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.