Convergence Accelerator Phase I (RAISE): Unpacking the Technology Career Path

融合加速器第一阶段 (RAISE)：揭开技术职业道路

• 批准号: 1936956
• 负责人: Denis Nekipelov
• 金额: $ 73.53万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936956&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Phase; RAISE; Unpacking

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact/potential benefit of this Convergence Accelerator Phase I project is to provide a tool for navigating career development in STEM fields. The proposed solution has the potential to impact US labor supply by addressing the increasing demand for technology talent, which has resulted from the rapid digitalization and automation of the labor market. It can help bridge the gender and diversity gaps in the high-tech industry by better equipping women and minority groups in developing a successful technology career path. The convergence of the project is based on the coordinated work of a multi-disciplinary team, bringing together expertise in market design and econometrics at the University of Virginia and the machine learning expertise and domain knowledge in the field of technical recruiting at Riviera Partners. The team will extend its collaboration to developing formal econometric models and an AI application. The combined collaborative networking of the university and the industry will facilitate broader dissemination of the designed tool and formal project results in the academic community, high tech recruiting industry, and career development forums.This Convergence Accelerator Phase I project will comprise research and engineering effort to build an Artificial Intelligence (AI) tool for career management, targeted to help high-tech talent in STEM fields by transforming information into quantifiable data that can be analyzed and used directly in an econometric model. The goal is to characterize the long-term impact of career choices in conjunction with individual characteristics such as education, previous job experience, training, personal goals, career interests, and other components and to provide insights into the market value of various career choices. The final deliverable of the project is a web application, with an embedded econometric model, that is intended to be used by job candidates in technical fields. The proposed work will use recent advances in structural econometrics and machine learning, such as causal prediction and natural language processing, to create models that incorporate rich, but previously overlooked, data which will be analyzed in the econometric model. The tool that incorporates these causal relationships and models will assist in the planning and evaluation of career path options, such as switching careers, acquiring new skills, or taking a new position, providing more value than commonly used compensation-based job decisions.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.

NSF 融合加速器支持基于团队的多学科努力，以解决国家重大挑战并在不久的将来展示交付成果的潜力。 该融合加速器第一阶段项目的更广泛影响/潜在好处是为 STEM 领域的职业发展提供一个工具。拟议的解决方案有可能通过解决劳动力市场快速数字化和自动化导致的对技术人才不断增长的需求来影响美国的劳动力供应。它可以通过更好地帮助女性和少数群体开辟成功的技术职业道路，帮助弥合高科技行业的性别和多样性差距。该项目的融合基于多学科团队的协调工作，汇集了弗吉尼亚大学市场设计和计量经济学方面的专业知识以及 Riviera Partners 技术招聘领域的机器学习专业知识和领域知识。该团队将扩大合作范围，开发正式的计量经济学模型和人工智能应用程序。大学和行业的联合协作网络将促进所设计的工具和正式项目结果在学术界、高科技招聘行业和职业发展论坛上更广泛地传播。该融合加速器第一阶段项目将包括研究和工程工作，以构建用于职业管理的人工智能（AI）工具，旨在通过将信息转化为可直接在计量经济模型中分析和使用的量化数据来帮助STEM领域的高科技人才。目标是结合个人特征（如教育、以前的工作经验、培训、个人目标、职业兴趣和其他组成部分）来描述职业选择的长期影响，并提供对各种职业选择的市场价值的见解。该项目的最终交付成果是一个带有嵌入式计量经济学模型的 Web 应用程序，旨在供技术领域的求职者使用。拟议的工作将利用结构计量经济学和机器学习的最新进展，例如因果预测和自然语言处理，来创建包含丰富但以前被忽视的数据的模型，这些数据将在计量经济学模型中进行分析。整合这些因果关系和模型的工具将有助于规划和评估职业道路选择，例如转换职业、获取新技能或担任新职位，比常用的基于薪​​酬的工作决策提供更多价值。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。