Curriculum Vitae

Dr. Yijun Zhao

Assistant Professor
Director of the MS in Data Science (MSDS) Program
Computer and Information Sciences Department
Fordham University

Peer-reviewed Journal and Conference Publications
( authors marked in red are Fordham students/alumni, mostly from the MSDS program)


  1. Y. Zhao, D. Smith, and A. Jorge, " Comparing Two Machine Learning Approaches in Predicting Lupus Hospitalization Using Longitudinal Data," Scientific Reports, 2022   [Online]

  2. Y. Zhao, Y. Ding, H. Chekerid, and Y. Wu, "Student Adaptation to College and Coping in Relation to Adjustment During COVID-19: A Machine Learning Approach," PLOS One, 2022   [Online]
  3. Y. Zhao, Z. Qi, and S. Do, J. Grossi, and J. Kang, and G. Weiss, "Addressing Disparity in GRE-optional Admissions by Predicting GRE Performance Using Application Materials," Under Review
  4. Y. Zhao, S. Xu, and J. Ossowski, "Deep Learning Meets Statistical Arbitrage: An Application of Long Short-Term Memory Networks to Algorithmic Trading," Journal of Fiancial Data Science, 2022.   [Online]

  5. Y. Zhao, M. Qin, and A. Jorge, "A Calibrated Ensemble Algorithm to Address Data Heterogeneity in Machine Learning: An Application to Identify Severe SLE Flares in Lupus Patients," IEEE Access, 2022   [Online]

  6. S. Li, and Y. Zhao, "Addressing Motion Blurs in Brain MRI Scans Using Conditional Adversarial Networks and Simulated Curvilinear Motions," Journal of Imaging, 2022   [Online]

  7. Y. Zhao, Y. Ding, Y. Shen, S. Failing, and J. Hwang, "Different coping patterns among U.S. graduate and undergraduate students during COVID-19 pandemic: A machine learning approach," International Journal of Environmental Research and Public Health, 2022   [Online]

  8. Y. Zhao, Y. Ding, Y. Shen, and W. Liu, "Gender Difference in Psychological, Cognitive, and Behavioral Patterns Among University Students During COVID-19: A machine learning approach," Frontiers in Psychology, 2022   [Online]

  9. A. Jorge , D. Smith, Z .Wu,T. Chowdhury, K. Costenbader, Y. Zhang, H.Choi, C. Feldan, and Y. Zhao "Exploration of Machine Learning Methods to Predict Systemic Lupus Erythematosus Hospitalizations," Lupus, 2022   [Online]

  10. M. He, X. Wang, and Y. Zhao, "A calibrated deep learning ensemble for abnormality detection in musculoskeletal radiographs," Scientific Reports, 2021   [Online]

  11. H. Pardoe, S. Martin, Y. Zhao, A. George, H. Yuan, J. Zhou, W. Liu, and O. Devinsky, "Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video," Journal of Magnetic Resonance Imaging, 2021   [Online]

  12. E. Thrall, S. Lee, J. Schrier, and Y. Zhao , "Machine Learning for Functional Group Identification in Vibrational Spectroscopy: A Pedagogical Lab for Undergraduate Chemistry Students," Journal of Chemical Education, 2021   [Online]

  13. S. Bai and Y. Zhao "Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches," Systems, 2021   [Online]

  14. Y. Zhao, T. Wang, R. Bove, B. Cree, R. Henry, H. Lokhande, M. Polgar-Turcsanyi, M. Anderson, R. Bakshi, H. Weiner, T. Chitnis, and SUMMIT Investigators. "Ensemble learning predicts multiple sclerosis disease course in the SUMMIT study," npj Digital Medicine, 2020   [Online]

  15. Y. Zhao, B. Healy, D. Rotstein, C. Guttmann, R. Bakshi, H. Weiner, C. Brodley, and T. Chitnis "Exploration of Machine Learning Techniques in Predicting Multiple Sclerosis Disease Course," PLOS ONE, 2017   [PDF]

  16. M. Kong and Y. Zhao, "Computing k-independent sets for regular bipartite graphs," Congressus Numerantium Vol. 143(2000), pp. 65-80  [PDF]


  1. Y. Zhao and T. Chitnis, "Dirichlet Mixture of Gaussian Processes with Split-kernel: An Application to Predicting Disease Course in Multiple Sclerosis Patients," The International Joint Conference on Neural Networks (IJCNN), 2022   [PDF]

  2. Q. Xu, M. Sun, B. Fu, and Y. Zhao, "Deep Learning Based Parking Vacancy Detection for Smart Cities," Hawaii International Conference on System Sciences, 2022   [Online]

  3. D. Leeds, C. Chen, Y. Zhao , F. Metla, J. Guest, and G. Weiss, "Generalize Sequential Pattern Mining of Undergraduate Courses," International Conference on Educational Data Mining (EDM), 2022   [PDF]

