
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


Vaughan Jones: “What is it about the Plane?”

Brian Browning: "The one penny imputed genome: Rapid genotype imputation with millions of samples"

Lily Khadjavi: "Policing in Los Angeles and the issue of racial profiling"

Rodrigo Bañuelos: "Events of Small Probabilities Do Happen"

Ryan Hynd: "Adhesion dynamics and the sticky particle system"

Rochelle Gutiérrez: "Rehumanizing Mathematics: A Vision for the Future"

Panel Discussion: Latinx in STEM Disciplines and Higher Education

Jesús de Loera: "Variations of Carathéodory theorem for Integer Optimization"

Ivelisse Rubio: "Exploring, generalizing and applying the covering method"

Edgar Lobaton: "From Cyborg-Insect to Human Motion: Topological Data Analysis..."

Su-In Lee: "Interpretable Machine Learning for Precision Medicine"

Sushmita Roy: "Regulatory network inference on developmental and evolutionary lineages"

Casey Greene: "Deep learning: privacy preserving data sharing along with some hints and tips"

Manuel Rivas: "Large-scale inference across population biobanks"

Noah Zaitlen: "A Short Tutorial on Linear Mixed Model Association Testing in Genetics"

Hae Kyung Im: "Prediction and Dissection of Complex Traits"

William Noble: "Machine learning methods for making sense of big genomic data"

Yanjun Qi: "Making Deep Learning Interpretable for Analyzing Sequential Data about Gene Regulation"

John Novembre: "Computational tools for understanding geographic structure in genetic..."
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