Talks

The Heisenberg algebra plays an important role in many areas of mathematics and physics. Khovanov constructed a categorical analogue of this algebra which emphasizes its connections to representation theory and combinatorics. Recently, Brundan, Savage, and Webster have shown that the Grothendieck group of this category is isomorphic to the Heisenberg algebra. However, applying an alternative decategorification functor called the trace to the Heisenberg category yields a richer structure: a W-algebra, an infinite-dimensional Lie algebra related to conformal field theory. In this talk, we will describe recent work extending this latter result to a generalized version of the Heisenberg category associated to an arbitrary graded Frobenius algebra F. The decategorifications of this Frobenius Heisenberg category conjecturally provide connections to alternative Heisenberg algebras and W-algebras associated to the algebra F. This is joint work with Alistair Savage (University of Ottawa).

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.