Conference: UCLA Synthetic Data Workshop

该奖项支持来自不同数学学科和计算机科学的专家，特别是研究生和其他早期职业研究人员参加即将于2023年4月13日至4月14日在加州大学洛杉矶分校举行的加州大学洛杉矶分校综合数据研讨会。研讨会的目标是促进研究人员在与合成数据和数据隐私相关的几个领域的合作，包括差异隐私、公平和对抗性鲁棒性。这项活动的基本原理是，合成数据生成是一个迅速发展和高度学科化的研究领域，引起了许多注意。对于开发欺诈欺骗和垃圾邮件识别的算法程序，以及在制造业和供应链管理中构建人工智能驱动的模型，合成数据已成为一种宝贵的资源。本次研讨会的目标是研究由这些进步产生的科学基础，并研究解决开放性问题的新策略。讲习班还将以介绍性会谈的形式提供大量教学内容，介绍其重点领域的背景和最近令人兴奋的进展。这些讲座将对非专家开放，包括研究生和初级研究人员。当获取真实数据的成本太高或风险太大时，合成数据特别有用。最近的结果暗示了一个新的和有希望的方向，即从业者可以通过使用合成数据解决边缘场景和危险事件来有效地训练人工智能模型。尽管合成数据有许多成功的应用，但它的科学基础，例如，在保真度、实用性和隐私性之间的权衡，仍然缺失。此外，合成数据的产生和利用的行业标准和有关合成数据的隐私法尚未制定。本次研讨会将为专家们提供一个环境，就合成数据的开放性问题交换意见，例如在创建合成数据时是否会丢失隐私，使用合成数据是否会影响公平性，以及如何在最基本的层面上判断合成数据的质量和有用性。研讨会的网站是https://ucla-synthetic-data.github.io/.This，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award supports participation by experts from diverse mathematical disciplines and computer science, especially graduate students and other early-career researchers, in the upcoming UCLA Synthetic Data Workshop to be held at the University of California, Los Angeles, from April 13 to April 14, 2023. The goal of the workshop is to foster the collaboration of researchers in several areas connected to synthetic data and data privacy, including differential privacy, fairness, and adversarial robustness. The rationale for this activity is that synthetic data generation is a rapidly growing and highly disciplinary research area that draws much attention. For the development of algorithmic procedures for fraud deception and spam identification, as well as for the construction of AI-driven models in manufacturing and supply chain management, synthetic data has become a valuable resource. The goal of this workshop is to investigate scientific foundations that are spawned by these advancements and examine new strategies for solving open problems. The workshop will also have a substantial pedagogical component in the form of introductory talks that will cover background and recent exciting progress in its focus areas. These talks will be accessible to non-experts, including graduate students and junior researchers.Synthetic data is especially useful when obtaining real-world data is either too costly or too risky. Recent results hint at a new and promising direction that practitioners may effectively train AI models by addressing edge scenarios and dangerous occurrences while using synthetic data. Despite numerous successful applications of synthetic data, its scientific foundation, e.g., the tradeoff among fidelity, utility, and privacy, is still missing. In addition, industrial standards for generating and utilizing synthetic data, as well as the privacy law concerning synthetic data, are yet to be established. This workshop will provide an environment for experts to exchange their ideas for open questions about synthetic data, such as whether or not privacy is lost when creating synthetic data, whether or not using synthetic data affects fairness, and how, at the most basic level, one should judge the quality and usefulness of synthetic data. The website for the workshop is https://ucla-synthetic-data.github.io/.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.