Utah State
University

Biosketch of Dr. Xiaojun Qi


Dr. Xiaojun Qi is a Professor in the School of Computing and Director of the Computer Vision Laboratory at Utah State University (USU). She is an expert in artificial intelligence, specializing in image processing, machine learning, computer vision, and deep learning. Dr. Qi has built and sustained a nationally recognized research program that actively involves both undergraduate and graduate students in tackling complex problems in machine learning and computer vision. She has secured competitive funding from the National Science Foundation (NSF), Air Force Research Lab, Utah Department of Transportation, Utah Water Research Laboratory, and TCL Research America, serving as Principal Investigator (PI) on more than 20 projects and Co-PI on two others.

Throughout her career, Dr. Qi has mentored over 95 students and published more than 125 peer-reviewed papers, book chapters, and conference proceedings in top-tier venues. Her professional leadership includes participation on 16 technical program committees, five NSF review panels, and reviewing for 40 international journals, 23 international conferences, and four textbooks. From 2017 to 2025, she also served as Department Head of the Computer Science Department, guiding research, education, and faculty development.

Dr. Qi's recent research focuses on five key areas: 1) Developing interpretable AI systems for medical applications, including X-ray diagnosis, embryo development, and automated report generation using vision-language techniques. 2) Building robust and explainable detection models for Deepfakes and other synthetic media generated with deep learning. 3) Creating AI-based methods to assess intersection infrastructure and accessibility. 4) Applying Relational Deep Learning (RDL) to improve knowledge tracing in online learning systems. 5) Designing efficient multimodal deep learning approaches for agricultural applications, such as crop disease detection and nutrient deficiency identification.


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It was last maintained on July 31, 2025.