Bay Area Optimization Meeting 2017: From Data to Decisions.

2017 年湾区优化会议：从数据到决策。

• 批准号: 1643426
• 负责人: Jesus De Loera
• 金额: $ 1.5万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1643426&HistoricalAwards=false
• 关键词: Bay; Area; Optimization; Meeting; 2017

This award provides support for the conference Bay Area Optimization Meeting 2017: From Data to Decisions that will take place at the University California Davis, May 12-13, 2017. The meeting is the third event in the series of regional meetings, (San Francisco) Bay Area Optimization Meetings, held since 2014. The 2017 meeting is planned as a national meeting, with some international participation. Decision makers and policy analysts face increasing uncertainty, but also increasing availability of data and information. This conference brings together researchers from a variety of disciplines who work on mathematical methods that can be used to analyze large amounts of data and be applied to decision problems faced by government and industry. The diversity of participants of this conference will result in dissemination of mathematical ideas to a broad range of researchers that otherwise might not have been aware of state-of-the-art theories and methods. The participants include both theoretical and computational mathematicians who work on development of fundamentals of this discipline and experts in decision making in a variety of fields such as energy, defense, policy planning, and transportation, where the new methods will have impact. The meeting will feature 15 world-experts in stochastic and variational analysis. The meeting will involve a large number of students, postdoctoral fellows, and junior faculty who will participate in poster sessions and engage the speakers in technical discussions. The subject of the meeting lies at the intersection of equilibrium and optimization models for decision making and statistical models of learning, forecasting, and uncertainty quantification. The lectures and papers on timely issues will be made available to the mathematical sciences, management sciences, and engineering communities through publication in a special issue of the Mathematical Programming journal. Recorded lectures will be available through the conference website. More information, including a list of speakers and abstracts, and registration information, can be found at https://www.math.ucdavis.edu/bayopt2017

该奖项为2017年会议湾地区优化会议提供了支持：从数据到将于2017年5月12日至13日在加利福尼亚戴维斯大学举行的决策。该会议是自2014年以来举行的一系列地区会议（旧金山）湾区优化会议的第三次活动。2017年以来举行的。2017年的会议是一场国立会议，并参加了国际比赛。决策者和政策分析师面临越来越多的不确定性，但数据和信息的可用性也会增加。这次会议汇集了来自各种学科的研究人员，这些学科从事数学方法的工作，这些方法可用于分析大量数据并应用于政府和行业面临的决策问题。 这次会议的参与者的多样性将导致数学思想传播给广泛的研究人员，否则可能尚未意识到最新的理论和方法。 参与者包括理论和计算数学家，他们从事这一学科的基础知识的发展以及在各种领域的决策方面的专家，例如能源，国防，政策计划和运输，新方法将对新方法产生影响。这次会议将在随机分析和各种分析中以15个世界专家为特色。这次会议将涉及大量学生，博士后研究员和初级教师，他们将参加海报会议，并让演讲者参与技术讨论。会议的主题在于，用于决策，预测和不确定性量化的决策和统计模型的平衡和优化模型的交集。关于及时问题的讲座和论文将通过在《数学编程杂志》的特刊中发表提供数学科学，管理科学和工程社区。录制的讲座将通过会议网站提供。可以在https://www.math.ucdavis.edu/bayopt2017中找到更多信息，包括演讲者和摘要列表以及注册信息。