Conference: Support for Early Career Participants in Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (UQ-MLIP)

Tufts University, Medford MA

Investigators

Abstract

The 2022 Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (UQ-MLIP) will be a venue for bringing together leading experts, scientists, and young researchers from across the US, in the domains of uncertainty quantification (UQ) and machine learning (ML) for physics modeling in order to: (1) identify challenges and opportunities to advance the field; (2) rapidly disseminate the latest advances in a single place; and (C) provide networking and mentoring opportunities for early career researchers. The conference will be held in Arlington, Virginia, August 18-19, 2022, and is organized by a leading group of experts in UQ and ML for physics modeling. This grant will fund students and a small number of early career researchers to participate in the conference, to engage deeply with the UQ-MLIP research community. The students and early career researchers will participate in panel discussions, attend invited talk sessions, and participate in poster sessions or present short talks. The conference will also advocate for the development of shared datasets for shared community use for validation and algorithm evaluation, under the auspices of the USACM (U.S. Association for Computational Mechanics) technical thrust area of uncertainty quantification. The project serves the national interest, as stated by NSF's mission, to promote the progress of science as it provides a forum to disseminate research efforts, connect researchers, and train the next generation of scholars. The conference organizers are recruiting students and early career researchers wishing to participate in the program from a diverse set of institutions, and with the goal of a diverse set of participants. Prospective participants will have to submit an application, and submissions are evaluated by a committee for scientific merit and to promote diversity. Students and early career researchers will participate in career panel discussions, attend invited technical talk sessions, and participate in poster sessions or present short talks on their research. The funding provided by NSF will have a significant impact on the careers of the future generation of researchers in uncertainty quantification, machine learning, and physics modeling, while encouraging diversity in the field. 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.