جست‌وجوی استاد و پژوهشگر

Haisen Zhaoمشاهده پروفایل

استاد

• Computer Graphics
• Digital Fabrication
• Additive Manufacturing
• +۵ مورد دیگر

Haisen Zhao is a Professor at Shandong University (SDU), affiliated with the School of Computer Science and Technology and the Interdisciplinary Research Center. His research focuses on geometric processing and digital fabrication, including additive and subtractive manufacturing. He completed his PhD at SDU in 2018 under Baoquan Chen, followed by postdoctoral research at the University of Washington (with Adriana Schulz) and IST Austria (with Bernd Bickel). He has authored over 20 top-tier publications in venues like ACM SIGGRAPH/Asia and IEEE TVCG, and holds multiple patents. Education: PhD in Computer Science (2018, SDU), Master’s (2014, SDU), and Bachelor’s (2011, SDU). Key awards include the CCF Doctoral Dissertation Award (2019) and Shandong Natural Science Award (2020). He leads a research group focused on advanced manufacturing techniques, advising PhD and Master’s students in areas like robotics, computer-aided design, and fabrication optimization. Teaching responsibilities include courses on computer graphics, human-computer interaction, and advanced topics for both undergraduates and graduates. His lab actively explores hybrid manufacturing strategies, inverse design methods, and robotic fabrication systems. Recent projects include neural accessibility learning for CNC machining and generative design for microstructure fabrication.

Adrien Bousseauمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Image Processing
• Non-Photorealistic Rendering
• +۵ مورد دیگر

Adrien Bousseau is a Senior Researcher at Inria within the GraphDeco group at Université Côte d'Azur. He earned his PhD from Inria Rhône-Alpes under Joëlle Thollot and François X. Sillion, with internships at Adobe and MIT. His postdoctoral research at UC Berkeley focused on computational design and graphics. Notable roles include leading the ANR DRAO project (2012–2015) and coordinating the CRISP associate team with UC Berkeley. He has received prestigious awards such as the Eurographics 2011 PhD Award and ERC grants for projects on 3D design and circular design. Research interests span image creation, stylization, vector graphics, and sketch-based modeling. Recent work explores computational methods for circular design and AI-driven design tools. He has supervised over 13 PhD students, including Nicolas Rosset (aerodynamics) and Emilie Yu (VR sketching). Key projects include ERC-funded initiatives on drawing interpretation and material acquisition. Service contributions include co-chairing Eurographics 2025 and EGSR 2021, and editorial roles at Computer Graphics Forum. Publicly accessible tools include Photoshop watercolor filters and academic resources like BendFields tutorials.

Pascale Le Gallمشاهده پروفایل

پژوهشگر

• Computer Science
• Formal Methods
• Conformance Testing
• +۶ مورد دیگر

Pascale Le Gall is an active researcher specializing in formal methods and computer science, with primary research conducted through the Mathematics and Computer Science for Complexity and Systems laboratory. Their work spans theoretical computer science, software engineering, and interdisciplinary applications in biological systems modeling. Le Gall's research focuses on formal verification techniques, particularly in conformance testing, symbolic execution, and graph transformations. Their work bridges theoretical computer science with practical applications in distributed systems verification, geometric modeling, and biological network analysis. Key research themes include developing frameworks for stochastic process discovery, feature interaction resolution, and topological operations in geometric modeling, demonstrating both theoretical depth and practical implementation value across multiple domains. Analysis of their recent publications reveals a strong trend toward interdisciplinary applications of formal methods, particularly in biological systems. Their work increasingly integrates statistical approaches with traditional formal verification techniques, as seen in Bayesian inference for process discovery and statistical model checking of biological pathways. The research shows consistent development of symbolic execution techniques applied to increasingly complex systems, from abstract data types to distributed biological networks. Pascale Le Gall maintains active collaborations with researchers including Christophe Gaston, Marc Aiguier, and Paolo Ballarini across multiple projects. Their publication record shows consistent output with significant contributions to model-based testing frameworks, geometric modeling using graph transformations, and formal analysis of biological systems. The researcher has contributed to both theoretical foundations and practical implementations of verification techniques, with numerous conference papers and journal articles spanning over 15 years of active research.

