Selected Talk Slides:

[12] Likelihood Based Inference in Fully and Partially Observed Exponential Family Graphical Models with Intractable Normalizing Constants (June 2024). [slides]

[11] Bayesian Covariate-Dependent Quantile Directed Acyclic Graphical Models for Individualized Inference (June 2023). [slides]

[10] Graphical Evidence (June 2022). [slides] [video]

[9] Bayesian Robust Learning in Chain Graph Models for Integrative Pharmacogenomics (March 2022). [slides]

[8] Beyond Matérn: on the class of confluent hypergeometric covariance functions for Gaussian process modeling (December 2020). [slides]

[7] Horseshoe regularization for machine learning in complex and deep models (August 2019). [slides]

[6] The graphical horseshoe estimator for inverse covariance matrices (May 2018). [slides]

[5] Default Bayes and prediction problems with global-local shrinkage priors (November 2016). [slides]

[4] The horseshoe+ estimator of sparse signals (January 2015). [slides]

[3] Bayesian feature selection in high-dimensional regression in presence of correlated noise (July 2014). [slides]

[2] Joint high-dimensional Bayesian variable and covariance selection with an application to eQTL analysis (October 2012). [slides]

[1] Simulation-based maximum likelihood inference for partially observed Markov process models (February 2012). [slides]

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