Santiago Olivella

I am an associate professor of Political Science and of Data Science and Society at the University of North Carolina, Chapel Hill. My research focuses on the use and development of novel computational methods for quantitative political research (particularly Bayesian and Machine Learning probabilistic models), the inferential study of networks, the measurement of latent traits, and the political consequences of electoral and legislative institutions. You can download my CV here.

Associate Professor of Data Science and Society

2024 - Present · University of North Carolina at Chapel Hill

Associate Professor of Political Science

2021 - Present · University of North Carolina at Chapel Hill

Assistant Professor

2017 - 2021 · University of North Carolina at Chapel Hill

Visiting Assistant Professor

2019 - 2020 · Harvard University

Visiting Associate Research Scholar

2015 - 2017 · Princeton University

Assistant Professor

2013 - 2016 · University of Miami (Miami, FL)

Ph.D. in Political Science

2007 - 2013 · Washington University in St. Louis

M.A. in Political Science

2007 - 2009 · Washington University in St. Louis

B.A. in Political Science

2001 - 2006 · Universidad de los Andes (Bogotá, Colombia)

Journal Articles

2025. Lo, A., Olivella, S., and Imai, K. A Statistical Model of Bipartite Networks: Application to Cosponsorship in the United States Senate. Political Analysis doi:10.1017/pan.2025.10021.

2023. Rosenman, E., and Olivella, S., and Imai, K. Race and ethnicity data for first, middle, and last names. Nature: Scientific Data. 10(299): 1–11.

2023. Shiraito, Y., Lo, J., and Olivella, S. A Nonparametric Bayesian Model for Detecting Differential Item Functioning: An Application to Political Representation in the US. Political Analysis. 31(3): 430–447.

2023. Rosenman, E., McCartan, C., and Olivella, S. Recalibration of Predicted Probabilities Using the 'Logit Shift': Why does it work, and when can it be expected to work well?. Political Analysis. 31(4): 651–661.

2022. Imai, K., Olivella, S., and Rosenman, E. Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements. Science Advances 8(eadc9824): 1-10.

2022. Olivella, S., and Pratt, T., and Imai, K. . Dynamic Stochastic Blockmodel Regression for Network Data: Application to International Militarized Conflicts. Journal of the American Statistical Association 117(539): 1068–1081.

2018. Olivella, S., and Montgomery, J. B. Tree Based Methods for Political Science. American Journal of Political Science 62(3): 729–744.

2017. Uscinski, J. and Olivella, S. . The Conditional Effect of Conspiracy Thinking on Attitudes toward Climate Change. Research & Politics October-December 2017: 1–9.

2017. Olivella, S., Kanthak, K. and Crisp, B. ...And Keep Your Enemies Closer: Cosponsorship Patterns as Reputation Building. Electoral Studies 46(1): 75-–86.

2015. Montgomery, J., Olivella, S., Potter, J., and Crisp, B. . An Informed Forensics Approach to Detecting Vote Irregularities. Political Analysis, 23(4): 488–505.

2015. Potter, J. and Olivella, S. Electoral strategy in geographic space: Accounting for spatial proximity in district-level party competition. Electoral Studies, 40(1): 76–86.

2014. Atkinson, M., Mann, C., Olivella, S., Simon, A. and Uscinski. J. (Where) Do Campaigns Matter? The Impact of National Party Convention Location. The Journal of Politics, 76(4): 1045–1058.

2014. Crisp, B., Olivella, S., Potter, J. and Mishler, W. . Elections as Instruments for Punishing Bad Representatives and Selecting Good Ones. Electoral Studies, 31(1): 1–15.

2013. Olivella, S. and Tavits, M. Legislative Effects of Electoral Mandates. British Journal of Political Science, 44(2): 301–321.

2013. Crisp, B., Olivella, S., Malecki, M., and Sher, M. Vote-Earning Strategies in Flexible List Systems: Seats at the Price of Unity. Electoral Studies, 32(4): 658–669.

2013. Crisp, B., Olivella, S., and Potter, J. Party System Nationalization and the Scope of Public Policy. Comparative Political Studies, 46(4): 431–456.

2012. Crisp, B., Olivella, S., and Potter, J. Electoral contexts that impede voter coordination. Electoral Studies, 31(1): 143–158.

2006. Olivella, S. and Vélez, C. Will Uribe’s coalition survive?” (in Spanish). Colombia Internacional, 64(2): 194–205.

Books

2025. Crisp, B. F., Cunha Silva, P., Olivella, S., and Rosas, G. Electoral System Incentives for Interparty and Intraparty Politics. Oxford University Press.

2020. Crisp, B. F., Olivella, S. and Rosas, G. The Chain of Representation: Preferences, Institutions, and Policy in Latin America’s Presidential Systems. Cambridge University Press.

Book Chapters

2020. Shoub, K., and Olivella, S. 'Machine Learning in Political Science' (in The SAGE Handbook of Research Methods in Political Science and International Relations). SAGE Publications.

2014. Olivella, S., and Botero, F. 'The preferential vote in Colombia' (in Spanish; in Lina Cano (Ed.) Reforma Estructural del equilibrio de poderes en Colombia). Universidad de Antioquia.

2013. Crisp, B., Olivella, S., and Potter, J. 'A Comparison of different indicators of party system consolidation' (in Spanish) in Torcal, M. (Ed.) Sistemas de partidos en América Latina. Causas y consecuencias de su equilibrio inestable. Editorial Anthropos.

2011. Olivella, S. 'Cross-Tabular Analysis.' in Badie B. et al. (Eds.) International Encyclopedia of Political Science. SAGE Publications.

2009. Olivella, S. and Rodríguez-Raga, J. C. . 'What is spatial is special: Proximity voting in Colombia' (in Spanish). in Botero, F. (Ed.) Juntos pero no revueltos? Partidos, candidatos y campañas en las elecciones legislativas de 2006 en Colombia. Uniandes - CESO.

i3pack (R package)

Incentives for Inter- And Intra-Party Electoral Competition.

lda (R package)

Collapsed Gibbs Sampling Methods for Topic Models.

wru (R package)

Who are You? Bayesian Prediction of Racial Category Using Surname, First Name, Middle Name, and Geolocation.

NetMix (R package)

Variational EM estimation of mixed-membership stochastic blockmodel for networks, incorporating node-level predictors of mixed-membership vectors, as well as dyad-level predictors.

poisbinom (R package)

Provides the probability, distribution, and quantile functions and random number generator for the Poisson-Binomial distribution.

Representing communities of economic interest in the U.S. House (w/ S. Treul and N. Pinnell)

Communities of shared interest can easily fail to coincide with congressional dis-tricts. When areas that demarcate these communities span multiple congressionaldistricts, do representatives shift their efforts to reflect the interests of a broader set ofcitizens, or do they maintain a focus on the local interests of their own constituents?Using journey-to-work data for millions of Americans, we measure the extent to whichone such community — the labor market area — crosses congressional district bound-aries, and evaluate the effect of these shared economic interests on the representationalefforts of House members. We find that members coming from a common labor marketarea tend to collaborate more often, and that members representing districts embed-ded in larger labor market areas sponsor more legislation on national issues. Thesepatterns may exacerbate polarization in Congress and hurt representation of urbanminority populations, even in the absence of malapportionment.

DATA 120: Ethics of AI and Societal Decision Making

POLI 783: Fundamental Mathematics for Political Research

POLI 787: Advanced Quantitative Methods in Political Science

POLI 891: Introduction to Machine Learning

POLI 784: Regression Models and Causal Inference in Political Science

POLI 883: Introduction to Bayesian Modeling