Just to clear up further confusion: Frank was indeed at Oxford previously – he moved to UBC this Spring. The tutorial actually took place at NIPS 2015 in Montreal.
Microsoft Research does have multiple excellent researchers working on probabilistic programming. Infer.NET in particular is a highly advanced piece of technology for models in which you would use message passing algorithms to perform inference:
Systems like Anglican perform Bayesian inference in user-defined models, which are expressed as programs in a general-purpose language – in our case a subset of Clojure. An inference back end then implements a number of generic sampling-based algorithms like Metropolis-Hastings, sequential Monte Carlo and black box variational inference.
From the description, PAMELA provides a probabilistic version of a process modeling language. This is a concept that I'm personally not familiar with, but the intended use case appears to be the modeling of control systems, where inference is performed over some form of latent states in the control process: