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

该奖项为将于 2017 年 5 月 12 日至 13 日在加州大学戴维斯分校举行的“2017 年湾区优化会议：从数据到决策”会议提供支持。该会议是自 2014 年以来举办的（旧金山）湾区优化会议系列区域会议中的第三次活动。2017 年会议计划作为全国性会议，并有一些国际参与。决策者和政策分析师面临着越来越多的不确定性，但数据和信息的可用性也越来越高。这次会议汇集了来自不同学科的研究人员，他们致力于研究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的数学方法。 这次会议参与者的多样性将导致数学思想向广泛的研究人员传播，否则他们可能不会意识到最先进的理论和方法。 参与者包括致力于发展该学科基础知识的理论和计算数学家，以及能源、国防、政策规划和交通等各个领域的决策专家，新方法将在这些领域产生影响。此次会议将有 15 位随机和变分分析领域的世界专家参加。会议将有大量学生、博士后研究员和初级教师参加，他们将参加海报会议并与演讲者进行技术讨论。会议的主题在于决策的均衡和优化模型与学习、预测和不确定性量化的统计模型的交叉点。有关及时问题的讲座和论文将通过在《数学规划》期刊特刊上发表，向数学科学、管理科学和工程界提供。录制的讲座将通过会议网站提供。更多信息，包括演讲者和摘要列表以及注册信息，请访问 https://www.math.ucdavis.edu/bayopt2017