
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


Serena Yeung - Computer Vision for Segmenting & Analyzing Subcellular Components in CE Tomography

Katerina Naydenova - Molecular structure extrapolation to zero dose with cryoEM - IPAM at UCLA

Petar Petrov - Laser phase-contrast transmission electron microscopy & computational opportunities

Chris Russo - Potential electron cryomicroscopy in situ: technology to identify molecules in cells

Roy Lederman - Approaches for Exploring the Geometry of Molecular Conformations in Cryo-EM

Ellen Zhoung - Machine learning for determining protein structure and dynamics from cryo-EM images

Geoffrey Woollard - Stochastic inference and Computational Optimal Transport in 3D heterogeneity

Carlos Oscar Sorzano - Machine learning for Single Particle Analysis by Cryo-Electron Microscopy

Steven Ludtke - Contextual Conformational Variability in CryoEM and CryoET using Deep Learning

Yimo Han - Assisting 4D-STEM data processing by machine learning and Bayesian optimization

Peng Wang - Electron Ptychography: Emerging Computational Microscopy for Physical/Biological Science

Andrew Maiden - Ptychographic imaging via set projection algorithms - IPAM at UCLA

Sandra van Aert - 3D atomic resolution through dose-efficient fusion of image & analytical technique

Emilie Ringe - Electron and optical microscopy of magnesium nanoparticles - IPAM at UCLA

Soledad Villar - Introduction to equivariant machine learning - IPAM at UCLA

Yonina Eldar - Model Based Deep Learning with Application to Super Resolution - IPAM at UCLA

Yongsoo Yang - Neural network-assisted atomic electron tomography - IPAM at UCLA

Emily King - PCA is not dead: Vectorized persistent homology and flag medians - IPAM at UCLA

Ross Harder - Bragg Coherent Diffraction Imaging at the Advanced Photon Source 34-ID Beamline
Follow this podcast in Podwise
Sign in to get AI summaries, transcripts and mind maps for any episode, including new ones.
