MIT EECS - Draper Laboratory Undergraduate Research and Innovation Scholar
Estimators for Sources of Information in Networks
In the age of electronic social networks in which unprecedented amounts of data are are available, understanding and extracting information from networks has become a natural necessity. The goal of this project area is to develop estimators for the source of some information (think of it as a gossip) based on a snapshot of the network at some point in the future. Understanding the performance of source estimators in network classes can contribute to understanding how information flows through a network and how much information one can extract from them.
I worked with Professor Muriel Medard and Mr Ulric Ferner on using the Theory of Network Coding on Content Distribution Networks. Developed and implemented a mathematical model to measure and improve the accuracy, through Machine Learning techniques, of deliveries in AmazonFresh, Amazons grocery delivery service. I worked in RWTH Aachen Theoretical Computer Science Department during the summer of 2010 building efficient graph-theory algorithms to be compared with the results expected from Courcelles Theorem.