A Virtual Conference on Applications of Statistical Methods and Machine Learning in the Space Sciences; Boulder, Colorado; March 22-26, 2021 or June 28 - July 2, 2021

The award is to support a virtual conference, Applications of Statistical Methods and Machine Learning in the Space Sciences, which aims to bring together experts in academia and industry to leverage recent advancements in statistics, data science, artificial intelligence (AI) and information theory to make use of large volume datasets in the field of space sciences. The conference anticipates participation of researchers from all disciplines of space science and data scientists, statisticians and AI experts.

The conference will be a unique opportunity for sharing recent trends and advancements in exploring “big data”, and for fostering interdisciplinary collaborations.

Machine learning is an emerging trend in the space sciences to identify patterns and extract information from the enormous data acquired using spacecraft and ground-based observations. Statistical methods have already been in use in data analysis for decades and the aim of the conference is to bring these two techniques together in the analysis of “big data” in the space sciences.

Machine learning (ML) algorithms, particularly neural networks, present a “black box” solution to the problem in which a particular method is applied. There are advanced statistical methods that can be used either by themselves or in conjunction with a machine learning algorithm to probe deeper into the relationships between input and output variables in the ML model, and the underlying physics.

The conference will have an emphasis on “black box” versus “interpretable” models — that is, on understanding the physics and dynamics of the system while seeking an accurate solution using ML methods

The conference will provide training for new researchers in the use of these versatile and novel tools of data analysis. Recent graduates and early career researchers will be better equipped with an appreciation of the benefits of interdisciplinary approaches to understanding the fundamental science problems in their respective fields of interest.

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.