An active line of research in proof complexity over the last decade has been the study of proof space and trade-offs between size and space.

We consider two different models for adversarial errors and show how to design circuits that can recover from such errors. 

Change of measure techniques are ubiquitous in many areas of applied probability including in model selection in finance and in efficient simulation of rare events.

Problems in Metric embedding involve mapping a set (for our purposes, finite) of points from one metric space to another, while preserving pairwise distances.

What agents do depends not only on what they desire but also on what they believe and what they believe others will do. This makes it possible to influence agents by revealing or withholding information.