  4. Y. Wang, Y. Wang, C. Zhong, and Y. Zhao, "US County-level Risk Factors Associated with COVID-19 Exacerbation During Vaccination Era," IEEE COMPSAC, 2022   [Online]

  5. W. Liu, J. Zhang, and Y. Zhao, "A Comparison of Deep Learning and Traditional Machine Learning Approaches in Detecting Cognitive Impairment Using MRI Scans," IEEE COMPSAC, 2022   [Online]

  6. Y. Wang, Y. Wang, C. Zhong, and Y. Zhao, "Assessing Deep Learning Approaches in Detecting Masked Facial Expressions," IEEE COMPSAC, 2022   [Online]

  7. Y. Zhao, J. Ossowski, X. Wang, S. Li, O. Devinsky, S. Martin, and H. Pardoe, "Localized Motion Artifact Reduction on Brain MRI Using Deep Learning with Effective Data Augmentation Techniques," The International Joint Conference on Neural Networks (IJCNN), 2021   [Online]

  8. Y. Xiao and Y. Zhao, "Preserving Gender and Identity in Face Age Progression of Infants and Toddlers," International Joint Conference on Biometrics (IJCB), 2021   [Online]

  9. H. Yuan, W. Zheng, S. Yun, and Y. Zhao, "Parallel Deep Neural Networks for Musical Genre Classification: A Case Study," IEEE COMPSAC, 2021   [Online]

  10. Y. Shi, Z. Wu, S. Zhang, H. Xiao, and Y. Zhao, "Assessing Palliative Care Needs Using Machine Learning Approaches," IEEE COMPSAC, 2021   [Online]

  11. Y. Zhao, M. Berretta, T. Wang, and T. Chitnis, "A Temporal Model with Dynamic Imputation for Missing Target Values in Longitudinal Patient Data," IEEE International Conference on Healthcare Informatics (ICHI), 2020   [Online]

  12. Y. Zhao, B. Lackaye, J. Dy, and C. Brodley, "A Quantitative Machine Learning Approach to Master Students Admission for Professional Institutions," International Conference on Educational Data Mining (EDM), 2020   [PDF]

  13. Y. Zhao, Q. Xu, M. Chen, and G. Weiss "Predicting Student Performance in a Master’s Program in Data Science using Admissions Data," International Conference on Educational Data Mining (EDM), 2020   [PDF]

  14. Y. Zhao, W. Wu, Y. Jin, S. Gu, H. Wu, J. Wang, X. Jiang, and H. Xiao, "Predicting 30-Day Hospital Readmissions for Patients with Diabetes," International Conference on Health Informatics (HIMS), 2019   [PDF]

  15. Y. Zhao, T. Chitnis, and T. Doan, "Ensemble Learning for Predicting Multiple Sclerosis Disease Course," The 15th International Conference on Data Science, 2019,   [PDF]

  16. Y. Zhao and S. Lebak, "Deep Convolutional Autoencoder for Recovering Defocused License Plates and Smudged Fingerprints," The 15th International Conference on Data Science, 2019   [PDF]

  17. Y. Zhao, B. Ahmed, T. Thesen, K. E. Blackmon, J. Dy, and C. Brodley "A Non-parametric Approach to Detect Epileptogeic Lesions using Restricted Boltzmann Machines," 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016   [PDF]

  18. Y. Zhao, T. Chitnis, B. Healy, J. Dy, and C. Brodley "Domain Induced Dirichlet Mixture of Gaussian Processes: An Application to Predicting Disease Progression in Multiple Sclerosis Patients," The IEEE International Conference on Data Mining Series (ICDM), 2015   [PDF]

  19. Y. Zhao, C. Brodley, T. Chitnis, and B. Healy, "Addressing Human Subjectivity via Transfer Learning: An Application to Predicting Disease Outcome in Multiple Sclerosis Patients," 2014 SIAM International Conference on Data Mining, 2014   [PDF]

  20. B. Ahmed, T. Thesen, K. Blackmon, and Y. Zhao, O. Devinsky, R. Kuzniercky, C. Brodley, "HierarchicalConditional Random Fields for Outlier Detection: An Application to Detecting Epileptogenic Cortical Malformations," The 31st International Conference on Machine Learning (ICML), 2014   [PDF]

Abstract + Poster

  1. Y. Zhao, H. Yuan, J. Zhou, S. Martin, H. Pardoe, "Deep Convolutional Neural Networks for Predicting Head Pose During Brain MRI Acquisition," Journal of Vision for VSS Annual Meeting, 2020   [Online]

  2. Y. Zhao, J. Ossowski, X. Wang, S. Li, S. Martin, H. Pardoe, "Deep Convolutional Autoencoder for Reducing Motion Artifactsin Structural Brain MRI Scans," Conference for Organization of Human Brain Mapping (OHBM), 2020

  3. A. Jorge, Z. Wu, T. Chowdhury, Y. Zhao, "Exploration of Machine Learning Methods in Predicting Systemic Lupus Erythematosus Hospitalizations," ACR Convergence, 2020   [Online]