dr leonidas guibas

When cutting through the material, crack fronts directly affect the coupling between simulation nodes, requiring a dynamic adaptation of the nodal shape functions. Action Classification in Still Images. The company’s technical advisory board includes former Google, Uber, and Apple visionaries Brian McClendon and Jaron Waldman, as well as Dr. Leonidas Guibas, a prominent Stanford University professor, and Herman Kaess, former CEO of Bosch Korea. We demonstrate our method for a wide range of materials, from stiff elastic to highly plastic objects that exhibit brittle and/or ductile fracture.


Most frequent co-Author Most cited colleague Top subject. This gives a one-dimensional representation of native protein three-dimensional structure whose quality depends on the nature of the library. Aishima, J., Russel, D. S., Guibas, L. J., Adams, P. D., Brunger, A. T. Pauly, M., Keiser, R., Adams, B., Dutre, P., Gross, M., Guibas, L. J. Inverse kinematics in biology: The protein loop closure problem. Data Stashing: Energy-Efficient Information Delivery to Mobile Sinks through Trajectory Prediction. The methodology described in this paper admits various implementations or extensions to incorporate more information and adapt to different settings, which thus provides a systematic tool to explore the low-density intermediate states in complex biomolecular folding systems. Complex fracture patterns of interacting and branching cracks are handled using a small set of topological operations for splitting, merging, and terminating crack fronts. Lee, H., Wicke, M., Kusy, B., Gnawali, O., Guibas, L. Persistence-based Segmentation of Deformable Shapes, 3rd Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment. Ground-breaking research shaped the Computational Geometry field. View details for DOI 10.1016/j.comgeo.2006.11.006, View details for Web of Science ID 000247580500007, View details for PubMedCentralID PMC3001684, View details for DOI 10.1007/s00453-007-0151-y, View details for Web of Science ID 000248325000009. We predict that a tightly coordinated process of hemifusion neck expansion and pore formation is responsible for the rapid vesicle fusion mechanism, while isolated enlargement of the hemifusion diaphragm leads to the formation of a metastable hemifused intermediate. Landmark selection and greedy landmark-descent routing for sensor networks. APPLIED REGRESSION ANALYSIS AND GENERALIZED LINEAR MODELS fox 2008. Leonidas Guibas is the Paul Pigott Professor of Computer Science (and by courtesy, Electrical Engineering) at Stanford University, where he heads the Geometric Computation group. In May 2020, DeepMap was named a “Cool Vendor in Autonomous Systems" by Gartner. One of the first applications of the medial axis to X-ray crystallography is presented here. RoamHBA: Maintaining group connectivity in sensor networks, A probabilistic approach to inference with limited information in sensor networks, Sensor Tasking for Occupancy Reasoning in a Camera Network. Ovsjanikov, M., Ben-Chen, M., Solomon, J., Butscher, A., Guibas, L. Detecting Network Cliques with Radon Basis Pursuit. Professor Guibas heads the Geometric Computation group in the Computer Science Department of Stanford University and is a member of the Computer Graphics and Artificial Intelligence Laboratories. Kim, Y. M., Mitra, N. J., Yan, D., Guibas, L. Microtiles: Extracting Building Blocks from Correspondences. We also find that folding is not simply the reverse of high-temperature unfolding and suggest that this may be a general feature of biomolecular folding. Characterization of transient intermediate or transition states is crucial for the description of biomolecular folding pathways, which is, however, difficult in both experiments and computer simulations. Efrat, A., Guibas, L. J., Hall-Holt, O. Huang, X., Yao, Y., Bowman, Gregory, R., Sun, J., Guibas, Leonidas, J., Carlsson, G. Image Webs: Computing and Exploiting Connectivity in Image Collections. A Computational Framework for Handling Motion. Gu, C., Chang, H., Maibaum, L., Pande, V. S., Carlsson, G. E., Guibas, L. J. Hairpins are a ubiquitous secondary structure motif in RNA