Conference: UCLA Synthetic Data Workshop

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.