• Differential Geometry
• Computational Geometry
• Numerical Analysis
• +۲ مورد دیگر

Erchuan Zhang is an Adjunct Research Fellow at the School of Physics, Mathematics and Computing, The University of Western Australia. His research focuses on differential geometry, computational geometry, and numerical analysis, particularly in the areas of Riemannian manifolds, geodesics, and variational curves. Dr. Zhang's work centers on geometric modeling and optimization on non-Euclidean spaces, with significant contributions to cubic curve construction via the de Casteljau algorithm on manifolds and efficient computation of multi-objective variational curves. His research bridges theoretical differential geometry with practical computational methods for solving complex geometric problems. Recent publications (2022-2025) consistently address geometric modeling on manifolds, featuring themes like geodesic convergence analysis, boundary value problem solutions for Lagrangian systems, and cubic curve generalizations. These works appear in high-impact journals including Computer Aided Geometric Design and the SIAM Journal on Numerical Analysis , reflecting strong interdisciplinary connections between pure mathematics and computational science.

Leonidas Guibasمشاهده پروفایل

استاد

• Computational Geometry
• Computer Graphics
• Robotics
• +۶ مورد دیگر

Leonidas Guibas is the Paul Pigott Professor of Engineering and Professor (by courtesy) of Electrical Engineering at Stanford University's Department of Computer Science. He leads the Geometric Computation group and is affiliated with the Computer Graphics and Artificial Intelligence Laboratories. His research focuses on algorithms for sensing, modeling, and reasoning about the physical world, with expertise in computational geometry, robotics, sensor networks, and topological data analysis. Current work includes geometric modeling with point clouds, 3D reconstruction, and mobility data analysis. He holds prestigious awards including ACM Fellow (1999), Allen Newell Award (2008), and membership in the National Academy of Sciences (2022). Education: PhD, Stanford University (1976) MS & BS, California Institute of Technology (1971) Research Interests: Geometric and topological data analysis 3D reconstruction and 3D shape analysis Sensor networks and robotics Biological structure modeling Machine learning for geometric problems Recent Articles Focus: Recent work emphasizes neural radiance fields (NeRF), dynamic Gaussian splatting, and physically plausible 3D shape generation. Publications span advancements in symmetry detection, articulated object manipulation, and multi-view video generation. Awards & Honors: Fellow, ACM (1999) Allen Newell Award (2008) Fellow, IEEE (2011) Member, National Academy of Engineering (2017) Member, National Academy of Sciences (2022) Advising & Teams: Advises doctoral and master's students on topics like geometric computing and robotics. Leads interdisciplinary teams at Stanford's ICME, HAI, and Woods Institute. Current advisees include Ian Huang, Boxiao Pan, and Colton Stearns. Labs & Collaborations: Active in the Geometric Computation group and collaborates with the Stanford AI Lab. Works on projects funded by grants in robotics, computer vision, and computational biology.

• Computer Graphics
• Human-Computer Interaction
• Geometric Modeling
• +۵ مورد دیگر

Carlo H. Séquin is a Professor of Computer Science in the EECS Department at the University of California, Berkeley. He is affiliated with the Graphics (GR) and Human-Computer Interaction (HCI) research areas, and associated with the Berkeley Center for New Media (BCNM), Berkeley Institute of Design (BID), CITRIS, and the Visual Computing Lab (VCL). His research focuses on geometric modeling, artistic geometry, mathematical visualizations, and CAD tools. Education: Ph.D. in Experimental Physics (1969), University of Basel, Switzerland. Past Roles: Head of Computer Science Division (1980–1983), co-inventor of RISC architecture with David Patterson. His work spans computer graphics, VLSI design, and the intersection of art and mathematics. He has pioneered functional optimization for surface design and contributed to CCD technology at Bell Labs. Séquin's awards include the Berkeley Citation (2016), ACM Fellow (1997), and teaching excellence awards. He advises students in Ph.D. and M.S. programs, as seen in his Ph.D. alumni list . His research is supported through CITRIS and other institutional collaborations. Séquin's artistic projects, such as mathematical sculptures and architectural designs, bridge engineering and art, exemplified by his work on the Pillar of Engineering and Twisted Tori .

• Differential Geometry
• Integral Geometry
• Riemannian Geometry
• +۳ مورد دیگر

Ana Dorotea Tarrío Tobar is a Professor in the Department of Mathematics at Universidade da Coruña (UDC) in Spain, specializing in Applied Mathematics. She is an active member of the Differential Geometry and Its Applications research group, with a strong publication record spanning over three decades. Her research focuses on Integral Geometry, Riemann Geometry, Contact Detection, and Applications of Geometry in Engineering. Key research areas include Extended Bianchi-Cartan-Vranceanu spaces, Curvature analysis, Geodesics, Contact detection, Quadrics, and graph bases. Her work bridges theoretical geometry with practical engineering applications. Dr. Tarrío Tobar teaches Mathematics I and II for the Degree in Technical Architecture, and Mathematics 1 for the Concurrent program of the Degree in Biology and Chemistry. Her recent publications (2022-2016) show a consistent output in prestigious mathematics journals, with increasing focus on computational geometry and educational applications. Her collaborative work spans multiple institutions across Spain and internationally, with frequent co-authorship with researchers like María J. Souto-Salorio, M. Brozos-Vázquez, and María J. Pereira-Sáez. While she has not directed bachelor's or master's theses since 2013, her research projects have been funded by various Spanish institutions including the Ministry of Culture, Education and University Planning, Xunta de Galicia, and the Ministry of Economy and Competitiveness.

