Bài giảng đại chúng "How Artificial Intelligence and Deep Learning is Changing Medicine?"
Thời gian: 14:00 đến 15:30 Ngày 26/04/2019
Địa điểm: C2-714, VIASM
Báo cáo viên: Prof. Jean-Daniel Zucker
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About Prof. Kathryn Hess, École polytechnique fédérale de Lausanne, France
Webpage: https://hessbellwald-lab.epfl.ch/hessbellwald/
Biography:
Kathryn Hess is a professor of mathematics at École Polytechnique Fédérale de Lausanne (EPFL) and is known for her work on homotopy theory, category theory, and algebraic topology, both pure and applied. In particular, she applies the methods of algebraic topology to better understanding neuroscience, cancer biology, and materials science. She is a fellow of the American Mathematical Society.
Hess has worked and written extensively on topics in algebraic topology including homotopy theory, model categories and algebraic K-theory. She has also used the methods of algebraic topology and category theory to investigate homotopical generalizations of descent theory and Hopf–Galois extensions. In particular, she has studied generalizations of these structures for ring spectra and differential graded algebras.
She has more recently used algebraic topology to understand structures in neuroscience and materials science.
Hess received the Polysphere d'Or Teaching Award for her teaching at EPFL in 2013. In 2017, she was named a fellow of the American Mathematical Society for "contributions to homotopy theory, applications of topology to the analysis of biological data, and service to the mathematical community". In 2017, she received an award as a distinguished speaker of the European Mathematical Society.
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List of articles on these subjects:
- M. W. Reimann, M. Nolte, M. Scolamiero, K. Turner, R. Perin, G. Chindemi, P. Dlotko, R. Levi, K. Hess, and H. Markram, Cliques of neurons bound into cavities provide a missing link between structure and function, Front. Comput. Neurosci., 12 June 2017, doi: 10.3389/fncom.2017.00048.
Cliques of neurons bound into cavities provide a missing link between structure and function
- L. Kanari, P. Dlotko, M. Scolamiero, R. Levi, J. C. Shillcock, K. Hess, and H. Markram, A topological representation of branching morphologies, Neuroinformatics (2017) doi: 10.1007/s12021-017-9341-1.
A topological representation of branching neuronal morphologies
- J.-B. Bardin, G. Spreemann, K. Hess, Topological exploration of artificial neuronal network dynamics, Network Neuroscience (2019) https://doi.org/10.1162/netn\_a\_00080.
Topological exploration of artificial neuronal network dynamics
- L. Kanari, S. Ramaswamy, Y. Shi, S. Morand, J. Meystre, R. Perin, M. Abdellah, Y. Wang, K. Hess, and H. Markram, Objective classification of neocortical pyramidal cells, Cerebral Cortex (2019) bhy339, https://doi.org/10.1093/cercor/bhy339.
Objective morphological classification of neocortical pyramidal cells
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Few videos that may be of interest as well, ordered from least to most mathematical:
The very short, very vague introduction to our work, filmed by the EPFL: https://youtu.be/ZQTqvv6HHHY
A TEDx talk I gave a couple of years ago: https://youtu.be/uQKrCKy3h1E
A series of excellent videos produced by PBS on our work:
https://youtu.be/M0M3srBoTkY, https://youtu.be/rlI1KOo1gp4, https://youtu.be/akgU8nRNIp0
A talk I gave to an audience of applied topologists two years ago: https://youtu.be/vD27zKxoio0