publications: His recent work emphasizes sub-Riemannian geometry, stochastic control, and aerospace applications. Key trends include optimal control in constrained systems and geometric approaches to robotic and biological movement. Students: Co-supervised over 10 doctoral theses, including those by Clara Leparoux (2023), Meryem Kafnemer (2022), and Sofya Maslovskaya (2018). Labs/Teams: Active within the Optimization and Commande team at UMA, contributing to interdisciplinary research in control systems and applied mathematics.

• Manufacturing Engineering
• Computer-Aided Design (CAD)
• Digital Mock-Ups
• +۴ مورد دیگر

Dr Adelaide Marzano is a Lecturer at the School of Computing Engineering and the Built Environment, Edinburgh Napier University, and a Visiting Professor at Pentecost University, Ghana. Her research focuses on manufacturing systems, CAD integration, tolerance analysis, and educational technology. She actively contributes to the Centre for Engineering and Mathematical Modelling, examining topics such as digital twin simulations for industrial ergonomics and e-learning efficacy. She serves as a PhD External Examiner and is a member of the Institution of Mechanical Engineers. Affiliations: Edinburgh Napier University (Primary), Pentecost University (Visiting) Key Roles: PhD Examiner, Research Group Member (Centre for Engineering and Mathematical Modelling) Her research spans manufacturing processes, including tolerance-aware product development and virtual reality applications in aerospace training. Recent work includes optimizing distillery operations via digital twin frameworks and investigating student preferences for e-learning tools across UK and Portuguese institutions. She also collaborates with industries like COMAU on ergonomic work cell design. Her current PhD project supervision involves machining optimization of additively manufactured medical components, emphasizing interdisciplinary approaches to advance manufacturing precision and efficiency.

Arnaldo Delli Carriمشاهده پروفایل

دانشیار

• Nonlinear Dynamics
• Finite Element Analysis
• Nonlinear Vibration Analysis
• +۳ مورد دیگر

Arnaldo Delli Carri is an Associate Professor at the CEES School of Engineering, serving as Curriculum Lead and actively involved in academic leadership. His research focuses on Nonlinear Dynamics and Finite Element Analysis, with expertise in structural analysis, modal testing, and vibration dynamics. He is currently accepting PhD students in areas like Nonlinear Vibration Analysis and FE model validation. Research interests include advanced topics such as nonlinear system identification, model upgrading, and machine learning applications in structural performance prediction. Notable projects involve analyzing planetary gearboxes, 3D printing opportunities in Africa, and laser vibrometer-based nonlinear detection. His work integrates experimental and computational methods to address challenges in mechanical systems and biomedical engineering. Collaborations span international partnerships, focusing on structural dynamics and nonlinear systems. His contributions include over 17 publications since 2011, with a focus on vibration analysis, finite element validation, and additive manufacturing applications. He currently supervises PhD candidates exploring nonlinear dynamics and FE formulations.

Kasper Green Larsenمشاهده پروفایل

استاد

• Machine Learning
• Theoretical Computer Science
• Algorithms and Data Structures
• +۳ مورد دیگر

Kasper Green Larsen is a Professor in the Department of Computer Science at Aarhus University. His research focuses on theoretical computer science, machine learning, algorithms, and data structures. He has made significant contributions to boosting algorithms, PAC learning theory, and computational geometry. His work often bridges algorithm design with complexity theory, addressing challenges in optimization, memory efficiency, and lower bounds analysis. Key research areas include: Algorithmic Learning Theory (e.g., boosting, bagging, and PAC learners) Data Structure Design (e.g., invertible Bloom tables, succinct representations) Computational Complexity (e.g., lower bounds for dynamic and oblivious algorithms) Geometric Algorithms (e.g., hierarchical searching, range queries) Recent publications emphasize foundational advancements in learning theory (e.g., optimal weak-to-strong learning) and data efficiency (e.g., memory-reduced Bloom filters). His work frequently appears in top conferences like IJCAI, ICALP, and SODA, reflecting rigorous theoretical contributions with practical implications.