• Computer Vision
• Deep Learning
• 3D Point Cloud Processing
• +۷ مورد دیگر

Yusuf Hüseyin Şahin is an Assistant Professor in the Department of Computer Engineering at Istanbul Technical University, Faculty of Computer and Informatics. He earned all his academic degrees—B.Sc., M.Sc., and Ph.D.—from the same institution in Computer Engineering. His research lies at the intersection of computer vision, deep learning, and 3D data processing, with applications in medical imaging, architectural heritage, and drone-based vision systems. B.Sc., M.Sc., Ph.D. in Computer Engineering, Istanbul Technical University His primary research interests include 3D point cloud processing, deep learning, image segmentation, adversarial attacks, and medical image analysis. He has published extensively on these topics, particularly focusing on point cloud registration, segmentation, and classification using neural networks. His recent work explores uncertainty modeling, active learning, and generative models for both synthetic data creation and real-world applications. The trend in his publications from 2017 to 2024 shows a clear progression from foundational work in CNN-based 3D classification and cerebral vessel analysis to advanced topics such as dynamic graph networks, conformal prediction, and heritage digitization. His work bridges theoretical machine learning with practical applications in healthcare and cultural preservation. He is currently leading a research project titled "Konformal Tahmin ile Sıcaklık Tahmin Modellerinde Doğruluğun Arttırılması" (Improving Temperature Prediction Accuracy Using Conformal Forecasting), funded under the SRP program from 2025 to 2026. This indicates an expanding interest in predictive modeling and uncertainty quantification. While no scientific awards are listed in the provided texts, his h-index of 5 and 293 citations on Scopus reflect an active and growing research profile. Dr. Şahin teaches undergraduate courses such as Data Structures (BLG 223E) and Object-Oriented Programming (BLG 252E). He has no listed advisees or thesis supervision records. He is part of a collaborative research network involving Gozde Unal and other researchers in medical and architectural computer vision. His lab activities appear to focus on deep learning for 3D data, with emphasis on robustness, efficiency, and real-world deployment.

• Geometric Deep Learning
• Machine Learning
• Data Science
• +۱۱ مورد دیگر

Michael A. Perlmutter is an Assistant Professor in the Department of Mathematics at Boise State University and an affiliate faculty member in the Computing Ph.D. program. His research bridges theoretical and applied aspects of machine learning, data science, and inverse problems, with interdisciplinary applications in biomedicine and environmental systems. Research Interests: His work primarily focuses on Geometric Deep Learning , particularly neural network models for non-Euclidean data such as graphs and manifolds. This includes theoretical development of the geometric scattering transform and designing deep learning architectures for signed and directed graphs. He also investigates Phase Retrieval in ptychographic imaging, emphasizing robust, efficient algorithms with provable guarantees. His applied interests span AI-aided drug discovery, analysis of metabolic networks, single-cell data prediction, tensor compression, audio denoising, and data science for ecological and hydrological systems. Scientific Awards: Advising and Grants: While specific students and grants are not listed, his active research program and Ph.D. affiliation suggest involvement in mentoring graduate students and pursuit of external funding, particularly in interdisciplinary data science and machine learning. His career trajectory includes multiple competitive postdoctoral appointments, indicating a strong research profile. Labs and Teams: Though not explicitly named, his work implies collaboration with interdisciplinary teams in computational mathematics, biomedical data science, and environmental modeling, possibly within or affiliated with Boise State's data science and computing initiatives.

