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 | Dependence Measurements 1) Mutual Information In probability theory and information theory, the mutual information (MI) of two random elements is a measure of the mutual dependence between the two random elements. It measures how much knowing one of these random elements reduces uncertainty about the other. 2) Copulas In probability theory and statistics, a copula is a multivariate probability distribution for which the marginal probability distribution of each variable is uniform. Copulas are used to describe the dependence between random variables. Copulas have been used widely in many scientific fields. In econometrics and quantitative finance, Copulas have been used to model the dependence structures in financial markets, tail risk, portfolio-optimization applications, etc. Authorship Attribution Authorship attribution is the process of determining the author of a text in question by capturing an author's writing style based on selected stylistic features. Authorship attribution has numerous applications such as plagiarism detection, cyber-crime investigation, and social media forensics. My research in this area mainly includes the following two areas. 1) Authorship attribution for traditional works like novels, fictions, etc. 2) Authorship attribution in social media. Deep Learning Deep learning is a sub-field of machine learning based on artificial intelligence that has networks capable of learning from unstructured data without human supervision. It mimics the working of human brains in processing data. It has been successfully used in many aeras such as computer vision, natural language processing, speech recognition, etc. |