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IE's Lin takes home Data Analytics Award from IEOM
By
Stephen Greenwell
Ying Lin, an associate professor in the Cullen College of Engineering's Industrial Engineering Department, received a Data Analytics Award at the 8th North American International Conference, organized by Industrial Engineering and Operations Management.
Ying Lin, an associate professor in the Cullen College of Engineering's Industrial Engineering Department, received a Data Analytics Award at the 8th North American International Conference, organized by Industrial Engineering and Operations Management.

Ying Lin, an associate professor in the Cullen College of Engineering's Industrial Engineering Department, has earned an international award for her work in the field of data analytics and as director of the Smart Health & Intelligent Engineering Systems (SHINES) Lab.

At the 8th North American International Conference on Industrial Engineering and Operations Management (IEOM), Lin received a Data Analytics Award from the organization. She called the distinction “a great honor” for her and her research group.

“This recognition means a lot to me and my group,” she said. “I want to express my heartfelt gratitude to my students for their efforts and gratitude to my department chair for his support. I am passionate about data analytics and its potential to drive meaningful insights and positive change, and I look forward to continuing my work in this field.”

Lin has been a member of the Cullen faculty since Fall 2017. She earned her doctorate from the University of Washington, and her master's degree from the University of South Florida. In 2020, she received a $435,000 grant to continue research on identifying underlying genetic contributors to some forms of psychiatric illness. That award is part of a larger $2.4 million project by the Lieber Institute for Brain Development in Baltimore.

Lin said her current research is focusing on analyzing data integrated from multiple sources.

“This type of data is usually associated with heterogeneous structures and the risk of privacy leakage,” she said. "To solve these issues, we have developed novel machine learning frameworks for multimodal data integration and privacy-preserving data sharing. Our recent works have been published in IISE Healthcare Systems Engineering and BMC Bioinformatics.”

With more than 20,000 members in 151 countries, IEOM strives to be the premier global organization dedicated to the advancement of industrial engineering and the operations management discipline. For more information, visit the organization's website.

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