Abstract

In April, the United States presidential administration announced a whole-of-government effort focused largely on gathering and increasing access to disaggregated data on the experiences of historically underserved groups. The importance of disaggregating the data on specific subpopulations can easily be overlooked in efforts that target diversity broadly. Drawing inspiration from astrophysics, this talk will focus on data and analyses related to the hiring of a specific population that is underrepresented in scientific research: African-American doctoral degree holders. Using the Drake equation to frame the discussion, the talk will address the extent to which the search for African-American terrestrial intelligence (SATI) can be understood through the analytical lens of the search for extra-terrestrial intelligence (SETI). With this framing, we will tackle an oft-cited cause for underrepresentation, the pipeline, in light of statistical arguments suggesting the implausibility that pipeline problems fully explain the observed underrepresentation in some elite settings. The talk will briefly touch some unexpected benefits of involving a more diverse population in science, arguing that diverse groups both do scientific research differently and do different scientific research. The talk will conclude with a call for accountability through disaggregating data in diversity, equity, and inclusion (DEI) initiatives.

Materials

Slides: Video:

This webinar was brought to you by the Exascale Computing Project (ECP) HPC Workforce Development and Retention Action Group, which organizes a webinar series on topics related to developing a diverse, equitable, and inclusive work culture in the computing sciences.

Speaker

Dr. Damian Rouson, Lawrence Berkeley National Laboratory

Biography Damian Rouson is the Group Lead for the Computer Languages and Systems Software (CLaSS) Group at Lawrence Berkeley National Laboratory (“Berkeley Lab”). CLaSS studies, designs, and develops new parallel programming languages and execution environments, including compilers, libraries, and networking middleware. Damian’s research at Berkeley Lab explores the use of machine learning to accelerate predictions of climate change’s regional impacts using Fortran 2018 and UPC++. He also founded Sourcery, Inc. a research software engineering consultancy focused on modern Fortran, including modernizing legacy Fortran. Sourcery, Inc. has worked on software projects in domains ranging from particle-beam physics and nuclear energy to weather and climate science. He also founded Sourcery Institute, a California public-benefit nonprofit corporation granted 501(c)(3) tax-exempt status for research and education in computational science and engineering. Sourcery Institute offers training courses, publishes open-source course modules, and funds a Ph.D. fellowship at Cranfield University. He co-authored the textbook Scientific Software Design: The Object-Oriented Way (Cambridge University Press, 2011) and has taught related university courses and tutorials on Fortran 2018 and agile software development. He is an alternate member of the Fortran standards committee. He holds a B.S. from Howard University and an M.S. and Ph.D. from Stanford University, all in mechanical engineering.