AI, machine learning/statistical learningdata mining/pattern recognition, missing data analysis, power analysis, visual analytics, computer graphics, extended reality (xR), digital health/virtual health, behavioral trajectory modeling, pattern recognition, visualization and validation for longitudinal studies with missing data, wearable biosensor data analytics
julia.fang@yu.edu I 646-592-8024 | 205 Lexington Avenue, 7th FL, NYC
Dr. Julia Hua Fang is a professor and program director in the M.S. in Artificial Intelligence at the Katz College of Science and Health at Yeshiva University, where she leads the Computational Tended Immersive Intelligence (CIXI) Lab. Dr. Fang’s research centers on artificial intelligence, machine learning and statistical learning, with a focus on behavioral trajectory pattern recognition, missing data analysis and multisite longitudinal digital trials. She is the inventor of the patent “System and Methods for Trajectory Pattern Recognition.” Her work advances AI-driven solutions for digital health, digital twins, wearable biosensor analytics and the Internet of Things (IoT), integrating computational intelligence with real-world applications and beyond.
She has maintained continuous research funding from U.S. federal agencies such as NIH and for more than 15 years and has served on review panels for these organizations. Dr. Fang is an active ASA member, a senior member of IEEE, and has been selected as a 2025–2026 IEEE Communications Society Distinguished Lecturer. She has contributed extensively to the IEEE community, serving on editorial boards such as the IEEE Internet of Things Journal and IEEE Transactions on Big Data. She is currently an area editor for the IEEE IoT Journal, focusing on Artificial Intelligence for IoT, and also serves on its steering committee. She has participated in technical program committees for major ACM/IEEE and international conferences in AI, machine learning, data mining, and connected health. In addition, she serves on the IEEE HEALTHCOM Steering Committee and has been an Advisory Group member of the IEEE Standards Association Healthcare and Life Sciences Practice Program, contributing to the development of standards in emerging AI and machine learning technologies.
Dr. Fang holds a Ph.D. in statistics, specifically on computational statistics (a computational science at the interface of statistics and computer science) from Ohio University.
Lomasov S**, Fang H, Wang H. Federated Choquet Regression with LASSO for Outcome Prediction in Multisite Longitudinal Trial Data. ACM Trans Comput Healthc. 2026 Jan;7(1):1. doi: 10.1145/3761824. Epub 2026 Jan 14. PMID: 41948010; PMCID: PMC13052512.
Ngo H*, Fang H, Rumbut J, Wang H. Federated Fuzzy Clustering for Decentralized Incomplete Longitudinal Behavioral Data. IEEE Internet Things J. 2024 Apr 15;11(8):14657-14670. doi: 10.1109/jiot.2023.3343719. Epub 2023 Dec 18. PMID: 38605934; PMCID: PMC11006372.
Fang H, Zhang Z*. An Enhanced Visualization Method to Aid Behavioral Trajectory Pattern Recognition Infrastructure for Big Longitudinal Data. IEEE Trans Big Data. 2018 Jun;4(2):289-298. doi: 10.1109/TBDATA.2017.2653815. Epub 2017 Jan 16. PMID: 29888298; PMCID: PMC5990046.
Fang H. MIFuzzy Clustering for Incomplete Longitudinal Data in Smart Health. Smart Health (Elsevier Journal). 2017 Jun;1-2:50-65. doi: 10.1016/j.smhl.2017.04.002. Epub 2017 Apr 27. PMID: 28993813; PMCID: PMC5631546.
Fang, H., Rizzo, M. L., Wang, H., Espy, K. A., & Wang, Z. A new nonlinear classifier with a penalized signed fuzzy measure using effective genetic algorithm. Pattern Recognition. 2010; 43(4), 1393-1401. PMCID: PMC2838252.
