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Yifu Li

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Yifu Li

Assistant Professor

Contact Information

Office: CEC 116-E



Ph.D., Industrial and Systems Engineering, Virginia Tech
B.S., Industrial and Systems Engineering, Virginia Tech


Yifu Li is an Assistant Professor in the School of Industrial Engineering at the University of Oklahoma. He received his Ph.D. and a B.S. degree in Industrial and Systems Engineering at Virginia Tech. His research focuses on integrating data quality assurance with data-driven modeling for multi-modality data sets in smart manufacturing and healthcare. The proposed methods can provide enhanced data analytics performance and interpretation.

His research is published in various manufacturing and healthcare journals, including ASME Journal of Manufacturing Science and Engineering (JMSE), Journal of the American Medical Informatics Association (JAMIA), IISE Transactions. He is a member of the Institute for Operations Research and the Management Sciences (INFORMS), the Institute of Industrial and Systems Engineers (IISE), and the Institute of Electrical and Electronics Engineers (IEEE).

Research Interests

  • Data-driven modeling
  • Data Quality
  • Natural Language Processing

Selected Publications

  • LiY., Sun, H., Deng, X., Zhang, C., Wang, H. P., Jin, R., 2020, “Manufacturing Quality Prediction Using Smooth Spatial Variable Selection Estimator with Applications in Aerosol Jet® Printed Electronics Manufacturing.” IISE Transactions, 52 (3), 321-333. 
  • LiY., Jin, R., Yuan L., 2018, “Classifying Relations in Clinical Narratives using Segment Graph Convolutional and Recurrent Neural Networks (Seg-GCRNs)” Journal of the American Medical Informatics Association 26 (3), 262-268. 
  • Sun, H., Wang, K., Li, Y., Zhang, C., Jin, R., 2017, “Quality Modeling of Printed Electronics in Aerosol Jet Printing Based on Microscopic Images”, ASME Transactions Journal of Manufacturing Science and Engineering 139 (7), 071-082.  
  • Li, Y., Mohan, K., Sun, H., Jin, R., 2017, “Ensemble Modeling of In Situ Features for Printed Electronics Manufacturing With In Situ Process Control Potential”, IEEE Robotics and Automation Letters 2 (4), 1864-870.

List of all publications: Google Scholar