معرفی
Qi Li is a Professor in the Computer Science Program at Western Kentucky University in Bowling Green, KY. His professional activities span research, teaching, and academic service with a focus on computer vision, pattern recognition, and data analysis.
Dr. Li's research interests include Data mining, Machine learning, Bioinformatics, Appearance based recognition, and related fields in computer vision. His work bridges theoretical approaches with practical applications, particularly in biological imaging and face recognition systems.
His publication record shows a consistent focus on feature detection, particularly addressing the challenge of imbalanced points in image analysis. His research has evolved from foundational work in linear discriminant analysis to specialized applications in embryo contour extraction and face recognition systems. The publications demonstrate a strong emphasis on solving practical computer vision problems with mathematical rigor.
Dr. Li has served on editorial boards for several prominent journals including Neurocomputing (2007-present), International Journal of Computers, Systems, and Signals (2007-present), and previously for the International Journal of Data Mining, Modelling, and Management (2008-2010) and the International Journal of Data Analysis Techniques and Strategies (2008-2010).
As an active member of the academic community, Dr. Li has served on program committees for numerous conferences including the International Conference on Machine Learning and Applications (ICMLA), International Conference on Image and Graphics (ICIG), and IEEE International Conference on Tools with Artificial Intelligence (ICTAI). He has also reviewed for prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Knowledge and Data Engineering.
His teaching portfolio is extensive, covering undergraduate and graduate courses including Programming Languages, Operating Systems, Interactive Computer Graphics, Data Structures, Algorithms, Data Mining, and Computer Vision.