molecules. These visualizations allow one to easily detect formation of secondary and tertiary structures as the protein folds. The method is inspired by the classical Morse theory in mathematics which characterizes the topology of high-dimensional shapes via some functional level sets. Scrum Product Owner. Leonidas John Guibas (Greek: Λεωνίδας Γκίμπας) is the Paul Pigott Professor of Computer Science and Electrical Engineering at Stanford University, where he heads the geometric computation group and is a member of the computer graphics and artificial intelligence laboratories. View details for Web of Science ID 000265617200017, View details for PubMedCentralID PMC2719471, View details for DOI 10.1145/1516522.1516526, View details for Web of Science ID 000266818600004, View details for Web of Science ID 000275366201065, View details for Web of Science ID 000275711800010, View details for Web of Science ID 000275366201006, View details for DOI 10.1111/j.1467-8659.2009.01380.x, View details for Web of Science ID 000264544700026. Professor Guibas' interests span computatio Leonidas Guibas is the Paul Pigott Professor of Computer Science (and by courtesy, Electrical Engineering) at Stanford University, where he heads the Geometric Computation group. Automatic fitting methods that build molecules into electron-density maps usually fail below 3.5 A resolution. Tang, H., Kerber, M., Huang, Q., Guibas, L. Large-Scale Joint Map Matching of GPS Traces. He has been at Stanford since 1984 as Professor of Computer Science. A successful application of this method is given on a motivating example, a RNA hairpin with GCAA tetraloop, where we are able to provide structural evidence from computer simulations on the multiple intermediate states and exhibit different pictures about unfolding and refolding pathways. Leonidas J. Guibas, Department of Computer Science, Stanford University, Professor Guibas heads the Geometric Computation group in the Computer Science Department of Stanford University and is a member of the Computer Graphics and Artificial Intelligence Laboratories. Fast and free shipping free returns cash on delivery available on eligible purchase. Chen, C., Su, H., Huang, Q., Zhang, L., Guibas, L. Guided Real-Time Scanning of Indoor Environments, Computer Graphics Forum. Stanford University. Guided Real-Time Scanning of Indoor Environments, Graph Matching with Anchor Nodes: A Learning Approach, Acquiring 3D Indoor Environments with Variability and Repetition. Aanjaneya, M., Chazal, F., Chen, D., Glisse, M., Guibas, L., Morozov, D. Fourier-Information Duality in the Identity Management Problem, Network Warehouses: Efficient Information Distribution to Mobile Users. 3D reconstruction. My research interests are broadly in computer vision, machine learning, and AI. Li, Y., Huang, Q., Kerber, M., Zhang, L., Guibas, L. Image Co-Segmentation via Consistent Functional Maps. View details for Web of Science ID 000230169100004, View details for Web of Science ID 000231441000031, View details for Web of Science ID 000231323800048, View details for Web of Science ID 000230452800023, View details for DOI 10.1109/JSAC.2004.837364, View details for Web of Science ID 000226063100016, View details for Web of Science ID 000230375000004, View details for DOI 10.1016/j.cag.2004.08.015, View details for Web of Science ID 000225055200008, View details for Web of Science ID 000225807500008, View details for Web of Science ID 000224315900003, View details for DOI 10.1016/j.comgeo.2004.03.008, View details for Web of Science ID 000221974900005, View details for Web of Science ID 000222055900036, View details for Web of Science ID 000223848100226, View details for Web of Science ID 000222055900018, View details for Web of Science ID 000222055900031, View details for DOI 10.1109/JPROC.2003.814921, View details for Web of Science ID 000184655000006, View details for DOI 10.1007/s00454-003-2925-6, View details for Web of Science ID 000183405700005, View details for Web of Science ID 000180994200102, View details for Web of Science ID 000189435400008, View details for Web of Science ID 000186833000017.

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