Women are dramatically underrepresented in computer science at all levels in academia, and account for just 15% of tenure-track faculty. Understanding the causes of this gender imbalance would inform both policies intended to rectify it and employment decisions by departments and individuals. Progress in this direction, however, is complicated by the complexity and decentralized nature of faculty hiring, and the non-independence of hires. In this talk, I'll describe an investigation of the multi-dimensional nature of gender inequality in computer science faculty hiring, using comprehensive data on both hiring outcomes and scholarly productivity for 2659 tenure-track faculty across 205 Ph.D.-granting departments in North America. Using a network model of the hiring process, we characterize the role of gender alone on observed hiring outcomes, and we investigate gender differences in scholarly productivity, postdoctoral training rates, career movements up the rankings of universities, and the ability of departments to independently improve their gender balance. Our model additionally allows us to calculate an effective 'gender penalty' in terms of the number of additional papers a female candidate would need to have written in order to place similarly to a male candidate, and to make a long-term estimate as to when gender parity in faculty hiring will be reached. I'll close with some discussion of the subtle nature of gender inequality in faculty hiring networks, and how our results compare to past work on diversity in academia.

This is joint work with Samuel F. Way and Daniel B. Larremore.

Aaron Clauset is an Assistant Professor of Computer Science at the University of Colorado at Boulder and in the BioFrontiers Institute, and External Faculty at the Santa Fe Institute. His lab generally focuses on understanding the mechanisms by which large-scale patterns emerge from the collective actions of heterogeneous individuals and on developing novel techniques for inferring such patterns and mechanisms from rich data sources. Much of this work is methodological in nature, and he actively develops novel statistical and computational methods for automatically analyzing and modeling complex phenomena in biological, social and technological systems. Aaron completed his Ph.D. in Computer Science at the University of New Mexico in 2006, and was an Omidyar Fellow at the Santa Fe Institute from 2006-2010.