Amir Ghasemian

Amir Ghasemian is a CIFellow in HNL at Yale University, working with Nicholas Christakis and Edoardo Airoldi. He received his PhD degree in Computer Science from University of Colorado Boulder under the supervision of Aaron Clauset. He is also an Affiliate Research Scientist in CSSLab at UPenn.

His current research involves inference and learning in complex networks. More specifically he is interested in looking at inference problems from different angles like Bayesian approaches in statistics, free energy in statistical physics and information theoretic perspective from electrical engineering and physics. He is interested in complex networks analysis and modeling using probabilistic graphical models, machine learning techniques, non-parametric models, information theory, signal processing, optimization, and linear algebra.