CBMS Conference: Topological Methods in Machine Learning and Artificial Intelligence

这一国家科学基金会奖支持2019年5月13日至17日在南卡罗来纳州查尔斯顿的查尔斯顿学院主办的NSF-CBMS关于机器学习和人工智能中的拓扑方法的区域会议。会议将由斯坦福大学的Gunnar Carlsson教授和Ayasdi Inc.担任首席讲师。Carlsson教授将发表一系列十场讲座，向与会者介绍快速崛起的拓扑数据分析领域，该领域使用拓扑学中常用的许多技术，即形状研究，来分析跨多个应用领域的海量和复杂的数据集。这次会议将使更多的与会者受益，因为数据科学正在迅速成为一门拥有许多高影响力应用的跨学科学科。该系列讲座和随后的专著的主要目标将是医学科学的应用，包括例如更好地针对和预测疾病以及改善患者护理，尽管这些讲座将使更多的人受益。NSF资助的绝大多数参与者将从早期职业研究人员、研究生、少数族裔和女性中招募。拓扑数据分析是指将拓扑用作理解大型复杂数据集并与之交互的工具。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--包括将空间作为单纯复形的组合构造、同调和上同调、局部到全局的计算和应用方法，以及范畴理论和函数论语言的组织能力--都在这门学科的发展中发挥了重要作用。卡尔松-S教授的系列讲座和随之而来的专著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；同源性在塑造分析任务、图像补丁的统计和病毒进化方面的应用；从拓扑到点云情况的局部到全局方法的适应；持久性景观和持久性图像及其在药物发现中的应用；集群；以及作为数据源的算法。会议网站位于http://math.cofc.edu/CBMS-TDA2019/This，该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势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.