PCL investigates the mathematical and conceptual foundations of probabilistic computation, including new ways of understanding probability as a computational resource and its relationship to physical and information-processing systems.

This work builds on a program of foundational research initiated by PCL Principal Scientist Raed Shaiia in 2012, while extending that work to explore new theoretical structures, their limits, and their potential to support new forms of computation. 

PCL investigates probabilistic approaches to computationally difficult problems, with particular emphasis on optimization, scientific computing, and engineering analysis.

Research in this area explores whether probabilistic methods can provide new approaches to problems traditionally addressed through deterministic computation, with the goal of developing computational methods capable of addressing increasingly complex scientific and engineering problems.

PCL investigates how probabilistic methods can be applied to the modeling of complex molecular and physical systems.

Research in this area explores new computational approaches to representing and analyzing systems whose complexity makes conventional modeling increasingly challenging.

PCL investigates the role of probabilistic computation in artificial intelligence and complex decision-making systems.

Research in this area explores computational approaches designed to operate under uncertainty, incomplete information, and increasing system complexity.