Home

Research

Publications

Thesis Advising

Teaching

Curriculum Vitae

Dr. Yijun Zhao

Associate Professor
Director of the MS in Data Science (MSDS) Program
Co-director of the Dual MS in Data Science and MA in Economics Program
Co-director of the Joint MS in Data Science and Quantitative Economics Program
Computer and Information Sciences Department
Fordham University

Publications (by application domain)

Authors marked in red are Fordham students/alumni, mostly from the MSDS program. You may also browse by research area , type, and year .

Healthcare AI & Medical Image Analysis

  • Key Projects:
    • Large Language Models for Early Prediction of Multiple Sclerosis Relapse
    • Predict disease course in Multiple Sclerosis
    • Identify Severe SLE Flares in Lupus Patients
    • Abnormality detection in musculoskeletal radiographs
    • In‐scanner head‐pose estimation and motion‐artifact reduction in brain MRI
  • Collaborators:
    • Harvard Medical School
    • Brigham & Women’s Hospital (BWH)
    • Massachusetts General Hospital
    • NYU Grossman School of Medicine
  1. Y. Zhao , E. Madil, A. Kitessa, B. Castle , A. Katre , and T. Chitnis,“From Structured EHR Data to Narratives: Large Language Models for Early Prediction of Multiple Sclerosis Relapse”, IEEE International Conference on Data Mining Workshops (ICDMW), 2025 [Online]
  2. 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]
  3. 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]
  4. Y. Zhao , D. Smith , and A. Jorge, " Comparing Two Machine Learning Approaches in Predicting Lupus Hospitalization Using Longitudinal Data," Scientific Reports, 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. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. M. He , X. Wang , and Y. Zhao , "A calibrated deep learning ensemble for abnormality detection in musculoskeletal radiographs," Scientific Reports, 2021 [Online]
  12. Y. Shi , Z. Wu , S. Zhang , H. Xiao, and Y. Zhao , "Assessing Palliative Care Needs Using Machine Learning Approaches," IEEE COMPSAC, 2021 [Online]
  13. 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]
  14. 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
  15. 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]
  16. 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]
  17. A. Jorge, Z. Wu , T. Chowdhury , Y. Zhao , "Exploration of Machine Learning Methods in Predicting Systemic Lupus Erythematosus Hospitalizations," ACR Convergence, 2020 [Online]
  18. 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]
  19. Y. Zhao , T. Chitnis, and T. Doan , "Ensemble Learning for Predicting Multiple Sclerosis Disease Course," The 15th International Conference on Data Science, 2019, [PDF]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. 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]

Educational AI

  • Key Projects:
    • MAESTRO: Multilingual AI-driven Educational System for TRaining and Online learning
    • Autograder
    • Predictive modeling of graduate admissions
    • Automated detection of AI‐generated application materials
    • Quantitative analysis of letters of recommendation to uncover linguistic biases
    • Studies of student behavior and adaptation during the COVID-19 pandemic
  • Collaborators:
    • Co-directors of Fordham EDM Lab
    • Colleagues in Fordham’s Psychology and Chemistry departments
  1. Y. Zhao , A. Borell , F. Martinez , H. Xue , and G. Weiss, "Admissions in the Age of AI: Detecting AI-generated Application Materials in Higher Education," Scientific Reports, 2024 [Online]
  2. F. Martinez , G. Weiss, M. Palma , H. Xue , A. Borelli , and Y. Zhao , "GPT vs. Llama2: Which Comes Closer to Human Writing?," International Conference on Educational Data Mining (EDM), 2024
  3. Y. Zhao , Z. Qi , S. Do , J. Grossi , J. Kang , and G. Weiss, "Addressing Disparity in GRE-optional Admissions by Predicting GRE Performance Using Application Materials," International Conference on Educational Data Mining (EDM), 2024
  4. Y. Zhao , Z. Qi , J. Grossi , and G. Weiss, "Gender and culture bias in letters of recommendation for computer science and data science masters programs," Scientific Reports, 2023 [Online]
  5. Y. Zhao , X. Chen , H. Xue , and G. Weiss, "A Machine Learning Approach to Graduate Admissions and the Role of Letters of Recommendation," PLOS One, 2023 [Online]
  6. Y. Zhao , T. Wang , D. Mansah , E. Parnoff , S. He , and G. Weiss, "A Quantitative Machine Learning Approach to Evaluating Letters of Recommendation," Hawaii International Conference on System Sciences (HICSS), 2023 [Online]
  7. Y. Zhao , Y. Ding, H. Chekerid , Y. Wu , and Q. Wang, "Ethnic Differences in Response to COVID-19: A Study of American-Asian and Non-Asian College Students," Behavior Sciences, 2023 [Online]
  8. E. Thrall, F. Martinez Lopez , T. Egg , S. Lee , J. Schrier, and Y. Zhao , "Rediscovering the Particle-in-a-Box: Machine Learning Regression Analysis for Hypothesis Generation in Physical Chemistry Lab," Journal of Chemical Education, 2023 [Online]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]

