On the Interplay of Optimal Transport and Distributionally Robust Optimization Optimal Transport (OT) seeks the most efficient way to morph one probability distribution into another one, and Distributionally Robust Optimization (DRO) studies worst-case risk minimization problems under distributional ambiguity. It … Read more
To Adjust or not to Adjust? Estimating the Average Treatment Effect in Randomized Experiments with Missing Covariates Randomized experiments allow for consistent estimation of the average treatment effect based on the difference in mean outcomes without strong modeling assumptions. Appropriate … Read more
Universality of regularized regression estimators in high dimensions The study of regularized regression estimators is of fundamental importance in high dimensional inference. From the canonical linear model to many of its extensions, a by now well-established type of analysis has … Read more
Harnessing Extra Randomness: Replicability, Flexibility and Causality Many modern statistical procedures are randomized in the sense that the output is a random function of data. For example, many procedures employ data splitting, which randomly divides the dataset into disjoint parts … Read more
Statistical Tools for Inference on Samples of Networks with Applications to Neuroimaging Networks are an increasingly common data structure, and their growing popularity demands the development of new statistical methodology. In this talk I'll discuss several projects motivated by applications … Read more
Energetic Variational Inference In this talk, I plan to highlight one component of my on-going research, energetic variational inference. Variational Inference (VI) is an important research area in the field of machine learning. Many VI approaches have been developed and … Read more
Neural Collapse in Deep Net Training Modern deep neural networks are structurally complex and massively over-parameterized. One might then expect these networks to exhibit many particularities with little regularity across architectures and applications. However, through extensive experiments on contest winning … Read more
Uncovering the Interpretability and Identifiability of Gaussian Processes: From Application to Theory Gaussian processes (GPs) are widely employed as versatile modeling and predictive tools in spatial statistics, functional data analysis, computer modeling and diverse applications of machine learning. While … Read more
Analysis of Covariance: Model-based and Randomization-based For randomized clinical trials with at least moderate sample size, adjustment of comparisons between treatments for baseline covariables can be helpful for two reasons. One is enhancement of power, and the other is … Read more
Existence of MLEs for the parameters of an adjusted linear preferential attachment model of random graphs with covariates In this talk, we consider a variation on the preferential attachment model (PAM) of random graphs with covariates. It is similar to … Read more
Every Statistical Problem is a Missing Data/Causal Inference Problem Across numerous fields of domain science research, challenges arise routinely where data of interest are missing and/or inferences are required regarding, for example, the causal effects of treatments based on observational … Read more
