Conference: Machine Learning in Science and Engineering

会议：科学与工程中的机器学习

• 批准号: 1822279
• 负责人: Dana Randall
• 金额: $ 3万
• 依托单位: Georgia Tech Research Corporation
• 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: 2018-06-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822279&HistoricalAwards=false
• 关键词: Conference; Machine; Learning; Science; Engineering

This award supports the first annual Symposium on Machine Learning in Science and Engineering (MLSE), held in Pittsburg, Pennsylvania, June 6-8, 2018. The meeting, initially organized by Carnegie Mellon University and Georgia Tech, is the first comprehensive and open annual conference bringing together leading researchers in science and engineering whose work benefits from advances in machine learning and data science. While machine learning has revolutionized many areas of biological and biomedical research, its impact across the sciences and engineering is at an early stage. This symposium will bring together researchers in a diversity of Science, Technology, Engineering, and Math (STEM) areas focused on applying machine learning to problems of fundamental or applied nature. Presentations will focus on adapting existing machine learning methods to current research areas, developing new machine learning algorithms specific to science and engineering, and identifying new frontiers of research that may only be pursued using a data-driven approach. The symposium will offer attendees focused short courses taught by experts in machine learning on a variety of cutting-edge tools that are critical in advancing these fields. The MLSE symposium will help catalyze machine learning methodologies and collaborations across the sciences and engineering, bringing together researchers in a diversity of STEM areas focused on applying machine learning to fundamental and applied problems. Presentations will focus on adapting existing machine learning methods to current research areas, developing new machine learning algorithms specific to science and engineering, and identifying new frontiers of research that may only be pursued using a data-driven approach.The symposium is anticipated to reach, in its first year, at least 400 direct participants and attendees, including under-represented minorities, those attending Minority Serving Institutions (MSIs), and low income students local to the conference venues, or selected to travel to the event based on merit and need. Several groups within the research community are involved, including a diverse group of students, early career researchers and faculty. Information will be widely disseminated on a continuing basis through news items published via community-specific and broad news release venues. Partial support is being provided primarily to enable participation by students and young researchers, in addition to a limited number of tutorial and plenary speakers. The organizers are committed to promoting participation among underrepresented groups, junior researchers and students, and including tutorials to widen accessibility to as large a group of attendees, as possible. The organizers will have an open competition for these travel awards, selected by a diverse committee, and the opportunity to apply will be widely disseminated across the relevant disciplines.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.

该奖项支持于2018年6月6-8日在宾夕法尼亚州匹兹堡举行的首届科学与工程领域机器学习年度研讨会。这次会议最初由卡内基梅隆大学和佐治亚理工学院组织，是第一次全面和开放的年度会议，聚集了科学和工程领域的领先研究人员，他们的工作受益于机器学习和数据科学的进步。虽然机器学习已经给生物和生物医学研究的许多领域带来了革命性的变化，但它对科学和工程的影响还处于早期阶段。这次研讨会将汇集科学、技术、工程和数学(STEM)领域的研究人员，专注于将机器学习应用于基础或应用性质的问题。演讲将侧重于使现有的机器学习方法适应当前的研究领域，开发专门针对科学和工程的新的机器学习算法，并确定只能使用数据驱动方法进行的研究的新前沿。研讨会将为与会者提供由机器学习专家讲授的关于各种尖端工具的重点短期课程，这些工具对推动这些领域的发展至关重要。MLSE研讨会将有助于促进机器学习方法和跨科学和工程领域的合作，将各种STEM领域的研究人员聚集在一起，专注于将机器学习应用于基本和应用问题。演讲将集中于使现有的机器学习方法适应当前的研究领域，开发专门针对科学和工程的新的机器学习算法，并确定只能使用数据驱动的方法进行研究的新前沿。研讨会预计在第一年至少有400名直接参与者和与会者，包括代表人数不足的少数族裔、参加少数族裔服务机构(MSI)的人和会议场馆当地的低收入学生，或根据优点和需求被选为前往活动的人。研究社区中的几个团体参与其中，包括不同的学生、早期职业研究人员和教职员工。信息将继续通过社区特定和广泛的新闻发布场所发布的新闻项目广泛传播。提供部分支助主要是为了使学生和青年研究人员能够参与，此外还有数量有限的辅导员和全体演讲者。组织者致力于促进代表性不足的群体、初级研究人员和学生的参与，并包括教程，以扩大尽可能多的与会者的可及性。组织者将对这些旅行奖项进行公开竞争，由一个多元化的委员会选出，申请的机会将在相关学科中广泛传播。该奖项反映了NSF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。