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Institute for Pure & Applied Mathematics (IPAM) · Science

Institute for Pure & Applied Mathematics (IPAM)

The Institute for Pure and Applied Mathematics (IPAM) is a National Science Foundation Math Institute at the University of California, Los Angeles (UCLA) that fosters the interaction of mathematics with a broad range of science and technology, builds new interdisciplinary research communities, promotes mathematical innovation, and engages and transforms the world through mathematics. IPAM fulfills its mission through workshops and programs that connect mathematics and other disciplines or multiple areas of mathematics. These activities bring in thousands of visitors annually from academia, government, and industry. The NSF has encouraged IPAM to make the scientific presentations of our speakers available to a wider audience. While this YouTube channel contains many of our most popular videos, our full video archive is available at www.ipam.ucla.edu/videos.

Episodes

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Vaughan Jones: “God May or May Not Play Dice but She Sure Loves a von Neumann Algebra”

10 May 2018
1h 7m
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Vaughan Jones: “What is it about the Plane?”

10 May 2018
1h 0m
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Brian Browning: "The one penny imputed genome: Rapid genotype imputation with millions of samples"

27 Apr 2018
50m
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Lily Khadjavi: "Policing in Los Angeles and the issue of racial profiling"

16 Mar 2018
47m
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Rodrigo Bañuelos: "Events of Small Probabilities Do Happen"

16 Mar 2018
48m
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Ryan Hynd: "Adhesion dynamics and the sticky particle system"

16 Mar 2018
45m
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Rochelle Gutiérrez: "Rehumanizing Mathematics: A Vision for the Future"

16 Mar 2018
56m
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Panel Discussion: Latinx in STEM Disciplines and Higher Education

16 Mar 2018
1h 22m
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Jesús de Loera: "Variations of Carathéodory theorem for Integer Optimization"

16 Mar 2018
53m
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Ivelisse Rubio: "Exploring, generalizing and applying the covering method"

16 Mar 2018
53m
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Edgar Lobaton: "From Cyborg-Insect to Human Motion: Topological Data Analysis..."

16 Mar 2018
43m
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Su-In Lee: "Interpretable Machine Learning for Precision Medicine"

08 Mar 2018
52m
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Sushmita Roy: "Regulatory network inference on developmental and evolutionary lineages"

08 Mar 2018
47m
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Casey Greene: "Deep learning: privacy preserving data sharing along with some hints and tips"

07 Mar 2018
43m
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Manuel Rivas: "Large-scale inference across population biobanks"

07 Mar 2018
46m
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Noah Zaitlen: "A Short Tutorial on Linear Mixed Model Association Testing in Genetics"

07 Mar 2018
44m
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Hae Kyung Im: "Prediction and Dissection of Complex Traits"

07 Mar 2018
41m
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William Noble: "Machine learning methods for making sense of big genomic data"

07 Mar 2018
46m
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Yanjun Qi: "Making Deep Learning Interpretable for Analyzing Sequential Data about Gene Regulation"

07 Mar 2018
52m
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John Novembre: "Computational tools for understanding geographic structure in genetic..."

07 Mar 2018
46m
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