Abstract: In survival analysis, estimating the conditional survival function given predictors is often of interest. There is a growing trend in the development of deep…
Abstract: In this talk, I will present recent advances in developing manifold learning algorithms for biomedical data analysis that explicitly account for underlying structural information.…
Abstract: Teaching recommendations for implementing data science at the introductory level include placing greater emphasis on predictive modelling. In contrast to how simple linear regression…
Abstract: Identifying covariates that modify treatment effects is a critical problem in causal inference. Yet existing data-adaptive methods lack rigorous error control, risking spurious findings…
Abstract: We consider linear regression with covariates that are separable random elements in a general Hilbert space. We first develop a principal component analysis for…
Regression is a workhorse for analyzing data from randomized experiments, adaptive trials, and related designs, where valid inference typically relies on {\it robust standard errors}.…
Abstract: This talk addresses reinforcement learning (RL) in the physical world where dynamics exhibit smooth transitions. The challenge is to we want to control the…