Vasu Raghavan

Text Analytics is a discipline that employs different algorithms to analyze text documents. In this introductory talk on Text Analytics, we will discuss briefly about the topic modeling method called Latent Dirichlet Allocation (LDA) and its applications. LDA models a generative process that represents documents as mixtures of topics and topics are modeled as a distribution of words. Topics and distributions are latent in the sense that they are not observed. LDA infers the clusters of topics and the distributions from the observed documents and the words contained in them. LDA and other Text Analytics algorithms find applications in many disciplines which include genetics, documents classification, legal, annotating images, and recommendation systems.

Biography:

Vasu Raghavan is a mathematician, a computer scientist, and a data scientist. He has a Master’s degree in Mathematics, a Master’s degree in Computer Science, both from reputed universities in India, and a Master’s degree in Predictive Analytics from Northwestern University, Evanston, Illinois. He has over thirty five years of experience in applied software research and more than five years of experience in data Science. He started his career as a developer of operating systems for one of India’s indigenous computer manufacturers. Then he participated in India’s effort to modernizing telecommunication network. He designed and developed software for implementing wide area data communication at Center for Development of Advanced Computing (formerly called Electronic Research and Development Center, Trivandrum, India).  He also developed software for Digital Signal Processing. His current assignments include developing predictive models for business and engineering problems at Deere & Co. Moline, IL.