Markov Chain Monte Carlo (MCMC) and Clustering: MCMC is widely used in computational physics as an optimization method to solve physics models. Clustering is one kind of unsupervised learning in machine learning, and some of the clustering problems can be solved by optimization. As an optimization method, MCMC can be applied to a variety of clustering problems. For instance, Word Sense Disambiguation (WSD) is one of the most important topics in the field of Natural Language Processing. In order to figure out how many different meanings an ambiguous word can refer to automatically, it can be treated as an clustering problem and then converted into an optimization problem with a customized objective function. MCMC is perfect methodology for optimization problem like this. How to scale this solution for Big Data is another interesting topic. Dr Luo has designed and implemented a large-scale disambiguation system to identify and disambiguate multi-sense skills, using Markov Chain Monte Carlo (MCMC). More details can be found in his paper (Macau: Large-Scale Skill Sense Disambiguation in the Online Recruitment Domain).