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
Sufficient linear complementarity problems: algorithms and computational results Linear complementarity problems (LCP) generalize some fundamental problems of mathematical optimization like linear programming (LP) problem, linearly constrained quadratic programming (LQP) problem and some other. It admits an enormous number of applications … Read more
Models of Patient Access and Payment Parity in Telehealth In response to the increased use of telehealth to replace traditional office visits with a physician, several US states and Medicare have adopted or proposed telehealth payment parity policies. These policies … Read more
Estimation and Inference for Assortment Optimization We present two works on assortment optimization. In the first part, we consider a class of assortment optimization problems in an offline data-driven setting. A firm does not know the underlying customer choice model … Read more
