CBMS Conference: Topological Methods in Machine Learning and Artificial Intelligence

该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，该会议于2019年5月13日至17日由南卡罗来纳州查尔斯顿的查尔斯顿学院主办。会议将邀请斯坦福大学和Ayasdi公司的Gunnar Carlsson教授担任首席讲师。卡尔森教授将提供一系列的十场讲座，向参与者介绍拓扑数据分析这一快速发展的领域，该领域采用了拓扑中常用的许多技术，形状研究，分析跨多个应用领域的大量复杂数据集。由于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，因此会议将使广泛的参与者受益。系列讲座和随后的专著的主要目标将是医学科学的应用，包括，例如，更好地定位和预测疾病和改善病人护理，尽管讲座将使更多的人受益。绝大多数nsf支持的参与者将从早期职业研究人员、研究生、少数民族和女性中招募。拓扑数据分析是指使用拓扑作为理解大型复杂数据集和与之交互的工具。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——包括简单复形空间的组合构造、同调和上同调、局部到全局的计算和应用方法，以及范畴论和泛函语言的组织能力——都在这一学科的发展中发挥了重要作用。•教授?S系列讲座和由此产生的专著将向学生和研究人员介绍这一迅速兴起的领域。主题将包括数据的拓扑建模；机器学习;同源性在形状分析任务、图像补丁统计和病毒进化中的应用；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在药物发现中的应用聚类;算法作为数据源。该会议的网站是http://math.cofc.edu/CBMS-TDA2019/This。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.