Tuesday, May 28, 2019
1 p.m., Avery 347
Shruti Daggumati, Ph.D.Ph.D. graduate, University of Nebraska–Lincoln
In this talk, I will discuss the approaches to tackling the many challenges in data science. I will start by introducing my overall teaching methodologies, philosophy, describe the challenges of teaching data science to introductory level students and the proper pedagogical approaches which are required in order to instill academic success. Finally, I will discuss various problem-solving techniques required to handle situations where incomplete data and data with outliers exist, during which time I will introduce the different types of missing data and how to handle those specific instances.
Shruti Daggumati graduated with a Ph.D. from the University of Nebraska-Lincoln. She received her M.S. in Computer Science and B.S. in Computer Engineering at the University of Nebraska-Lincoln as well. She was the lead researcher as a systems engineer at Verizon for OCR of government contracting. Her research expertise lies at the intersection of machine learning, data visualization, and computational linguistics. Her research integrates non-linear stochastic models applied to the identification of ancient scripts using machine learning methodologies. She is the lead author of several publications which have appeared in top database and application venues such as IDEAS and ADBIS.