It is often challenging to make sense of the information hidden within large volumes of unstructured data and speech. How do we seamlessly classify and extract key ideas with automation? In this presentation, we explore the open source tools, algorithms and services that may come in handy for the design of a reference architecture to sieve out underlying insights from texture descriptions. More specifically, we discuss how we can assemble a suite of text mining and probabilistic graphical processes into an automated pipeline, built on top of a modular framework to surface embedded topics of interest.
Johnson is currently Head Data Science for Big Data Analytics Center of Excellence at DBS Bank. He holds an adjunct faculty appointment at SMU School of Information Systems where his core focus areas include applied statistical computing, machine learning as well as big data tools and techniques. An avid programmer and data enthusiast, Johnson enjoys developing apps and data products. Most recently, he was awarded first prize in Singapore’s largest coding competition, [email protected] 2015 as well as the CapitaLand Data Challenge 2016. Johnson completed his bachelor’s degree at University of California, Berkeley, majoring in the subjects of Pure Mathematics, Statistics and Economics. He received his postgraduate degree in Statistics at Yale University.