This Lunch & Learn Series, exclusively tailored for faculty and graduate students within the EECS department, takes place on the last Friday of every month during the fall and spring semesters. A light lunch will be provided by the department. Please RSVP to Angie Chance by March 26.

Join us and engage with our guest speaker, who will highlight campus resources, new initiatives, and other valuable information to foster research collaborations, networking, and other opportunities among our faculty and students.

Interactive Data Science: Detecting the Expected and Discovering the Unexpected

Abstract

Well-designed interactive data science systems seamlessly integrate human-centered data visualization techniques with automated computational guidance. Although the growing complexities of modern data sets demand algorithmic guidance and robust computing resources, most scientific endeavors also hinge on the availability of interactive data exploration techniques that allow users to make new discoveries, especially those of the unexpected variety. Achieving such a balance, however, is not trivial; it requires a judicious orchestration of human perception and cognition strengths with the power of machine learning and artificial intelligence software executing on advanced computational technologies.

In this talk, I will describe the aims of my interactive data science research and highlight several tools that I have developed in the context of practical scientific challenges. Most of my talk will focus on the evolution of a multivariate visual analytics framework that was originally developed for my dissertation research over a decade ago. I will discuss the original design and key extensions that have been added over the years to solve challenges in both industry and multidisciplinary science fields. Along the way, I will highlight the interaction, data visualization, and statistical / machine learning elements of the work, as well as their integration into data-driven, human-directed, and machine-enabled tools. I will conclude with a short overview of my career and lessons learned.

Biography

Chad Steed is a Distinguished Research Staff member who leads the Data Science and Visualization Group in the Cyber Resilience and Intelligence Division at Oak Ridge National Laboratory (ORNL). He returned to ORNL in late 2022 after an industry sabbatical that included service as Vice President in Enterprise Data Analytics at Regions Financial Corp. (2021) and the Senior Director of Data Analytics at Lirio, LLC (2022). In his first stint at ORNL, which spanned over a decade, he progressed from Associate to Senior Research Staff, served as a Team Leader, and was the founding Director of the ORNL Visual Informatics for Science and Technology Advances (VISTA) Laboratory. Prior to first joining ORNL, he enjoyed positions with the Naval Research Laboratory as a computer scientist and Lockheed Martin as a software engineer. Dr. Steed earned a Ph.D. in Computer Science from Mississippi State University with an emphasis in data visualization / computer graphics and software engineering. He also received a M.S. in Hydrographic Science and a B.S. in Software Engineering (with a Fine Art minor) from the University of Southern Mississippi. His research focuses on developing methods that orchestrate visual analytics, machine learning, and scalable data management to enable human-directed, machine-enabled, and data-driven analysis with broad multidisciplinary applications. He received an ORNL Innovation Award in 2024, the 2014 UT-Battelle Early Career Researcher Award, two ORNL Technology Commercialization Awards (2013 and 2014), and an R&D 100 Award in 2013. In 2018, he was also named a MSU Bagley College of Engineering Distinguished Fellow. He has 99 publications, 2 awarded and 3 pending patents, and several open-source software projects. He is a senior member of the IEEE and ACM societies. For more information about Dr. Steed’s work visit https://csteed.com.