Dmitriy Dubovitskiyمشاهده پروفایل

مدرس

• Digital Image Processing
• Machine Learning
• Embedded Control Systems
• +۱۲ مورد دیگر

Dmitriy Dubovitskiy is a Part-Time Lecturer and Honorary Research Fellow at De Montfort University, affiliated with the School of Engineering and Sustainable Development within the Faculty of Computing, Engineering and Media. His work bridges computer science and healthcare, focusing on innovative solutions for cancer diagnosis and biomedical imaging. PhD in Computer Science, De Montfort University (UK), in collaboration with Bauman Moscow State Technical University Specialization: Digital Image Processing, Object Recognition using Fractal Geometry and Fuzzy Logic Dr Dubovitskiy's research centers on the mathematical modeling of natural structures using advanced pattern analysis techniques. He applies fractal geometry , fuzzy logic , and machine learning to develop automated systems for skin and cervical cancer screening , cytopathology , and industrial quality control . His interdisciplinary approach spans biology, pharmacology, physics, and engineering. The trend in his publications shows a consistent focus on biomedical image analysis , particularly in oncology and diagnostics. His work emphasizes automated decision-making , real-time recognition , and portable or web-based screening platforms . Key themes include texture classification, convex hull algorithms, and optical machine vision, with increasing integration of AI and mobile technologies. His scientific awards reflect excellence in both research and innovation: Commercialising Award, DIT Hothouse (2011) Microsharp Limited Prize for Best Research (2003) INTAS Research Grant (06-1000013-9357) Multiple Best Presentation/Poster Awards (2001–2004) Overseas Students Research Award (2000–2003) Dr Dubovitskiy has advised on several externally funded and industrial research projects, including collaborations with Oxford University , Trinity College Dublin , and companies like Microsharp Limited and MoleTest (UK) Ltd . He secured the INTAS grant and contributed to technology commercialization efforts. He teaches Dynamics and Control and mentors through consultancy. He is an active member of the British Machine Vision Association (BMVA) , MIET , and the Cambridge Knowledge Transfer Network . He is associated with the Centre for Engineering Science and Advanced Systems (CESAS) at DMU, where he contributes to research in intelligent systems and advanced computing. His work often involves interdisciplinary teams focused on translating academic research into practical healthcare and industrial applications.

• Video Coding
• Immersive Media
• Digital Watermarking
• +۵ مورد دیگر

Glenn Van Wallendael is an Associate Professor at Ghent University's Faculty of Engineering and Architecture , affiliated with the Department of Electronics and Information Systems . He leads research in video coding, digital watermarking, and immersive media technologies. Academic Focus: Video compression standards (HEVC, H.266), AI for multimedia, virtual reality Key Collaborations: iMinds, imec, European research consortia Research Interests include: Video compression algorithms (HEVC, SVC, MV-HEVC) Digital watermarking for copyright protection Machine learning applications in image/video analysis Quality of Experience (QoE) in immersive environments Recent Publications (2024-2025) show expertise in: Deepfake detection using vision transformers Medical image landmarking tools Lightweight geometric approximation methods AI-driven video quality assessment Doctoral Mentorship includes supervising: 2021: Hannes Mareen (video forensics) 2020: Vasileios Avramelos (light field coding) 2017: Johan De Praeter (adaptive video encoding)

• Autonomous Navigation for Robots
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Jesús Tordesillas Torres is an Assistant Professor in the Department of Electronics, Automation, and Communications at the School of Engineering, Comillas Pontifical University. He joined the institution in June 2024, bringing extensive experience from postdoctoral research at MIT and ETH Zurich, and prior academic training from MIT and the Polytechnic University of Madrid. Education: PhD in Aeronautics and Astronautics, Massachusetts Institute of Technology (MIT), 2022 MS in Aeronautics and Astronautics, MIT, 2019 MS in Industrial Engineering, Polytechnic University of Madrid, 2019 BS in Industrial Engineering, Polytechnic University of Madrid, 2016 His research focuses on robotics, particularly autonomous navigation, trajectory planning, and optimization under uncertainty. He integrates deep learning and control theory to develop systems capable of safe, fast, and perception-aware navigation in dynamic and unknown environments. His work spans aerial and ground robots, multiagent systems, and challenging terrains, with strong emphasis on real-world deployment and robustness. The recent publications highlight a consistent trend in trajectory optimization, perception-aware planning, and multiagent coordination. His work bridges theoretical advances in optimization and learning with practical robotic applications, especially in safety-critical and communication-constrained scenarios. Scientific Awards: Best Paper Award, IEEE ICRA 2023 1st Place, Urban Circuit, DARPA Subterranean Challenge (2020) 2nd Place, Tunnel Circuit, DARPA Subterranean Challenge (2021) Finalist, Best Paper, IEEE IROS 2019 Jesús Tordesillas Torres has been actively involved in research grants and projects, including the ADS FERRARI WP-2 Project funded by Airbus Defence and Space (2025–2025). He mentors students and collaborates with leading institutions such as MIT, ETH Zurich, and the University of Pennsylvania. He also serves as a reviewer for top-tier journals including IEEE Transactions on Robotics , International Journal of Robotics Research , and IEEE Robotics and Automation Letters , as well as major conferences like ICRA and IROS. He leads and participates in research teams focused on autonomous systems, with affiliations to the Institute for Research in Technology (IIT) at Comillas. His invited talks at ETH Robotics Summer School and University of Pennsylvania reflect his growing influence in the robotics community.

