
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


Joseph Landsberg: "Introduction to the Geometry of Tensors (Part 2/2)"

David Gross: "Stabilizer Formalism & Quantum Error Correction Through the Lens of Tensors (Pt 2/2)"

Anna Seigal: "Tensors in Statistics and Data Analysis"

Lek-Heng Lim: "What is a tensor? (Part 2/2)"

Ankur Moitra: "Tensor Decompositions and their Applications (Part 1/2)"

Frank Verstraete: "Mathematical Aspects of Tensor Networks (Part 1/2)"

Ming Yuan: "Low Rank Tensor Methods in High Dimensional Data Analysis (Part 2/2)"

Joseph Landsberg: "Introduction to the Geometry of Tensors (Part 1/2)"

Lek-Heng Lim: "What is a tensor? (Part 1/2)"

David Gross: "Stabilizer Formalism & Quantum Error Correction Through the Lens of Tensors (Pt 1/2)"

Anna Seigal: "From Linear Algebra to Multi-Linear Algebra"

Ming Yuan: "Low Rank Tensor Methods in High Dimensional Data Analysis (Part 1/2)"

Sanjeev Arora: "How to allow deep learning on your data without revealing your data"

Tina Eliassi-Rad: "Task-driven Network Discovery via Deep Reinforcement Learning on Embedded Spaces"

Louis-Martin Rousseau: "Combining Reinforcement Learning & Constraint Programming for Combinator..."

Stefanie Jegelka: "Task structure and generalization in graph neural networks"

Bistra Dilkina: "Decision-focused learning: integrating downstream combinatorics in ML"

Stefano Gualandi: "Discrete Optimal Transport by Parallel Network Simplex"

Zico Kolter: "Fast semidefinite programming for (differentiable) combinatorial optimization"
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