DMS Statistics and Data Science Seminar

Speaker: Santu Karmaker (Computer Science and Software Engineering, Auburn University)

Title: Data Science for "All"

Abstract: Big data is ubiquitous across domains, and more and more stakeholders are choosing to use machine learning to get the most out of their data. This pressing need has inspired researchers to develop tools for Automated machine learning (AutoML), which is essentially automating the process of applying machine learning to real-world problems. But although automation and efficiency are some of AutoML's main selling points, the process still requires a surprising level of human involvement from a data scientist and are still far from a “real automatic system.” As a result, AutoML tools are not yet directly usable by domain experts like doctors, business professionals, social scientists, and so on, who have little / no knowledge of machine learning / data science. In summary, data science is not yet open to “all.” How can we make data science more accessible to the general people? This talk will focus on this big question while discussing three independent yet related line of works. The first direction is about helping general users annotate big corpus of text data with minimal guidance from the users. The second direction is about enabling end users to perform interesting semantic analysis of unstructured data without worrying about the underlying intricate details of machine learning. The third and final direction will be laying out a vision for Virtual Interactive Data Scientist, a natural dialog-based intelligent agent which can be thought of as the future siri or alexa, which can assist users in solving real-life data science problems.

Seminar website (which also contains the links for the recordings of the seminars): http://webhome.auburn.edu/~ezc0066/stat-datasci-seminar.html

Join from PC, Mac, Linux, iOS or Android: https://auburn.zoom.us/j/93758346031