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arXiv:2310. 14448v2 [stat. ME] 15 May 2024 Department of Mathematics and Herbert Wertheim School of Public Health and Halicioglu Data Science Institute University of California, San Diego
Meet the Team | AI-READI I'm a Professor of Cognitive Science and the Halıcıoğlu Data Science Institute at UC San Diego, where my lab takes a data science approach to cognitive neuroscience: leveraging many, large, heterogeneous neural datasets to understand the physiology of human cognition For AI-READI, I develop and run education and training modules in data science, machine learning, and artificial intelligence
Dr. Rajesh Gupta - Jio Institute Discover the work of Rajesh Gupta, renowned computer science and engineering professor and Founding Director of the Halıcıoğlu Data Science Institute at UC San Diego
D. N. Politis C. V. - University of California, San Diego DIMITRIS N POLITIS Distinguished Professor Education 1990 Stanford University, Ph D in Statistics 1990 Stanford University, M S in Statistics 1989 Stanford University, M S in Mathematics 1985 Rensselaer Polytechnic Institute, M S in Computer and Systems Engineering 1984 University of Patras, B S in Electrical Engineering Positions 2021-present Halicioglu Data Science Institute
Virginia R. de Sa - OpenReview Virginia R de Sa Associate Director, Halicioglu Data Science Institute, Halicioglu Data Science Institute, University of California, San Diego Full Professor, Cognitive Science, University of California, San Diego Joined November 2017
Development, validation, and transportability of several machine . . . Measured VO<sub>2max< sub> is a strong predictor of mortality Using ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to adjustment Future studies should seek to reproduce these results so that VO<sub>2max< sub> …
High-dimensional inference for dynamic treatment effects Estimating dynamic treatment effects is a crucial endeavor in causal inference, particularly when confronted with high-dimensional confounders Doubly robust (DR) approaches have emerged as promising tools for estimating treatment effects due to their flexibility However, we showcase that the traditional DR approaches that only focus on the DR representation of the expected outcomes may fall
Supporting Information ¶Halicio ̆glu Data Science Institute, University of California San Diego,
arXiv:2310. 14448v1 [stat. ME] 22 Oct 2023 Department of Mathematics and Herbert Wertheim School of Public Health and Halicioglu Data Science Institute University of California, San Diego