ML Seminar: Shancong Mou

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Tuesday from 11 a.m. - 12 p.m. during the Fall 2024 semester.

This week's speaker, Professor Shancong Mou (Industrial and Systems Engineering, University of Minnesota), will be giving a talk titled "AI/ML-enabled Data Fusion for Complex Engineering Systems".

Abstract

Recent advancements in artificial intelligence (AI) and machine learning (ML), along with improvements in sensor technologies and computing power, have paved the way for data-driven solutions across a wide range of engineering applications. This talk explores novel applications of AI/ML methodologies for data analytics in complex engineering systems, highlighting the convergence of engineering science, optimization, and statistics.

I will start with an overview of the research landscape, followed by a discussion on AI/ML-enabled data fusion, demonstrated through several key examples:
Surface quality monitoring in personal electronics manufacturing, using robust learning for label-efficient monitoring of high-dimensional data.
Quality and productivity improvement in composite fuselage assembly, a critical process in modern airplane manufacturing, utilizing PDE-constrained optimization for design and optimal control.
Control and Design Optimization in Composite material and semiconductor manufacturing processes, leveraging physics-informed machine learning.

The talk will conclude with a discussion of current challenges and future research directions.

Biography

Shancong Mou is an assistant professor in the Department of Industrial and Systems Engineering at the University of Minnesota, Twin Cities. He received his Ph.D. in Industrial Engineering from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech in 2024. He also holds an MS in Computational Science and Engineering from Georgia Tech.

His research focuses on AI/ML-enabled data analytics for quality and productivity improvement in complex engineering systems, intersecting with statistics, operations research, machine learning, and computational science. His work is supported by NSF, Apple, Boeing, and OG Technologies. He has received numerous awards and scholarships from ASA, IISE, ISA, and Georgia Tech, including the Mary G. and Joseph Natrella Scholarship from ASA and the ISyE Outstanding Graduate Student Instructor of the Year award in 2022.

Start date
Tuesday, Sept. 17, 2024, 11 a.m.
End date
Tuesday, Sept. 17, 2024, Noon
Location

3-180 Keller Hall
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