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Abstract: We are witnessing intolerable levels of violence across the globe. As scholars, how do we expose, articulate, and resist emerging forms of violence of…
You and a guest are cordially invited to join fellow Anteaters and ICS Dean Marios Papaefthymiou in New York City for a cocktail reception, hosted by ICS alumnus and UCI Foundation Trustee Tim Kashani (BS'86, MBA'88) and his wife, Pamela Winslow Kashani. Tim is founder and CEO of IT Mentors and Apples and Oranges Arts.
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: Research on media, technology, and neurodivergence has expanded greatly over the past decade to be more inclusive of neurodivergent (ND) adults (particularly those identifying…
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: In a very short time, AI has completely changed how we create software. In this talk, I will overview how we reached this juncture,…
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…
Abstract: In her talk, Prof. Brunswicker will depart from her work on LLM-enabled end-to-end UAV operations to envision a future in which multi-drone robotic systems…
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}.…