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Texas A&M University
Mathematics

Events for 02/22/2023 from all calendars

I and T Committee Meeting

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Time: 4:00PM - 5:00PM

Location: Bloc 117


Topology Seminar

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Time: 4:00PM - 5:00PM

Location: BLOC 628

Speaker: Ruobing Zhang, Princeton University

Title: (Joint with Noncommutative Geometry Seminar) Metric geometry of Einstein 4-manifolds with special holonomy

Abstract: Studying the geometry of Einstein spaces and their generalizations is a major theme in current areas of differential geometry. This talk will introduce major developments in the metric geometry and global analysis of Einstein 4-manifolds. Recently geometric studies have culminated in a complete understanding of Einstein 4-manifolds with special holonomy. Among the tremendous progress in this area, an essential chapter is to understand how Einstein moduli spaces can be compactified and how Einstein metrics can degenerate, especially when volume collapse occurs. Understanding collapsing geometry is much more challenging than studying the non-collapsing case so that little progress was made during the past three decades. I will particularly summarize recent breakthrough in this direction on the accurate characterization of singularity formation and the complete classification of degeneration limits on all scales.


Noncommutative Geometry Seminar

iCal  iCal

Time: 4:00PM - 5:00PM

Location: BLOC 628

Speaker: Ruobing Zhang, Princeton University

Title: (Joint with Topology Seminar) Metric geometry of Einstein 4-manifolds with special holonomy

Abstract: Studying the geometry of Einstein spaces and their generalizations is a major theme in current areas of differential geometry. This talk will introduce major developments in the metric geometry and global analysis of Einstein 4-manifolds. Recently geometric studies have culminated in a complete understanding of Einstein 4-manifolds with special holonomy.

Among the tremendous progress in this area, an essential chapter is to understand how Einstein moduli spaces can be compactified and how Einstein metrics can degenerate, especially when volume collapse occurs. Understanding collapsing geometry is much more challenging than studying the non-collapsing case so that little progress was made during the past three decades. I will particularly summarize recent breakthrough in this direction on the accurate characterization of singularity formation and the complete classification of degeneration limits on all scales.


AMUSE

iCal  iCal

Time: 6:00PM - 7:00PM

Location: BLOC 306

Speaker: Stephan Wojtowytsch, Texas A&M University

Title: Neural networks and how to train them

Abstract: The most time-consuming part of deep learning is the training of neural networks. We give a gentle introduction to data science, machine learning and deep learning. Our focus will be on training algorithms modeled on a hiker lost in the fog (gradient descent) and a heavy ball rolling down a mountainside (gradient descent with momentum) and their advantages.