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We present an overview of four challenging research areas in multiscale physics and engineering as well as four data science topics that may be developed for addressing these challenges. We focus on multiscale spatiotemporal problems in light of the importance of understanding the accompanying scientific processes and engineering ideas, where “multiscale” refers to concurrent, non-trivial and coupled models over scales separated by orders of magnitude in either space, time, energy, momenta, or any other relevant parameter. Specifically, we consider problems where the data may be obtained at various resolutions; analyzing such data and constructing coupled models led to open research questions in various applications of data science. Numeric studies are reported for one of the data science techniques discussed here for illustration, namely, on approximate Bayesian computations. 

A computational approach has become an indispensable tool in materials science research and related industry. At the University of Illinois, Urbana-Champaign, our team at the Department of Materials Science and Engineering (MSE), as part of a Strategic Instructional Initiatives Program (SIIP), has integrated computation into multiple MSE undergraduate courses over the last years. This has established a stable environment for computational education in MSE undergraduate courses through the duration of the program. To date, all MSE students are expected to have multiple experiences of solving practical problems using computational modules before graduation. In addition, computer-based techniques have been integrated into course instruction through iClicker, lecture recording, and online homework and testing. In this paper, we seek to identify the impact of these changes beyond courses participating in the original SIIP project. We continue to keep track of students' perception of the computational curriculum within participating courses. Furthermore, we investigate the influence of the computational exposure on students' perspective in research and during job search. Finally, we collect and analyze feedback from department faculty regarding their experience with teaching techniques involving computation.