Workshop for Women in Shape Analysis

这笔赠款支持美国妇女前往研讨会的妇女在形状分析（WiShT），将在Nesin数学村在伊斯坦布尔，土耳其，在一周6月5日至12日，2016年。 该研讨会旨在通过汇集来自世界各地的处于职业生涯各个阶段（从研究生到高级研究员）的女性研究人员来加强形状建模社区，以促进研究合作和指导。由于形状建模本质上是跨学科的，涉及数学、计算机科学和生物技术，因此合作尤为重要。此外，由于没有一个国家有很多女性从事形体建模工作，国际合作对于形成强大的女性研究人员网络至关重要。在土耳其主办讲习班反映了土耳其有意识地努力使中东和非洲妇女更容易参加讲习班。 参与者将花一个星期的时间在小组中一起工作，以解决形状建模中的一个开放性问题。WiShT的参与者将开始与其他参与者合作产生新的结果，而不是参与者观看既定结果演示的更典型的研讨会结构。在讲习班之后，将通过出版会议记录、建立网站和邮件列表服务器以及组织后续会议和与会者聚会，维持和加强研究网络。指导和专业发展将以正式和非正式的方式进行。有关研讨会的更多信息，请参阅http://nesinkoyleri.org/eng/events/2016-wis2/index.phpShape分析是许多应用领域的关键组成部分，包括图像识别，医学成像，生物医学工程，图形学和计算机动画。该研讨会将解决形状建模和分析中三个非常不同的挑战性问题，由经验丰富的研究人员领导，广泛的参与者将使跨学科的学者团队在每个领域取得实质性进展。第一组将探索形状复杂性的许多可能的定义，并将研究它们之间的关系，希望得到一个总体的形状复杂性的数学理论，并了解何时每个复杂性的概念是最合适的。第二组将专注于基于形状的分割模型的组合优化，包括离散和连续。 每种模式都提出了挑战：连续模型可能不会收敛到最优解，或者可能慢得多，而在离散设置中，整合形状信息或以特定预定义先验为目标的结构是相当具有挑战性的。第2组将通过探索变分公式到离散域的映射来结合联合收割机这些方法，并解决与优化所得离散模型相关的挑战。第3组将专注于二维和三维的卷积骨架。我们的目标是开发一种方法，将输入形状和相关的卷积骨架转换为一个新的骨架，该骨架是鲁棒的，紧凑的，可重复的，可以平滑地参数化原始形状。每个小组的结果将在AWM-Springer卷中发表，并通过会议和期刊出版物传播。

This grant supports travel for US women to the Workshop for Women in Shape Analysis (WiShT), to be held at the Nesin Mathematics Village in Istanbul, Turkey, in the week June 5-12, 2016. The workshop is designed to strengthen the shape modeling community by bringing together women researchers at various stages in their careers (from graduate student to senior researcher) and from across the world, to foster research collaboration and mentorship. Because shape modeling is interdisciplinary in nature, drawing from mathematics, computer science, and biotechnology, collaborations are especially crucial. Furthermore, since no single country has many women working in shape modeling, international collaborations are essential to forming robust networks of women researchers. Hosting the workshop in Turkey reflects a conscious effort to make the workshop more accessible for women from the Middle East and Africa. Participants will spend one week working together in small groups to solve one of a selection of open questions in shape modeling. Instead of the more typical workshop structure where participants watch presentations of established results, WiShT participants will begin generating new results in collaboration with other participants. Following the workshop, the research network will be maintained and strengthened by publishing a proceedings volume, establishing a website and listserv, and organizing follow-up conferences and reunions for participants. Mentoring and professional development will happen both formally and informally. For more information about the workshop see http://nesinkoyleri.org/eng/events/2016-wis2/index.phpShape analysis is a critical component in many applications areas, including image recognition, medical imaging, biomedical engineering, graphics, and computer animation. The workshop will tackle three very different challenging problems in shape modeling and analysis in groups led by experienced researchers, and the broad range of participants will allow an interdisciplinary team of scholars to make substantial progress in each area. Group 1 will explore the many possible definitions of shape complexity and will study the relationships between them in the hope of deriving an overarching mathematical theory of shape complexity and an understanding of when each notion of complexity is most appropriate. The second group will focus on combinatorial optimization for shape-based segmentation models, both discrete and continuous. Each model presents challenges: continuous models may not converge to optimal solutions or may be much slower, while in the discrete setting, integrating shape information or targeting structures with a particular predefined prior is quite challenging. Group 2 will combine these approaches by exploring the mapping of variational formulations to the discrete domain and tackle the challenges associated with optimizing the resultant discrete models. Group 3 will focus on convolution skeletons in both two and three-dimensions. The goal is to develop a method to transform an input shape and associated convolution skeleton into a new skeleton that is robust, compact, and reproducible that gives a smooth parameterization of the original shape. Results from each group will be published in an AWM-Springer volume, as well as disseminated via conference and journal publications.