
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


Rama Vasudevan - Advancing Microscopy with Machine Learning: Lessons from Scanning Probe Microscopy

Daniel Cremers - Deep Learning: Challenges and Perspectives - IPAM at UCLA

Michael Lustig - Multi-modal Motion Imaging using Microwave tones in an MRI scanner - IPAM at UCLA

Hanbaek Lyu - Mesoscale reconstruction of images and networks using tensor decomposition

Anna Little - Unbiasing Procedures for Scale-invariant Multi-reference Alignment - IPAM at UCLA

Naomi Ginsberg - Formation and function of assembled nanomaterials with multimodal X-ray scattering

Alberto Bartesaghi - High-throughput cryo-ET: from data acquisition to high-resolution structures

Maya Topf - Refinement and validation of atomic models in cryo-EM maps using TEMPy2 - IPAM at UCLA

Carlos Esteve Yague - Spectral decomposition of atomic structures in heterogeneous cryo-EM

Yuehaw Khoo - New approaches in simulation of transition paths - IPAM at UCLA

Sonya Hanson - Pose estimation, temperature, noise, and biological temperature sensors via cryo-EM

Abhishek Singharoy - Cryo-EM: Ensemble refinement, free-energy landscapes and molecular dynamics

Pilar Cossio - Structural-ensemble probability refinement using Cryo-EM particles - IPAM at UCLA

Ayelet Heimowitz - Center of Mass Alignment for Noisy Tomographic Projections - IPAM at UCLA

Tristan Bepler - Detection of objects in cryo-electron micrographs using geometric deep learning

Frederick Poitevin - Inference Poses for Reconstruction of 3D Molecular Volumes from Cryo-EM Images

Joe Kileel - Method of moments in cryo-EM - IPAM at UCLA

Tamir Bendory - Recovering small molecular structures using cryo-EM - IPAM at UCLA

Mikhail Kudryashev - High throughput, high performance algorithms for high res subtomogram averaging
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