August 9-11, 2021 (virtual)

This workshop will include invited talks on machine learning applications in aquatic sciences, hydrology, atmospheric science, and translational biology. Free and open to the public, but registration is required.

Facilitated by the University of Minnesota, this workshop builds on the recognition that current black-box machine learning (ML) models have met with limited success in many scientific domains. This is due to their large data requirements, inability to produce physically consistent results, and lack of generalizability to out-of-sample scenarios. Instead of a purely data-driven approach that ignores decades (sometimes centuries) of accumulated knowledge in the science domains, there is an increasing interest in Knowledge Guided Machine Learning (KGML) approaches to integrate such knowledge into the ML models. This workshop will discuss recent advances made in this emerging area as well as their applications in the context of four domains: freshwater science, hydrology, climate and weather, and translational biology.

The workshop will include invited talks and poster sessions by data scientists (researchers in data mining, machine learning, and statistics) and researchers from four application areas (aquatic sciences, hydrology, atmospheric science, and translational biology) to discuss challenges, opportunities, and early progress in bringing scientific knowledge to machine learning. In doing so, it aims to foster interdisciplinary collaborations and interactions among these communities. The expected outcomes are the identification and greater understanding of challenges and opportunities in knowledge guided machine learning.

Click here for more information and registration link.