CBMS Conference: Topological Methods in Machine Learning and Artificial Intelligence

CBMS 会议：机器学习和人工智能中的拓扑方法

• 批准号: 1836362
• 负责人: Ben Cox
• 金额: $ 3.6万
• 依托单位: College of Charleston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1836362&HistoricalAwards=false
• 关键词: CBMS; Conference; Topological; Methods; Machine

This National Science Foundation award supports the NSF-CBMS regional conference on Topological Methods in Machine Learning and Artificial Intelligence, hosted by the College of Charleston in Charleston, South Carolina, during the week of May 13-17, 2019. The conference will feature Professor Gunnar Carlsson of Stanford University and Ayasdi Inc. as the Principal Lecturer. Professor Carlsson will deliver a series of ten lectures introducing participants to the fast-emerging field of Topological Data Analysis, which employs many of the techniques commonly used in topology, the study of shape, to analyze massive and complex data sets across multiple application domains. The conference will benefit a broad group of participants as data science is rapidly establishing itself as an interdisciplinary discipline with many high-impact applications. Main targets of the lecture series and the ensuing monograph will be applications to the medical sciences, including, e.g., better targeting and prediction of diseases and improved patient care, though the lectures will benefit a far larger constituency. The great majority of the NSF-supported participants will be recruited from amongst early career researchers, graduate students, minorities, and women. Topological Data Analysis refers to the use of topology as a tool for understanding and interacting with large and complex data sets. It should be viewed as another step in the development of Machine Learning. Many of the techniques used extensively in topology - including the combinatorial construction of spaces as simplicial complexes, homology and cohomology, local to global methods for computation and application, and the organizing power of the language of category theory and functoriality - all play important roles in the development of this subject. Professor Carlsson?s lecture series and the resulting monograph will introduce students and researchers to this rapidly emerging field. Topics will include topological modeling of data; machine learning; applications of homology to shape analytic tasks, statistics of image patches, and viral evolution; adapting local-to-global methods from topology to point cloud situations; persistence landscapes and persistence images with applications to drug discovery; clustering; and algorithms as data sources. The conference website is at http://math.cofc.edu/CBMS-TDA2019/This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个国家科学基金会奖支持NSF-CBMS机器学习和人工智能拓扑方法区域会议，由南卡罗来纳州查尔斯顿的查尔斯顿学院在2019年5月13日至17日的一周期间主办。会议将邀请斯坦福大学教授贡纳尔·卡尔森和Ayasdi公司。作为首席讲师。Carlsson教授将提供一系列的十个讲座，向与会者介绍拓扑数据分析的快速新兴领域，它采用了许多拓扑学中常用的技术，形状的研究，分析跨多个应用领域的大量和复杂的数据集。会议将使广泛的参与者受益，因为数据科学正在迅速成为一门具有许多高影响力应用的跨学科学科。讲座系列和随后的专题论文的主要目标将是应用到医学科学，包括，例如，更好地瞄准和预测疾病，改善病人护理，尽管这些讲座将使更多的选民受益。绝大多数NSF支持的参与者将从早期职业研究人员，研究生，少数民族和妇女中招募。 拓扑数据分析是指使用拓扑作为理解和与大型复杂数据集交互的工具。它应该被视为机器学习发展的又一步。拓扑学中广泛使用的许多技术--包括空间的组合构造为单纯复形、同调和上同调、局部到全局的计算和应用方法，以及范畴论和函性语言的组织能力--都在这门学科的发展中发挥着重要作用。卡尔森教授？的系列讲座和专题论文将向学生和研究人员介绍这一迅速崛起的领域。主题将包括数据的拓扑建模;机器学习;同源性的应用，以塑造分析任务，图像补丁的统计，和病毒进化;适应局部到全局的方法，从拓扑到点云的情况;持久性景观和持久性图像与应用程序，以药物发现;聚类;和算法作为数据源。会议网站是http://math.cofc.edu/CBMS-TDA2019/This奖反映了NSF的法定使命，并已被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。