Yijun Zhao

Dr. Yijun Zhao is an Associate Professor in the Department of Computer and Information Science at Fordham University. Her research explores Machine Learning methods to address bias and subjectivity in datasets, with applications in Healthcare Analytics and Neuroscience. She has collaborated with Harvard Medical School and NYU Langone Medical Center on projects related to Multiple Sclerosis and Epilepsy.

• Education:
• PhD in Computer Science, Tufts University
• MS in Computer Science, University of Kansas
• BS in Computer Science, Tianjin University

Research Interests
The success of supervised machine learning algorithms hinges on the assumption that data are drawn from a single underlying distribution, which often fails in real-world applications involving human judgment. Dr. Zhao has pioneered methods to enhance model performance when learning from biased or subjective datasets, particularly in biomedical domains. Her work includes:

• Predicting disease progression in Multiple Sclerosis patients
• Detecting brain abnormalities in neurological disorders
• Developing non-parametric and probabilistic machine learning techniques
• Applying transfer learning to mitigate human subjectivity
• Creating domain-specific Gaussian Process models
• Using Conditional Random Fields for outlier detection

Publications Trends
Dr. Zhao's work spans Machine Learning, Healthcare Analytics, and Graph Theory. Her recent publications focus on biomedical applications, including Multiple Sclerosis and Epilepsy prediction models, while her earlier work in 2000 addressed graph algorithms in Computer Science.

Teaching
Dr. Zhao teaches CISC 2000 Computer Science II and CISC 4631/CISC 6930 Data Mining at Fordham. Her courses emphasize object-oriented programming, data mining techniques, and algorithm implementation with real-world datasets.