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In 2003 he was awarded the Minerva Golden Brain Award and was elected a Fellow of the Royal Society in 2006. In 2000 he was President of the international Organization of Human Brain Mapping. His main contribution to theoretical neurobiology is a free-energy principle for action and perception (active inference).įriston received the first Young Investigators Award in Human Brain Mapping (1996) and was elected a Fellow of the Academy of Medical Sciences (1999). Friston currently works on models of functional integration in the human brain and the principles that underlie neuronal interactions. Mathematical contributions include variational Laplacian procedures and generalized filtering for hierarchical Bayesian model inversion. These contributions were motivated by schizophrenia research and theoretical studies of value-learning, formulated as the dysconnection hypothesis of schizophrenia. He invented statistical parametric mapping (SPM), voxel-based morphometry (VBM) and dynamic causal modelling (DCM). Karl Friston is a theoretical neuroscientist and authority on brain imaging. She is serving on the editorial board of Proceedings of National Academy of Sciences (PNAS) and the scientific advisory committee of the UK Turing Institute for Data Science and AI. She is Past President of the Institute of Mathematical Statistics (IMS), Guggenheim Fellow, Tukey Memorial Lecturer of the Bernoulli Society, Rietz Lecturer of IMS, and a COPSS E.L. She is a member of the US National Academy of Sciences and of the American Academy of Arts and Sciences. She and her team develop relevant theory to understand random forests and deep learning for insight into and guidance for practice.
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Together with her group, her work has leveraged new computational developments to solve important scientific problems by combining novel statistical machine learning approaches with the domain expertise of her many collaborators in neuroscience, genomics and precision medicine. She was formally trained as a statistician, but her research extends beyond the realm of statistics. She leads the Yu Group which consists of 15-20 students and postdocs from Statistics and EECS. AI, Machine Learning, Deep Learning and info-metricsīin Yu is Chancellor's Distinguished Professor and Class of 1936 Second Chair in the departments of statistics and EECS at UC Berkeley.Topics of interest include (but not limited to) The workshop will consist of invited talks, papers’ presentations, a graduate student session, and a panel discussion. The aims of the workshop are to provide a forum for the dissemination of new research in all areas of info-metrics and to stimulate discussion between researchers, and students, across fields. The workshop will focus on info-metrics in the 21st century: recent advances, open questions and directions for future research within an interdisciplinary perspective. In honor of Shannon's 105 th Birthday, and in celebration of our recently edited book Advances in Info-Metrics: Information and Information Processing across Disciplines (Chen, Dunn, Golan, Ullah, OUP, 2021), we will have a one-day online workshop to discuss recent advances in info-metrics and information-theoretic inference.