Events Archive
Filter By:

CMSA Math Science Literature Lecture Series

CMSA EVENTS

September 30, 2020      9:00 am
Speaker: Claire Voisin - Collège de France

TITLE: Hodge structures and the topology of algebraic varieties ABSTRACT: We review the major progress made since the 50’s in our understanding of the topology of complex algebraic varieties. Most of...
Read more

The Combinatorics of Rhombic Polygon Tilings

MATH TABLE

September 30, 2020      4:30 pm
Speaker: Hanna Mularczyk - Harvard Undergraduate

The geometry of rhombic tilings and tessellations like the Penrose tiling have captivated mathematicians and artists alike.  Hidden in the geometry of certain rhombic tilings of certain polygons, though, is...
Read more

Pointwise Bound for $\ell$-torsion of Class Groups

NUMBER THEORY

September 30, 2020      3:00 pm
Speaker: Jiuya Wang - Duke University

$\ell$-torsion conjecture states that $\ell$-torsion of the class group $|\text{Cl}_K[\ell]|$ for every number field $K$ is bounded by $\text{Disc}(K)^{\epsilon}$. It follows from a classical result of Brauer-Siegel, or even earlier...
Read more

CMSA Math Science Literature Lecture Series

CMSA EVENTS

September 30, 2020      12:00 pm
Speaker: Ralph Cohen - Stanford University

TITLE: Immersions of manifolds and homotopy theory ABSTRACT: The interface between the study of the topology of differentiable manifolds and algebraic topology has been one of the richest areas of work...
Read more

Algebraic Braids and Transcendental Retractions

HARVARD-MIT ALGEBRAIC GEOMETRY

September 29, 2020      3:00 pm
Speaker: Minh-Tam Trinh - MIT

If a complex, integral, projective curve C has only planar singularities, then its Jacobian admits a natural compactification with interesting topology. Work of Oblomkov, Shende, and others suggests the existence...
Read more

CMSA Math Science Literature Lecture Series

CMSA EVENTS

September 28, 2020      9:00 am
Speaker: Harry Shum - Tsinghua University

TITLE: From Deep Learning to Deep Understanding ABSTRACT: In this talk I will discuss a couple of research directions for robust AI beyond deep neural networks. The first is the need...
Read more