Ashwin Pananjady Martinمشاهده پروفایل

استادیار

• High-dimensional statistics
• Statistical machine learning
• Reinforcement learning
• +۴ مورد دیگر

Ashwin Pananjady Martin is an Assistant Professor at Georgia Tech with a joint appointment between the H. Milton Stewart School of Industrial and Systems Engineering and the School of Electrical and Computer Engineering. His research focuses on high-dimensional statistics, statistical machine learning, reinforcement learning, and information theory, with applications in data science and signal processing. He holds a Ph.D. from UC Berkeley's EECS department and a B.Tech. from IIT Madras. Education: Ph.D. in Electrical Engineering and Computer Sciences, UC Berkeley B.Tech. in Electrical Engineering, IIT Madras His research emphasizes statistical and computational challenges in high-dimensional data with geometric structures. Notable awards include the Lawrence D. Brown award and David J. Sakrison Prize for dissertation work, alongside teaching and industry fellowships. Awards: Inaugural Lawrence D. Brown Ph.D. student award David J. Sakrison Memorial Prize Swiss Re Research Fellowship Prior industrial experience includes research at Amazon and Microsoft during his Ph.D. No specific grants or advising details are provided in the text.

Montserrat Rosمشاهده پروفایل

دانشیار

• Embedded Systems
• Sensor Networks Data Fusion
• Cyber-Physical Systems
• +۶ مورد دیگر

Montserrat Ros is an Associate Professor and Associate Dean (Education) at the School of Electrical, Computer and Telecommunications Engineering within the Faculty of Engineering and Information Sciences at the University of Wollongong, Australia. She has been with the university since 2006, initially joining as a Lecturer in Computer Engineering and progressing to her current senior academic and leadership roles. Her educational background includes: B.E.(Hons1)/B.Sc. double degree majoring in Computer Systems Engineering and Mathematics from the University of Queensland (2000) Ph.D. degree in Computer Engineering from the University of Queensland (2007) Professor Ros's research focuses on the intersection of embedded computing systems and practical engineering applications. Her work spans several key areas including embedded systems design, sensor network data fusion, cyber-physical systems development, and innovative approaches to engineering education. She has particular expertise in sensor-based localization techniques, computer architecture optimization, and code compression methodologies for resource-constrained environments. More recently, her research has expanded into machine learning applications for constrained systems and Internet of Things implementations. Analysis of her recent publication record reveals a strong emphasis on Internet of Things networks, UAV-based systems, and applications of artificial intelligence in both engineering education and manufacturing processes. Her work demonstrates a consistent pattern of bridging theoretical computer engineering concepts with practical real-world applications across diverse domains including healthcare, environmental monitoring, and industrial automation. Her significant contributions to academia have been recognized through numerous prestigious awards: 2019: AAUT Citation for Outstanding Contribution to Student Learning 2018: IEEE TALE 2018 Meritorious Service Award 2018: Featured in UOW Leadership in Education Booklet 2017: UOW Vice Chancellor's Award for Outstanding Contribution to Teaching and Learning 2016: UOW Women of Impact for inspiring young women in STEM 2015: UOW Vice Chancellor's Interdisciplinary Research Excellence Award 2012 & 2007: UOW Vice Chancellor's Awards for Teaching Excellence 2011: UOW Vice Chancellor's Award for Community Engagement Senior Fellow of WATTLE (Wollongong Academy for Tertiary Teaching & Learning Excellence) Professor Ros has secured substantial research funding across multiple projects spanning from 2006 to the present. Her grant portfolio demonstrates a consistent focus on engineering education innovation, sensor network development, and practical applications of embedded systems. Notable projects include "The AI Tutor: Enabling 24x7 student support across engineering" (2024), "AI/IoT-powered Airborne System for Monitoring Water Level and Tidal Floods" (2023), and "Smart Eye: Airborne and AI-Driven Assessment Solution of Sugarcane" (2022). She actively supervises HDR students and has completed multiple successful candidatures. Her leadership extends beyond research and teaching, as evidenced by her role as Associate Dean (Education) for the Faculty of Engineering and Information Sciences. She is also actively involved in community engagement through volunteering with the State Emergency Service (Wollongong SES) and Athletics Wollongong Club.

