The development of computational data science techniques in natural language processing (NLP) and machine learning (ML) algorithms to analyze large and complex textual information opens new avenues to study intricate processes, such as government regulation of financial markets, at a scale unimaginable even a few years ago. This project develops scalable NLP and ML algorithms (classification, clustering and ranking methods) that automatically classify laws into various codes/labels, rank feature sets based on use case, and induce best structured representation of sentences for various types of computational analysis.
Understand interconnected nature of global multi-national companies via their supply chain, product and services competition, co-investments and co-ownerships as well as other dependencies between operations and revenue streams. We would like to consider the way news on any company specifically propagate down the connection graph and impact other businesses that are related in a way that is not necessarily explicit.
Given calcium imaging data of active neurons, can we detect groups of co-firing neurons, called neuronal ensembles? We have a number of datasets consisting of hundreds of neurons imaged for thousands of time steps, and seek to extend an existing CRF model to consider temporal relationships. The goal is to be able to detect neuronal ensembles that span multiple time steps, and that are not conditioned on external stimuli.