I am a Postdoctoral Research Fellow in the Department of Statistics at Temple University, working with Edoardo Airoldi. I received my PhD degree in Computer Science from University of Colorado Boulder under the supervision of Aaron Clauset.
My research interests lie in network science, statistical inference, causal inference, information theory, machine learning, data mining, and signal processing.
My current research involves inference and learning in complex networks. More specifically I am 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. More specifically I am 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. I like to use different perspectives to analyze the same problem to understand better the problem and find the relations among different perspectives. This is the reason that I am interested to interdisciplinary areas in computer science. Mostly I would like to design and analyze models for these problems and investigate under what conditions we can have informative solutions. I am interested to statistical physics because of its unique perspective of phase transitions in making conjectures, to probability theory to find proofs of these conjectures, to statistics and information theory to model and analyze problems, and to linear algebra and signal processing to analyze and understand the geometry of the data available for problems.