Zhang, C., Janzing, D., van der Schaar, M., Locatello, F., Spirtes, P., Zhang, K.,  Uhler, C. (2023). Causality in the time of LLMs: Round table discussion results of CLeaR 2023. In 2nd Conference on Causal Learning and Reasoning. Tübingen, Germany.

Mohammadi, F., Uhler, C., Wang, C., &; Yu, J. (2018). Generalized permutohedra from probabilistic graphical models. SIAM Journal on Discrete Mathematics. SIAM.https://doi.org/10.1137/16M107894X 

Raskutti, G., &; Uhler, C. (2018). Learning directed acyclic graphs based on sparsest permutations. STAT. Wiley.https://doi.org/10.1002/sta4.183 

Wang, Y., Nagarajan, M., Uhler, C., &; Shivashankar, G. (2017). Orientation and repositioning of chromosomes correlate with cell geometry dependent gene expression. Molecular Biology of the Cell. American Society for Cell Biology.https://doi.org/10.1091/mbc.E16-12-0825 

Martin Del Campo Sanchez, A., Cepeda Humerez, S. A., &; Uhler, C. (2017). Exact goodness-of-fit testing for the Ising model. Scandinavian Journal of Statistics. Wiley-Blackwell.https://doi.org/10.1111/sjos.12251 

Zwiernik, P., Uhler, C., &; Richards, D. (2017). Maximum likelihood estimation for linear Gaussian covariance models. Journal of the Royal Statistical Society. Series B: Statistical Methodology. Wiley-Blackwell.https://doi.org/10.1111/rssb.12217 

Fallat, S., Lauritzen, S., Sadeghi, K., Uhler, C., Wermuth, N., &; Zwiernik, P. (2017). Total positivity in Markov structures. Annals of Statistics. Institute of Mathematical Statistics.https://doi.org/10.1214/16-AOS1478 

Uhler, C., &; Shivashankar, G. V. (2016). Geometric control and modeling of genome reprogramming. BioArchitecture. Taylor &; Francis.https://doi.org/10.1080/19490992.2016.1201620 

Solus, L. T., Uhler, C., &; Yoshida, R. (2016). Extremal positive semidefinite matrices whose sparsity pattern is given by graphs without K5 minors. Linear Algebra and Its Applications. Elsevier.https://doi.org/10.1016/j.laa.2016.07.026 

Michaek, M., Sturmfels, B., Uhler, C., &; Zwiernik, P. (2016). Exponential varieties. Proceedings of the London Mathematical Society. Oxford University Press.https://doi.org/10.1112/plms/pdv066 

Klimova, A., Uhler, C., &; Rudas, T. (2015). Faithfulness and learning hypergraphs from discrete distributions. Computational Statistics &; Data Analysis. Elsevier.https://doi.org/10.1016/j.csda.2015.01.017 

Yu, F., Fienberg, S., Slakovi, A., &; Uhler, C. (2014). Scalable privacy-preserving data sharing methodology for genome-wide association studies. Journal of Biomedical Informatics. Elsevier.https://doi.org/10.1016/j.jbi.2014.01.008 

Lin, S., Uhler, C., Sturmfels, B., &; Bühlmann, P. (2014). Hypersurfaces and their singularities in partial correlation testing. Foundations of Computational Mathematics. Springer.https://doi.org/10.1007/s10208-014-9205-0 

Uhler, C., Lenkoski, A., &; Richards, D. (2014).  Exact formulas for the normalizing constants of Wishart distributions for graphical models. ArXiv. ArXiv.

Yu, F., Rybar, M., Uhler, C., &; Fienberg, S. (2014). Differentially-private logistic regression for detecting multiple-SNP association in GWAS databases. In J. Domingo Ferrer (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8744, pp. 170–184). Ibiza, Spain: Springer.https://doi.org/10.1007/978-3-319-11257-2_14 

Iglesias Ham, M., Kerber, M., &; Uhler, C. (n.d.). Sphere packing with limited overlap. arXiv.https://doi.org/10.48550/arXiv.1401.0468 

Uhler, C., Raskutti, G., Bühlmann, P., &; Yu, B. (2013). Geometry of the faithfulness assumption in causal inference. The Annals of Statistics. Institute of Mathematical Statistics.https://doi.org/10.1214/12-AOS1080 

Uhler, C., Slavkovic, A., &; Fienberg, S. (2013). Privacy-preserving data sharing for genome-wide association studies. Journal of Privacy and Confidentiality . Carnegie Mellon University.https://doi.org/10.29012/jpc.v5i1.629 

Uhler, C., &; Wright, S. (2013). Packing ellipsoids with overlap. SIAM Review. Society for Industrial and Applied Mathematics .https://doi.org/10.1137/120872309 

Uhler, C. (2012). Geometry of maximum likelihood estimation in Gaussian graphical models. Annals of Statistics. Institute of Mathematical Statistics.https://doi.org/10.1214/11-AOS957 

Malaspinas, A., &; Uhler, C. (2011). Detecting epistasis via Markov bases. Journal of Algebraic Statistics. Public Knowledge Project.http://dx.doi.org/10.18409/jas.v2i1.27 

Fienberg, S., Slavkovic, A., &; Uhler, C. (2011). Privacy Preserving GWAS Data Sharing. Presented at the Proceedings of the 11th IEEE International Conference on Data Mining, IEEE.https://doi.org/10.1109/ICDMW.2011.140 

Evans, S., Sturmfels, B., &; Uhler, C. (2010). Commuting birth and death processes. The Annals of Applied Probability. Institute of Mathematical Statistics.https://doi.org/10.1214/09-AAP615 

Sturmfels, B., &; Uhler, C. (2010). Multivariate Gaussians, semidefinite matrix completion, and convex algebraic geometry. Annals of the Institute of Statistical Mathematics. Springer.https://doi.org/10.1007/s10463-010-0295-4 

Uhler, C. (2009). Mastitis in dairy production: Estimation of sensitivity, specificity and disease prevalence in the absence of a gold standard. Journal of Agricultural Biological and Environmental Statistics. Springer.https://doi.org/10.1198/jabes.2009.0005 

Green, R., Malaspinas, A., Krause, J., Briggs, A., Johnson, P., Uhler, C.,https://doi.org/10.1016/j.cell.2008.06.021__Pääbo, S. (2008). A complete neandertal mitochondrial genome sequence determined by highhhroughput sequencing. Cell. Cell Press.https://doi.org/10.1016/j.cell.2008.06.021