Conference: Artificial Intelligence Summer School for Computer Science and Operations Research Education; College Park, Maryland; 19-24 May 2024

这笔赠款提供资金，支持在马里兰州学院公园举办人工智能计算机科学和运筹学研究教育暑期学校；2024年5月19日至24日。为运筹学(OR)和计算机科学(CS)领域的学生和研究人员举办的暑期班，帮助弥合运筹学和计算机科学在推进人工智能(AI)研究方面的差距。优化方法是支持人工智能的许多技术的核心。一般而言，OR社区从基于模型的角度开始开发优化方法，而CS社区响应海量数据集可用性的增加，从计算、数据驱动的角度开始。这两种观点都有很多可以相互传授和学习的地方，但到目前为止，它们是分开发展的。暑期班旨在突出这两个视角，并引导研究生在推进人工智能研究时理解这两个视角的价值。这项资助为来自美国各地的早期阶段的博士生提供支持，让他们参加由来自OR和CS社区的人工智能领域的学术研究人员组织的为期一周的暑期学校。暑期班的主题将集中在强化学习和公平/公平，并由运筹学和计算机科学研究小组联合编写专门针对该场合的辅导材料。其目的是拓宽学生对这两个领域的知识和认识，并在两个领域的界面上为未来的研究提供种子。组织者将广泛宣传暑期班，并将目标是两个领域的同等出勤率。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。

This grant provides funding to support the convening of Artificial Intelligence Summer School for Computer Science and Operations Research Education; College Park, Maryland; 19-24 May 2024. A summer school for students and researchers from the fields of operations research (OR) and computer science (CS) to assist in bridging the gap between the OR and CS perspectives on advancing artificial intelligence (AI) research. Optimization methods are at the core of many techniques that support AI. Generally speaking, the OR community, where optimization methods developed from foundational operational challenges, begins from a model-based perspective, while the CS community, responding to the rise in availability of massive data sets, begins from a computational, data-driven perspective. Both perspectives have much to teach and learn from each other, but to date, they have evolved separately. The Summer School is intended to highlight both perspectives and to guide graduate students in understanding the value of both perspectives as they advance AI research. This grant provides support for early-phase PhD students from across the US to attend a week-long summer school organized by academic researchers in the field of AI from both OR and CS communities. The topics for the summer school will focus on reinforcement learning and fairness/equity, with tutorial material developed especially for the occasion jointly by teams of OR and CS researchers. The intent is to broaden the knowledge and awareness of the students to both fields and to seed future research at the interface of both fields. The organizers will advertise the summer school widely and will target equal attendance across both fields.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.