[IISE Faculty List] IISE QCRE Webinar

[Juan DU]

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Dear colleagues,

IISE QCRE division presents webinar by Dr. Xiaochen Xian at Georgia Tech on Wednesday, Dec. 3, at 8 p.m. ET. The registration link is https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.iise.org%2Fdetails.aspx%3Fid%3D643&data=05%7C02%7Ciefac.list%40mailman.clemson.edu%7Cf1ad9260f3fd4edbb85908de263f1702%7C0c9bf8f6ccad4b87818d49026938aa97%7C0%7C0%7C638990249598111802%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=pqcom6XpWWgw0kKVoqwLrqymSim2MfKjViNMnMRV65s%3D&reserved=0.

Title: Adaptive Sampling for Anomaly Detection: From Operational Decision-Making to Strategic System Design
Abstract:
The ability to detect anomalies quickly is critical in fields ranging from environmental monitoring to industrial quality control. However, this task is often hampered by limited resources, allowing us to observe only a small subset of a system at any given time. This talk presents a cohesive research agenda that tackles this challenge across multiple levels, moving from real-time operational decisions to the foundational strategic design of the monitoring system itself.
We begin at the operational level, introducing data-driven pathwise sampling approaches for online anomaly detection. Using networks of Mobile Vehicle-based Sensors (MVSs), we demonstrate how to dynamically coordinate sensor routes in real-time. By integrating statistical process control with route optimization, these strategies enable sensors to act as an intelligent swarm, adaptively pursuing suspicious signals while respecting physical movement constraints. A refined Bayesian jump model is further presented to enhance operational decision-making under uncertainty.
We then ascend to the strategic design level, addressing the fundamental question: how should such a monitoring system be optimally configured before it is even deployed? We present a theoretical framework for the optimal design of monitoring schemes under partial observations. By deriving the first non-asymptotic formulas for average detection delay, we provide a principled method to make key strategic decisions―such as determining the optimal number of sensors―to balance detection performance with practical economic costs.
Bio: Dr. Xiaochen Xian is currently an assistant professor in H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. Prior to joining Georgia Tech, she was an assistant professor in the Department of Industrial and Systems Engineering at the University of Florida. Her research interest mainly focuses on big data analytics and system informatics. Specifically, her research includes big data stream monitoring and sampling, engineering knowledge-enhanced complex process modeling and diagnosis, on-demand machine learning, and system informatics and spatiotemporal real-time prediction.

Dr. Xian’s research has been supported by federal and local agencies including NSF, NIH, the Florida Center for Cybersecurity, and the Florida Space Grant Consortium. She is the recipient of multiple awards, including NIH NIBIB Trailblazer Award, Cottmeyer Family Faculty Fellowships, multiple paper awards from INFORMS, IISE, and IEEE, and feature articles in IISE magazine, AIE, and YoungStats. Dr. Xian is an associate editor of IEEE Transactions on Automation Science and Engineering.

Thank you for your attention, and we look forward to seeing you at the webinar.

Best,
Juan Du (she/her/hers), Ph.D.
Assistant Professor,
Smart Manufacturing Thrust, Data Science and Analytics Thrust
The Hong Kong University of Science and Technology (Guangzhou)
Associate Director, Industrial Intelligence and Data Analytics Lab
Affiliate Assistant Professor,
The Hong Kong University of Science and Technology
PI, Industrial Data Analytics and Digital Manufacturing (IDADM) Lab/<https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpersonal.hkust-gz.edu.cn%2Fjuandu%2FIDADM-Lab%2Findex.html&data=05%7C02%7Ciefac.list%40mailman.clemson.edu%7Cf1ad9260f3fd4edbb85908de263f1702%7C0c9bf8f6ccad4b87818d49026938aa97%7C0%7C0%7C638990249598132153%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=w3r5G1jyOR32IBvRbqM1vuCbHFHZMfBGiz0cp%2B%2B63g8%3D&reserved=0>
Phone: +86 020 88335989
Email: juandu at ust.hk<mailto:juandu at ust.hk>
Personal Link: https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsites.google.com%2Fview%2Fjuandu%2F&data=05%7C02%7Ciefac.list%40mailman.clemson.edu%7Cf1ad9260f3fd4edbb85908de263f1702%7C0c9bf8f6ccad4b87818d49026938aa97%7C0%7C0%7C638990249598148274%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=tqXgGoH0VgHJwmAXzvFj6g5%2BonJ7r6dF8OQVn%2B48LsU%3D&reserved=0

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