Physics

Many of the rich interactions between mathematics and physics arise using general mathematical frameworks that describe a host of physical phenomena: from differential equations, to algebra, to topology and geometry. On the other hand, mathematics also possesses many examples of "exceptional objects": they constitute the finite set of leftovers that appear in numerous classification problems. For example, groups of symmetries in three dimensions appear in two infinite families (cyclic groups and dihedral groups of n-sided polygons) and the symmetry groups of the five Platonic solids--- the 'exceptional' structures. 

The mathematical subject of moonshine refers to surprising relationships between other kinds of special/exceptional objects that arise from the theory of finite groups and from number theory. Increasingly, string theory has been a source of insights in and explanations for moonshine. It is even the source of new examples of moonshine that further implicate special objects in geometry. We will review moonshine, survey these developments, and highlight some of the (many!) exciting mysteries that remain.

The cosmic microwave background radiation has been an indispensable tool for learning about the origins and evolution of our Observable Universe. Satellites and ground based experiments measuring the temperature and polarization anisotropies with ever increasing angular resolution and sensitivity have established the standard cosmological model, LCDM, and constrained or ruled out a huge variety of theoretical models of the early Universe. Most of this cosmological information has thus far been derived from the primary CMB, anisotropies largely sourced during the epoch of recombination some 380,000 years after the Big Bang. However, experiments are achieving the sensitivity and resolution necessary to access a whole new treasure-trove of cosmological information: secondary temperature and polarization anisotropies induced by photons scattering from mass (CMB lensing) and free electrons (the Sunyaev Zel'dovich effect). Unlike the primary CMB, where cosmological information is contained in the pattern of fluctuations in statistically isotropic temperature/polarization anisotropies, the secondary CMB encodes cosmological information in the statistical anisotropies of non-gaussian temperature/polarization fields on the sky. In this talk, I will describe the possibilities for future CMB experiments, in concert with large galaxy surveys, to reconstruct the cosmological information contained in the statistics of the secondary CMB anisotropies. In particular, I will describe the information that can be reconstructed from kinetic/polarized Sunyaev Zel'dovich effect, CMB lensing, and the moving lens effect. I speculate on what we might learn from future measurements, and conclude by discussing several projects in progress.

Sexual harassment and sexual assault in the workplace is almost always a severe betrayal of trust.  I will describe research and theory that my students and I have developed over the last 25 years regarding interpersonal and institutional betrayals of trust.  My presentation will include an explanation of betrayal trauma theory and information about institutional betrayal.   I will present data from some of our research studies, including results from a study of sexual harassment of graduate students.   Included will be research-based recommendations for how to respond well to disclosures of harassment and sexual violence as well as steps individuals and institutions can take to constructively address sexual violence and promote institutional courage.  

Jennifer J. Freyd, PhD, is a Fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University and a Professor of Psychology at the University of Oregon.

From earliest infancy, we live in and learn to function in a world of causes and effects. Yet science has had an ambivalent, even hostile attitude toward causation for more than a century. Statistics courses teach us that “correlation is not causation,” yet they are strangely silent about what is causation.

A central reason for this silence is that causation does not reside in data alone, but in the process that generates the data. In order to answer causal questions, like “What would happen if we lowered the price of toothpaste?” or “Should I brake for this object?” we need a model of causes and effects. Judea Pearl has developed a simple calculus for expressing our cause-effect knowledge in a diagram and using that diagram to tell us how to interpret the data we gather from the real world. His methods are already transforming the practice of statistics and could equip future artificial intelligences with causal reasoning abilities they currently lack.

This talk is largely based on Mackenzie’s book co-written with Pearl, The Book of Why.

While it’s undeniably sexy to work with infinite-dimensional categories “model-independently,” we contend there is a categorical imperative to familiarize oneself with at least one concrete model in order to check that proposed model-independent constructions interpret correctly. With this aim in mind, we recount the n-complicial sets model of (∞,n)-categories for 0 ≤ n ≤ ∞, the combinatorics of which are quite similar to its low-dimensional special cases: quasi-categories (n=1) and Kan complexes (n=0). We conclude by reporting on an encounter with 2-complicial sets in the wild, where a suitably-defined fibration of 2-complicial sets enables the comprehension construction introduced in joint work with Verity. Special cases of the comprehension construction can be used to “straighten” a co/cartesian fibration of (∞,1)-categories into a homotopy coherent functor, exhibit a quasi-categorical version of the “unstraightening” construction, and define an internal model of the Yoneda embedding for (∞,1)-categories.