Ferit Öztürkمشاهده پروفایل

استاد

• Singularity Theory
• Contact Topology
• Symplectic Topology
• +۱ مورد دیگر

Ferit Öztürk is a Professor in the Mathematics Department at Bogazici University, specializing in singularity theory, contact topology, and symplectic topology. He leads the Geometry/Topology Seminar and supervises numerous graduate students in these fields. His research bridges topological methods with applications in robotics and dynamical systems. Recent work includes exotic manifold construction, real contact structures on 3-manifolds, and Morse-Bott theory on Lie groups. Dr. Öztürk holds a Ph.D. from Middle East Technical University (2001) and has developed course materials on Morse Theory and Projective Geometry. Student Supervision: 12+ M.S./Ph.D. students in topology and geometry Research Tools: Contact branched covers, real algebraic models, geometric surgery

Alain Trouvéمشاهده پروفایل

استاد

• Shape Spaces and Applications
• Imaging Processing and Computational Vision
• Computational Biology
• +۳ مورد دیگر

Alain Trouvé is a Professor at the Center for Mathematics and Their Applications (CMLA) within the Ecole Normale Supérieure de Cachan , France. His research focuses on Shape Spaces , Computational Anatomy , Imaging Processing , and Biological Imaging , with applications in medical and computational fields. He directs the Mathematics Department at ENS Cachan and contributes to neuroanatomical studies through diffeomorphometry techniques. Key roles include membership in the Conseil National des Universités (Section 26) and teaching responsibilities such as courses on Geometry and Shape Spaces and Probability Theory . His work spans from theoretical frameworks (e.g., Hamiltonian modeling of shape evolution) to practical applications like 3D cell imaging and white matter fiber analysis. Publications emphasize interdisciplinary methods, including diffeomorphic registration, varifold-based image analysis, and stochastic shape evolutions. Current projects explore multi-scale modeling of biological systems and AI-driven medical diagnostics. Key Research Themes: Diffeomorphic mappings, computational vision, and functional shape analysis. Teaching: Courses on geometric modeling and probability at undergraduate and graduate levels. Tools Developed: xIV-LDDMM Toolkit for multi-modal biomedical data analysis.

• Compliant Mechanisms
• Robotics
• Precision Manufacturing
• +۵ مورد دیگر

Dr. Guangbo Hao is a Professor of Mechanical Engineering at University College Cork (UCC), where he leads the UCC CoMAR research group and directs the Mechatronics/Robotics Lab. He holds dual PhDs from Northeastern University (China, 2008) and Heriot-Watt University (UK, 2011). His research focuses on compliant mechanisms, robotics, and their applications in precision manufacturing, energy harvesting, and medical devices. He has secured over €1.3M in research funding, including grants from SFI, EU Horizon 2020, and Enterprise Ireland. He is an ASME Fellow and has received multiple accolades, including the ASME Compliant Mechanisms Award (2017, 2018, 2022) and the UCC President’s Excellence in Teaching Award (2023). Education: BSc (2004), MSc (2007), PhD (2008) from Northeastern University; second PhD (2011) from Heriot-Watt University Research interests include compliant mechanisms, mechatronics, and robotics with emphasis on deployable structures, medical devices, and energy harvesting systems. His work has resulted in over 200 peer-reviewed publications and 30 invited talks. He supervises students in PhD, research-master, and taught-master programs, with notable student achievements in international competitions and awards. Professional roles include Associate Editorships at Advanced Equipment , ASME Journal of Mechanisms and Robotics , and IEEE Robotics and Automation Letters . He chairs major conferences like ASME IDETC/CIE 2025 and co-chairs the 2026 IEEE/ASME AIM conference. His labs, including the UCC Engineering Maker Lab, focus on innovation in robotics and mechatronics.

