Dan Li

Assistant Professor
Industrial & Systems Engineering

Pronouns: She/Her

dli27@uw.edu
SIG 218

Biography

Dan Li is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Washington. She received her Ph.D. in Industrial Engineering and her M.S. in Statistics from the Georgia Institute of Technology, and her B.S. in Automotive Engineering from Tsinghua University in Beijing, China. Her research interests lie in developing new data-driven algorithms tailored to enhance the cyber-physical resilience and security of critical infrastructures. Dan is the recipient of the NSF CAREER Award and the IISE Transactions Best Application Paper Award. She has also been recognized in multiple Best Track Paper and Best Student Paper Awards in Energy Systems, DAIS, and QCRE divisions at the IISE Annual Meetings, as well as the INFORMS QSR community.

Education

Industrial Engineering, Georgia Institute of Technology
Statistics, Georgia Institute of Technology
Automotive Engineering, Tsinghua University

Research Statement

My research interests include applied statistics and AI/ML for cyber-physical security and resilience, online anomaly detection, reinforcement learning, and complex systems modeling and control.

Current projects

Understanding the Integrated Cyber-Physical Resilience of Continuous Critical Manufacturing

Industrial internet-of-things (IIoT) technologies spark growing interest in manufacturing security and resilience. However, current solutions lack a holistic understanding of cyber-physical resilience in complex systems, failing to connect IIoT network vulnerabilities with dynamic manufacturing processes for effective detection and control. To address these gaps, this Faculty Early Career Development (CAREER) project aims to develop novel methodologies that integrate modeling, detection, and control measures for understanding the cyber-physical resilience of continuous critical manufacturing systems.

Human-Centered Cybersecurity in Robotic Surgery

Robotic-assisted surgery cyberinfrastructure has brought opportunities for advanced diagnostics, patient monitoring, and personalized medicine. However, the increased data and resource sharing enabled by these robotic-assisted surgery infrastructures has increased vulnerabilities and opportunities for cyberattacks. Existing studies on cyberinfrastructure security in these environments do not fully consider users' cybersecurity awareness and knowledge or evaluate training and mitigation for cyberattacks. This project aims to create a Human-Centered Cybersecurity in Robotic Surgery (HCCRS) framework by integrating data analytics and healthcare human factors to design new human-centered algorithms to detect, identify, and mitigate cyberattacks in the robotic surgery cyberinfrastructure.

Honors & awards