[1] He then went to the California Institute of Technology where he studied social science under Morgan Kousser and received a Ph.D. in 1998.

[10] This was awarded Best policy innovations from Politico (2011), the Antonio Pizzigati Prize for Software in the Public Interest from the Tides (2013) and the Brown Democracy Medal from Pennsylvania State University (2018).

[15][16] The undesirable implications of this result are that redistricting cannot be fully automated in practice and the choice of constraints and manual selection of the winning, "optimal" plan from a group of auto-generated plans, reintroduce value-laden and politically biased decision making back into the redistricting process (something that the use of "objective" computer programs was hoped to avoid), while potentially also legitimizing such undercover gerrymandering for the less knowledgeable public.

[15] Further, computational simulations that he performed showed also that even the constraints that have been traditionally considered politically non-preferential, such as the overall compactness of the district, are not necessarily non-preferential because compactness requirements have different effects on political groups if the groups are distributed in geographically different ways.

[8][9] Based on this analysis, Altman, McDonald and Gill developed methods to detect issues in social science statistical models and provide more replicable and reliable estimates.

It included the development of standards for data citation;[24] the creation of semantic fingerprint methods to verify data for scientific reuse, and long-term archiving;[25][26] the analysis of technical and institutional approach to long-term preservation;[27] the creation of taxonomic standards for author attribution (working with Amy Brand and other);[28] and the characterization of grand-challenge problems in scholarly communications.