David Toalمشاهده پروفایل

دانشیار

• Design optimisation & automation
• Machine learning & deep learning
• Computer aided design
• +۳ مورد دیگر

Dr. David Toal is an Associate Professor at the University of Southampton, specializing in the application of machine learning techniques to aerospace system design optimization. His research focuses on automated geometry creation, prediction of simulation outputs, and fundamental machine learning advancements. He is affiliated with the Computational Engineering and Design Group and the Institute for Life Sciences. His teaching interests include engineering design methods, optimization, reliability, and CAD integration. He supervises multiple PhD students in areas such as aerodynamic geometry generation and structural design automation. Dr. Toal has led projects funded by the European Union and EPSRC, including E-Break (FP7) and equipment grants for advanced computational tools. His work emphasizes multidisciplinary collaboration, leveraging CAD systems and deep learning for applications in advanced aerial mobility and turbine optimization. Recent publications highlight advancements in Kriging models, adversarial auto-encoders, and semantic segmentation for engineering design. Dr. Toal's research bridges computational methods with practical aerospace challenges, aiming to accelerate design processes through data-driven and AI-enhanced approaches.

Chi Zhouمشاهده پروفایل

دانشیار

• Additive Manufacturing
• Rapid Prototyping
• 3D Printing Technologies
• +۴ مورد دیگر

Chi Zhou is an Associate Professor and Director of Graduate Studies in the Department of Industrial and Systems Engineering at the University at Buffalo. He also holds an adjunct appointment as Adjunct Associate Professor in the Department of Computer Science and Engineering. His research focuses on additive manufacturing, rapid prototyping, and advanced material systems. Zhou has a PhD in Industrial and Systems Engineering (2011) from the University of Southern California, with additional degrees in Computer Science and Industrial Engineering. His work bridges manufacturing processes, material science, and computational methods. Key research areas include inkjet printing process optimization, hydrogel-based 3D printing, thermal insulation materials from agricultural byproducts, and smart material systems like magnetorheological metamaterials. He has pioneered methods for real-time process monitoring and defect detection in additive manufacturing. Zhou’s recent publications emphasize sustainable manufacturing solutions, such as bio-based insulation materials and cost-effective silica aerogel production. His contributions also span energy harvesting (e.g., conductive hydrogel generators) and advanced structural designs using triply periodic minimal structures. He has led interdisciplinary projects integrating digital twins for cyber manufacturing systems and geometric deep learning for mass customization applications.

Michael Bronsteinمشاهده پروفایل

استاد

• Geometric Deep Learning
• Graph Representation Learning
• Computer Vision
• +۴ مورد دیگر

Affiliations and Roles Michael Bronstein is a Professor at the Università della Svizzera italiana (USI) in the Faculty of Informatics and the Institute of Computational Science . He holds the Chair in Machine Learning and Pattern Recognition at Imperial College London and serves as Head of Graph Learning Research at Twitter . Previously, he was affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) as an Adjunct Professor. Education Ph.D. in Computer Science, Technion–Israel Institute of Technology (2007) Visiting appointments at Stanford University, MIT, Harvard University, and Tel Aviv University Research Interests Bronstein's work focuses on geometric deep learning , graph representation learning , and non-rigid shape analysis . He pioneered methods for extending machine learning to non-Euclidean domains like graphs and manifolds. His research combines theoretical advancements in spectral geometry with practical applications in computer vision, robotics, and medical imaging. Publications Trends His articles emphasize geometric deep learning frameworks, functional maps for shape correspondence, and spectral methods for manifold analysis. Key themes include invariant representations, partial shape matching, and applications in 3D reconstruction and graph neural networks. Awards and Honors Five ERC Grants Royal Society Wolfson Research Merit Award IEEE and IAPR Fellowships World Economic Forum Young Scientist Advising and Entrepreneurship Bronstein is a serial entrepreneur, founding companies like Novafora , Invision (acquired by Intel), and Fabula AI (acquired by Twitter). His academic advising spans PhD and Master’s students in machine learning and geometry processing. Labs and Teams Active in the Institute of Computational Science at USI and leads Twitter’s Graph Learning Research team, focusing on real-world applications of geometric deep learning.