Diana Cai will present her General Exam on Thursday, April 25, 2019 at 12:30pm in CS 105. The members of her committee are: Ryan Adams (adviser), Barbara Engelhardt (adviser), and Tom Griffiths. Everyone is invited to attend her talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Her abstract and reading list follow below. Title: Robust and Reliable Probabilistic Modeling Abstract: Probabilistic modeling is a pillar of modern statistical data analysis and has proved to be particularly salient for extracting interpretable latent structure in scientific and engineering applications, and the resulting inferences are an important part of decision making. Thus, it increasingly important to develop models that are interpretable, robust, deployable at scale. Robust probabilistic modeling is an important problem, as the stability of the model’s inferences directly impacts the decision making process. A natural way of providing robustness is to use a flexible modeling framework that encompasses a wide class of models. Bayesian nonparametric modeling provides a flexible probabilistic framework for learning from data, where the number of parameters of the model grows with the number of observations. We discuss a few applications of Bayesian nonparametrics, including for sparse network modeling and a Bayesian interpretation of the count-min sketch. Lastly, we outline a few research directions in robust and reliable probabilistic modeling, including probabilistic modeling under model misspecification and inference under computational constraints. Reading list: Bishop. Pattern Recognition and Machine Learning. Springer, 2006. Ferguson. A Bayesian analysis of some nonparametric problems. Annals of Statistics, 1973. Blackwell and MacQueen. Ferguson Distributions via Polya Urn Schemes. Annals of Statistics, 1973. Broderick, Jordan, and Pitman. Beta processes, stick breaking, and power laws. Bayesian Analysis, 2012. Caron and Fox. Sparse graphs using exchangeable random measures. Journal of the Royal Statistical Society: Series B, 2017. Cormode and Muthukrishnan. An Improved Data Stream Summary: The Count-Min Sketch and its Applications. Journal of Algorithms, 2005. Miller and Harrison. A simple example of Dirichlet process mixture inconsistency for the number of components. NeurIPS, 2013. Miller and Harrison. Mixture models with a prior on the number of components. Journal of the American Statistical Association, 2018. Miller and Dunson. Robust Bayesian modeling via coarsening. Journal of the American Statistical Association, 2018. Hennig. Probabilistic interpretation of linear solvers. SIAM Journal on Optimization, 2015. Cockayne, Oates, Ipsen, Girolami. A Bayesian Conjugate Gradient Method. Bayesian Analysis, 2019.