Seminar: Online Monitoring of Big Data Streams — Roadmap and Recent Advances, February 25
Attend the upcoming IE Decision Systems Engineering Spring 2022 Seminar Series event with Professor Kaibo Liu from the University of Wisconsin-Madison to learn more about research advances in online monitoring of big data streams.
Online Monitoring of Big Data Streams – Roadmap and Recent Advances
Presented by Kaibo Liu, University of Wisconsin-Madison
Friday, February 25, 2022
Noon–1 p.m.
Attend on Zoom
Abstract
The rapid advancements of internet of things technology and cyber-physical infrastructure have resulted in a temporally and spatially dense data-rich environment, which provides unprecedented opportunities for performance improvement in various complex systems. Meanwhile, it also raises new research challenges on process monitoring, such as heterogenous data formats, high-dimensional and big data structures, inherent complexity of the target systems and potential lack of complete a priori knowledge.
In this talk, Kaibo Liu will present an overview of his team’s research roadmap and recent advances in online monitoring of big data streams. He will discuss several research works in detail to elaborate the needs and research evolution of developing data science and multidisciplinary analytics methods for effective process monitoring, dynamic sampling and quality improvement in industrial applications tailored to the characteristics of big data.
About the speaker
Kaibo Liu is an associate professor at the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. He is also the associate director of UW-Madison IoT systems research center.
He received his Bachelor of Science degree in industrial engineering from the Hong Kong University of Science and Technology, a Master of Science degree in statistics and a doctoral degree in industrial engineering from the Georgia Institute of Technology.
Liu’s research is in the area of system informatics and industrial big data analytics, which an emphasis on the data fusion approach for real-time system modeling, monitoring, diagnosis, prognosis and decision-making. His research has been successfully funded by NSF, ONR, AFOSR, DOE, U.S. Army Corps of Engineers and industry.
He is the recipient of three prestigious early career award, including the 2019 Outstanding Young Manufacturing Engineer Award by SME, the 2019 Feigenbaum Medal Award by ASQ and the 2019 Dr. Hamed K. Eldin Outstanding Early Career IE in Academia Award by IISE, and also the winner of the Innovations in Education Award from IISE in 2020 and the Award for Technical Innovation in Industrial Engineering from IISE in 2021.
He currently serves as an associate editor of IEEE Transactions on automation science and engineering, the department editor of IISE Transactions on Data Science, Quality and Reliability, and the editor of IEEE CASE.