• Visualization
• Computer Graphics
• Human-Computer Interaction
• +۲ مورد دیگر

Dr. Yingcai Xiao is an Associate Professor in the Department of Computer Science at the University of Akron's College of Engineering and Polymer Science. He joined the university in 1995 after working as a Software Scientist at Intergraph. Education: Ph.D. in Computer Science, Ph.D. in Physics, M.S. in Mathematics Dr. Xiao's research focuses on Visualization , Computer Graphics , Human-Computer Interaction , and their applications in Video Game Design and Numerical Simulation . His work bridges theoretical computing with practical solutions in education, healthcare, and engineering design. His publications from 2006–2008 highlight expertise in open-source learning platforms (e.g., Sakai), virtual surgical systems using fuzzy logic, and distributed 3D CAD systems. These contributions reflect interdisciplinary interests in educational technology, medical simulation, and internet-based systems. Scientific Awards: Co-PI on NSF Grant: Interactive Learning to Stimulate the Brain's Visual Center and to Enhance Memory Retention Dr. Xiao has served as Program Chair for the International Conference on Computer Graphics, Visualization, Computer Vision, and Image Processing (2007–2021), demonstrating sustained leadership in his field. He has also co-authored a chapter in Biologically-Inspired Computing for the Arts: Scientific Data through Graphics , expanding his impact into arts-oriented computational methods.

• Machine Learning
• Computational Modeling
• Biomedical Engineering
• +۳ مورد دیگر

Stephen Baek is a faculty member at the University of Iowa, Department of Industrial and System Engineering, with a PhD from Seoul National University's School of Mechanical and Aerospace Engineering. His research spans interdisciplinary applications of machine learning in computational modeling, biomedical engineering, and geometric data processing. Current Affiliation: University of Iowa, Department of Industrial and System Engineering PhD Institution: Seoul National University Stephen's work focuses on physics-informed machine learning , multiscale modeling , and geometric deep learning . He develops algorithms that integrate physical principles with neural networks for applications in energetic materials , human pose estimation , and medical imaging . His research also includes federated learning and interpretable AI for constraint-based synthesis and text classification. Recent publications highlight a physics-aware deep learning framework (PARCv2) for spatiotemporal dynamics, graph convolutional networks for airway mesh smoothing, and prototype trajectory methods for explainable AI. This work bridges computer science with applied physics and healthcare domains. Stephen actively collaborates across disciplines, evidenced by co-authors from institutions like Iowa, Seoul National University, and Samsung Electronics, with publications in venues such as CoRR , J. Mach. Learn. Res. , and NeurIPS . His methodological innovations address challenges in model heterogeneity , 3D surface processing , and constraint satisfaction .

Antoni Jaume-i-Capóمشاهده پروفایل

استاد

• Artificial Intelligence
• Explainable Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Dr. Antoni Jaume-i-Capó is a Full Professor in the Department of Mathematics and Computer Science at the University of the Balearic Islands (UIB) , affiliated with the Computer Graphics and Vision and Artificial Intelligence Group (UGiVIA) and serving as Director of the Artificial Intelligence Applications Laboratory (LAIA@UIB) . His work bridges Artificial Intelligence , Computer Vision , and Medical Imaging with applications in Motor Rehabilitation and Explainable AI (XAI) . Research Interests : Artificial Intelligence, Explainable AI, Computer Vision, Human-based Computation, Intelligent Systems for Motor Rehabilitation, Medical Imaging Processing. Teaching : Courses include Algorithmics and Data Structures , Final Degree Projects , and Practical Placements for Informatics Engineering degrees across multiple campuses. Scientific Contributions span 15 recent publications focusing on XAI fidelity metrics , medical image analysis , and crowdsourced diagnostics . These works integrate machine learning for Sickle Cell Disease classification and kinect-based rehabilitation systems . Scientific Awards : Premio al mejor trabajo JENUI 2010 Students : Supervised over 11 PhD/Master’s students, including Gabriel Moyà-Alcover and Pedro Marrero-Fernández. Projects : Leads EU-funded initiatives like EXPLAINME and EUGAIN , alongside regional efforts such as People4Sicklemia .