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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 year)

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

2027

  1. Y. Zhao, F. Lopez, J. Margolis, A. Sanjyal, M. Kassa, A. Tame, JR. Goldberg, S. Pokhrel, G. Weiss. “MAESTRO: Multilingual AI-driven Educational System for TRaining and Online learning”, Proceedings of the 60th Hawaii International Conference on System Sciences (HICSS), Hawaii, USA, January 5-8, 2027
  2. E. Roginek, J. Kulesza, Y. Zhao, “FX-DRU: Learning from Synthetic Monte Carlo Radiographs for Zero-Shot Chest X-ray Processing”, Proceedings of the 60th Hawaii International Conference on System Sciences (HICSS), Hawaii, USA, January 5-8, 2027

2026

  1. E. Roginek, J. Kulesza, nd Y. Zhao, “A Machine Learning Approach for Denoising Monte Carlo Synthetic Radiographs”, Under review (minor revision), 2026
  2. J. Warren , Utsav Patel, G. Weiss, and Y. Zhao. “Identity-Based Description Bias in AI-Generated Alternative Text”, Findings of the Association for Computational Linguistics (EMNLP), Budapest, Hungary, October 24-29, 2026
  3. Y. Xu, Y. Zhao, R. Luo, G. Weiss, “SynTeX: Data-Efficient LaTeX OCR via Synthetic Pretraining and Limited Fine-Tuning”, Proceedings of the IEEE International Conference on Image Processing (ICIP), Tampere, Finland, September 13-17, 2026
  4. Y. Ding, Q. Wang, F. Lopez, Y. Wu, A. Zhang, and Y. Zhao, “A Machine Learning Approach to Examine Personality, Adjustment, and Engineering Identity Among College Engineering Students”, Journal of Civil Engineering Education, 2026 (In Press)
  5. Y. Liu, Y(uan) Li, Y(anjun) Li, and Y. Zhao, “V-SLOPE: Video Speech and Linguistic Optimization for Presentation Enhancement”, 30th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Dublin, Irland, September 9-11, 2026

2025

  1. 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]
  2. 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]
  3. 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]
  4. S. Do , A. Chu, Y. Zhao , and Y. Li, "Stock Market Forecasting with Pretrained Deep Learning Models," IEEE BigDataService, 2025 [Online]
  5. Y. Li , Y. Liu , and Y. Zhao , " A-CLAPS: Automatic Correction of Language and Pronunciation Errors in Slide-based Presentations," IEEE COMPSAC, 2025 [Online]
  6. D. Cordero , and Y. Zhao , P. Diaz , G. Weiss, "Unveiling Bias: Analyzing Race and Gender Disparities in AI Generated Imagery," IEEE COMPSAC, 2025 [Online]

2024

  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. 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
  3. 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
  4. 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
  5. 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

2023

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. Y. Zhao , Z. Du , S. Xu , Y. Chen , J. Mu , and M. Ning, "Social Media, Market Sentiment and Meme Stocks," , IEEE COMPSAC, 2023 [Online]
  7. F. Martinez , and Y. Zhao , "Integrating Multiple Visual Attention Mechanisms in Deep Neural Networks," IEEE COMPSAC, 2023 [Online]

2022

  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 , 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. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. Y. Wang , Y. Wang , C. Zhong , and Y. Zhao, "Assessing Deep Learning Approaches in Detecting Masked Facial Expressions," IEEE COMPSAC, 2022 [Online]

2021

  1. M. He , X. Wang , and Y. Zhao , "A calibrated deep learning ensemble for abnormality detection in musculoskeletal radiographs," Scientific Reports, 2021 [Online]
  2. 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]
  3. 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]
  4. S. Bai and Y. Zhao "Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches," Systems, 2021 [Online]
  5. 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]
  6. 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]
  7. H. Yuan , W. Zheng , S. Yun , and Y. Zhao , "Parallel Deep Neural Networks for Musical Genre Classification: A Case Study," IEEE COMPSAC, 2021 [Online]
  8. Y. Shi , Z. Wu , S. Zhang , H. Xiao, and Y. Zhao , "Assessing Palliative Care Needs Using Machine Learning Approaches," IEEE COMPSAC, 2021 [Online]

2020

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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
  7. A. Jorge, Z. Wu , T. Chowdhury , Y. Zhao , "Exploration of Machine Learning Methods in Predicting Systemic Lupus Erythematosus Hospitalizations," ACR Convergence, 2020 [Online]

2019

  1. 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]
  2. Y. Zhao , T. Chitnis, and T. Doan , "Ensemble Learning for Predicting Multiple Sclerosis Disease Course," The 15th International Conference on Data Science, 2019, [PDF]
  3. 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]

2017

  1. 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]

2016

  1. 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]

2015

  1. 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]

2014

  1. 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]
  2. 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]

2000

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