The IFDS Workshop on the Theoretical Foundations of Applied AI will take place August 13–15, 2025, at the University of Washington’s Seattle campus. This workshop brings together a diverse group of theoreticians and applied researchers to explore the theoretical underpinnings behind the successes and limitations of modern AI. Through invited talks and dynamic discussions, we aim to deepen our understanding of foundational principles that drive real-world impact. The workshop is part of the scientific programming of the NSF TRIPODS Institute for Foundations of Data Science (IFDS), a multi-institutional collaboration involving the University of Washington, University of Chicago, University of Wisconsin, and University of California-Santa Cruz. The workshop is organized with support from IFDS at the University of Wisconsin. We look forward to engaging conversations and new collaborations at the intersection of theory and applications.
IFDS is excited to announce a two-day NSF-supported workshop hosted by the Institute for Foundations of Data Science (IFDS) at the University of Wisconsin-Madison. The workshop, titled “Data Meets Dynamics: Workshop on Data Assimilation for Complex Systems and Applications,” will take place on August 21–22, 2025 (Thursday–Friday). This event is supported by NSF and IFDS.
This event is a collaboration between the IFDS at UW-Madison and the Data Science Center at Brigham Young University (BYU), a key partner of the UW IFDS. More details about the workshop can be found on our website:
The workshop will feature a range of activities, including oral presentations, poster sessions, and lightning talks, offering students and junior researchers an excellent opportunity to showcase their work. We encourage you to share this announcement with your department, your group members, or junior researchers who are interested in attending.
As the workshop is supported by the NSF, we are pleased to offer partial travel funding to selected participants. Additionally, there is no registration fee for this event. To apply for travel support and provide information about your participation, please complete the following form no later than March 1:
This workshop brings together researchers and practitioners to explore the broad landscape of data assimilation, emphasizing both theoretical foundations and practical applications. On the theoretical front, the workshop will delve into topics such as nudging data assimilation and its connections to partial differential equations (PDEs), control theory, and error analysis. For practical methods, we will highlight a range of Bayesian data assimilation techniques, including the ensemble Kalman filter and the particle filter, which represent discrete-in-time approaches. Continuous-intime frameworks, such as nudging methods, conditional Gaussian nonlinear data assimilation, and the ensemble Kalman-Bucy filter, will also be discussed, with real-world applications in climate science, atmospheric and ocean modeling, and engineering systems. A key focus of the workshop is to strengthen interdisciplinary connections between data assimilation and tools such as machine learning, stochastic models, parameter estimation, optimal control, and model identification. By fostering discussions among different communities, the event aims to bridge gaps between theory and practice, encourage collaboration, and inspire new research directions. Additionally, the workshop will provide an excellent opportunity for young researchers to gain exposure to various methods, equipping them with tools to address challenges in complex dynamical systems.
Ying Fan, a former IFDS Wisc RA advised by Kangwook Lee, has been selected as a 2024 Rising Star in Machine Learning. The award recognizes her outstanding achievements and potential in advancing the field of machine learning. She was invited to deliver a talk at the Rising Star in Machine Learning series, held on December 5 and 6 at the University of Maryland, College Park.
UChicago and UW-Madison are part of a new AI Institute for the SkAI that has foundational components stemming from IFDS research
IFDS PI Rebecca Willett was the recipient of the SIAM Activity Group on Data Science Career Prize at the 2024 SIAM Conference on Mathematics of Data Science (MDS24). Link: https://www.siam.org/publications/siam-news/articles/2024-october-prize-spotlight/