AI & ML Techniques, Tools, and Fairness

  • Key Projects:
    • Bias analysis and fairness metrics for Large Languate Models (LLMs)
    • Development of fairness-aware training algorithms
    • Design of interpretability and evaluation tools
    • Advancement of optimization and regularization methods
  1. E. Roginek, J. Kulesza, and Y. Zhao, “Monte Carlo Synthetic Data Generation for Radiograph Denoising”, IEEE International Conference on Data Mining Workshops (ICDMW), 2025 [Online]
  2. Y. Li , Y. Liu , and Y. Zhao , " A-CLAPS: Automatic Correction of Language and Pronunciation Errors in Slide-based Presentations," IEEE COMPSAC, 2025 [Online]
  3. D. Cordero , and Y. Zhao , P. Diaz , G. Weiss, "Unveiling Bias: Analyzing Race and Gender Disparities in AI Generated Imagery," IEEE COMPSAC, 2025 [Online]
  4. J. Warren , G. Weiss, F. Martinez, A. Guo, and Y. Zhao , “Decoding Fatphobia: Examining Anti-Fat and Pro-Thin Bias in AI-Generated Images”, Findings of the Association for Computational Linguistics (NAACL), 2025 [Online]
  5. S. Yun , H. Xue , X. Zhang , J. Zhang, and Y. Zhao , "Enhancing Crime Investigation: Attention-Based GAN for Sketch-to-Portrait Conversion," IEEE COMPSAC, 2024
  6. K. Afane and Y. Zhao , "Selecting Classifiers and Resampling Techniques for Imbalanced Datasets: A New Perspective," 28th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES), 2024
  7. F. Martinez , and Y. Zhao , "Integrating Multiple Visual Attention Mechanisms in Deep Neural Networks," IEEE COMPSAC, 2023 [Online]
  8. Y. Wang , Y. Wang , C. Zhong , and Y. Zhao, "Assessing Deep Learning Approaches in Detecting Masked Facial Expressions," IEEE COMPSAC, 2022 [Online]
  9. Q. Xu , M. Sun , B. Fu , and Y. Zhao , "Deep Learning Based Parking Vacancy Detection for Smart Cities," Hawaii International Conference on System Sciences (HICSS), 2022 [Online]
  10. H. Yuan , W. Zheng , S. Yun , and Y. Zhao , "Parallel Deep Neural Networks for Musical Genre Classification: A Case Study," IEEE COMPSAC, 2021 [Online]
  11. 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]
  12. 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]

Finance & Algorithmic Trading

  • Key Projects:
    • Deep learning on stock market forecasting
    • LSTM‐driven statistical arbitrage
    • Analysis of social-media-fueled MEME stock trends
  1. S. Do , A. Chu, Y. Zhao , and Y. Li, "Stock Market Forecasting with Pretrained Deep Learning Models," IEEE BigDataService, 2025 [Online]
  2. Y. Zhao , Z. Du , S. Xu , Y. Chen , J. Mu , and M. Ning, "Social Media, Market Sentiment and Meme Stocks," , IEEE COMPSAC, 2023 [Online]
  3. 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]
  4. S. Bai and Y. Zhao "Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches," Systems, 2021 [Online]

Other

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