Summer School: Data Science Methods in Materials Science & Engineering

A two-day summer school will take place in Amherst, Massachusetts in Summer 2022. The intended audience includes graduate students and postdocs in Chemistry, Physics, Engineering, Applied Mathematics with an interest in materials science, related energy research and manufacturing. Contact Markos Katsoulakis for further details.

Synopsis

There is a tremendous need for training young scientists and engineers in Data Science methods in order to take advantage in a synergistic way of ever-growing methods and tools in Data Science along with unprecedented advances in experimental methods and simulation capabilities that create new, highly informative (but potentially expensive) data sets. However, there is a lack of a systematic training paradigm for students in applied sciences and engineering who need to learn and use Data Science in an effective manner. This Summer School will address this type of challenge at a scale small enough for the TRIPODS Institute to put together communities that do not typically interact, and consider what are the best ways and practices to accomplish synergies between Materials and Data Science.

Structure

The structure of the School will be as follows:

• (a) Talks with DS-related themes and challenges by domain scientists (Mornings)
• (b) Motivated by problems in (a), introductory talks in (broadly relevant to (a)) methods in DS, (e.g. approximate inference, neural nets, graphical models, sparse data methods, etc.) by Data Scientists with an interest in applied science and engineering (Afternoons)
• (c) Round Table Discussion and Q&A between participants and students (Last day).
• (d) Get input, suggestions from all participants at the end of the meeting.