This project aims to create an annual week-long Data Science Forum (DSF), co-locating several events intended to advance knowledge, create networks of researchers, and train students and faculty. The focus of this forum will be on developing the next generation of AI technologies and researchers with the underlying hypothesis that applications across the sciences and engineering disciplines will be the context of the next major advances in data-driven approaches. DSF brings together the Second Symposium on Machine Learning in Science and Engineering (MLSE), a Women in Data Science Workshop (WDSW), and a Foundations of Data Driven Discovery workshop (FDDD). A forum including multiple events will bring more women to MLSE and FDDD, while juxtaposing MLSE with FDDD will encourage cross-fertilization among the domain scientists and engineers and the core TRIPODS (Transdisciplinary Research in Principles of Data Science) community. The 2nd MLSE will conclude with a Visioning Working Group where a select group of researchers will produce a white paper on the future of machine learning.
The Data Science Forum will help catalyze machine learning methodologies and collaborations across the sciences and engineering, bringing together a diverse set of STEM fields applying machine learning to fundamental and applied problems. Presentations will focus on adapting existing machine learning methods to current research areas, developing new machine learning algorithms specific to science and engineering, and identifying new frontiers of research that may only be pursued using a data-driven approach. Disseminating machine learning methods across science and engineering could have lasting implications for US research. The forum will supplement the technical research program with short courses taught by experts in machine learning on a variety of cutting-edge tools that are critical in advancing these fields. Each track will have a theme centered in a traditional domain, but each is aiming to itself be interdisciplinary. Running these tracks in parallel will help foster tight knit communities of researchers in each of the applied science or engineering tracks, as well as the TRIPODS community, while fostering cross-fertilization of ideas across fields through joint events and co-location.
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.
