MIT EECS - Draper Computer Undergraduate Research and Innovation Scholar
Information Measures On Directed Graphs
An observation which makes sense in our day to day activities is that filtering or processing of data can only reduce the amount of information available. A nice formal analogue of this statement in information theory is the data processing inequality for mutual information on Markov chains. More general dependency structures among random variables arise in a variety of fields such as digital communications, inference, and machine learning. This work aims at identifying suitable functionals for which data processing inequality analogues hold over a wider range of dependency structures among random variables. This work also aims at exploring interesting consequences of such data processing inequality analogues
I worked on probabilistic graphical models in the context of seismic image analysis with Professor Alan Willsky’s group over IAP and Spring 2013. Over IAP and Spring 2014, I worked on problems in information theory with Professor Polyanskiy.