Conference on Statistical Learning and Data Science

统计学习与数据科学会议

• 批准号: 1818546
• 负责人: Annie Qu
• 金额: $ 1.5万
• 依托单位: University of Illinois at Urbana-Champaign
• 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: 2018-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1818546&HistoricalAwards=false
• 关键词: Conference; Statistical; Learning; Data; Science

Columbia University will host a three-day conference on Statistical Learning and Data Science/Nonparametric Statistics, June 4-6, 2018 in New York City. The objective of the conference is to bring together researchers in statistical learning, data science and nonparametric statistics from academia, industry, and government. Statistical machine learning is widely recognized as a very active area of interdisciplinary research, closely related to statistics, optimization, and computer science. In addition, it also plays an essential role in the new important areas of data science and big data. Due to advances in technology, massive and complex data in the "big data era" are prevalent in almost every aspect of modern scientific research. It is critical to manage such huge amounts of data, and make reliable prediction and inference. Statistical machine learning techniques have developed substantial flexibility in handling such data, with a wide range of applications in diverse scientific disciplines. This conference is expected to (1) bring together researchers from different disciplines, including statistics, computer science, machine learning, engineering, and biomedical and other related research fields, to address recent development and emerging issues in statistical learning, data science and nonparametric statistics, (2) promote interactions and collaborations among researchers, (3) discuss new ideas and future research directions for statistical learning and data science, with a focus towards knowledge discovery in sciences and engineering, (4) provide an excellent opportunity for junior researchers to interact and learn from leading scientists in the field. The conference will consist of three plenary sessions, 55 invited sessions and poster sessions. Conference topics include unsupervised, semi-supervised and supervised learning, with applications in rankings, text and web mining, network analysis, bioinformatics, high-dimensional data, functional data, genomics, drug discovery, intrusion and fraud detection. NSF funding will provide travel support to students, post-doctoral scholars, and early-career researchers to encourage their participation in this event. The conference website is https://publish.illinois.edu/sldsc2018/.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.

哥伦比亚大学将于2018年6月4日至6日在纽约市举办为期三天的统计学习和数据科学/非参数统计会议。会议的目的是汇集来自学术界、工业界和政府的统计学习、数据科学和非参数统计学研究人员。统计机器学习被广泛认为是一个非常活跃的跨学科研究领域，与统计学、优化和计算机科学密切相关。此外，它还在数据科学和大数据等新的重要领域发挥着至关重要的作用。由于技术的进步，“大数据时代”的海量复杂数据几乎普遍存在于现代科学研究的各个方面。管理如此大量的数据，并做出可靠的预测和推理至关重要。统计机器学习技术在处理这些数据方面具有很大的灵活性，在不同的科学学科中具有广泛的应用。本次会议预计将（1）汇集来自不同学科的研究人员，包括统计学，计算机科学，机器学习，工程学，生物医学和其他相关研究领域，以解决统计学习，数据科学和非参数统计的最新发展和新出现的问题，（2）促进研究人员之间的互动和合作，（3）讨论统计学习和数据科学的新思想和未来研究方向，重点是科学和工程中的知识发现，（4）为初级研究人员提供与该领域领先科学家互动和学习的绝佳机会。会议将包括三个全体会议，55个邀请会议和海报会议。会议主题包括无监督，半监督和监督学习，应用于排名，文本和Web挖掘，网络分析，生物信息学，高维数据，功能数据，基因组学，药物发现，入侵和欺诈检测。NSF资助将为学生，博士后学者和早期职业研究人员提供旅行支持，以鼓励他们参与这项活动。会议网站是https://publish.illinois.edu/sldsc2018/.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。