Dr Qinfeng Shi of the ACVT has been awarded a 3 year ARC DECRA Grant valued at $375,000.

Probabilistic Graphical Models (PGMs) use graphs to represent the interactions between random variables and provide a formalism by which to represent complex probabilistic relationships. Despite the success of PGMs in many fields, the learning on real industrial large scale applications is very slow. I will exploit the sparsity and compressibility in PGMs, and turn the large scale PGMs to a number of small scale PGMs. Solving these small scale PGMs and then reversely recover the solutions in the original large scale PGMs in the context of Compressive Sensing. This way, I can effectively deal with large scale PGMs in the computational complexity of small scale PGMs as well as provide theoretical guarantees on the consistency of the solution.