Jef Caers, Stanford University
Professor
Stanford, California
jcaers@stanford.edu
Office:
(650) 723-1774
Bio/Research
My research occurs at the intersection of statistical science, computer science and the geological sciences. What is the fundamental research question I want to address? I believe that from a data-scientific point of view, most geological data and modeling questions can be broadly classified as p...
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Bio/Research
My research occurs at the intersection of statistical science, computer science and the geological sciences. What is the fundamental research question I want to address? I believe that from a data-scientific point of view, most geological data and modeling questions can be broadly classified as problems that are high-dimensional but have small sample size. Data are often sparse and computer experiments we run are CPU demanding, resulting in some low sample size. Yet the understanding we attempt to develop requires complex physical or geochemical models, analysis of multivariate, spatial problems over potentially large areas, require aggregation of data at various scale (in space and time) and hence are high dimensional problems. How do we formulate such problems? What are fundamental mathematical and computer science methods for analyzing such problems? How can we build predictive models for such problems? How do we integrate the various disciplines involved? Most of machine learning and statistics research currently does not take place in this setting.
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