معرفی
Dr. Yue Ning is an Assistant Professor in the Department of Computer Science at Stevens Institute of Technology, where she conducts cutting-edge research at the intersection of machine learning, graph mining, and data analytics. Her work focuses on social informatics, healthcare, and financial technologies, with an emphasis on developing predictive and generative methods that capture spatio-temporal, dynamic, and interpretable patterns in large-scale datasets. Dr. Ning is affiliated with the Stevens Institute for Artificial Intelligence (SIAI), Center for Research Toward Advancing Financial Technologies (CRAFT), and Semcer Center for Healthcare Innovation (CHI).
- PhD in Computer Science (2018), Virginia Tech
Dr. Ning's research spans several interconnected domains in artificial intelligence. Her work in Graph Neural Networks focuses on designing new architectures for dynamic and heterogeneous graph data, with applications in continual learning and knowledge graph reasoning. In healthcare AI, she develops methods for personalized care, epidemic forecasting, and medical representation learning. Her work on socially responsible AI addresses fairness in finance and medicine, fake news detection, and hate speech identification. She also explores transfer learning and federated learning frameworks for domain adaptation and bias mitigation in imbalanced data scenarios.
Analysis of Dr. Ning's recent publications reveals a strong focus on bridging graph neural networks with causal inference for robust event prediction. Her work increasingly integrates large language models with structured graph data for healthcare applications, particularly in medical coding and clinical record analysis. A notable trend is her growing emphasis on fairness and privacy in machine learning systems, reflected in multiple publications on graph fairness, certified unlearning, and bias mitigation in recommender systems.
Scientific Awards:
- NSF CAREER Award (2021-2026) for deep interpretable predictions for multi-scope temporal events
- NSF CRII Award (2020-2024) for learning dynamic graph-based precursors for event modeling
- Early Career Award for Research Excellence from Stevens Institute of Technology
- Research fellowship from NIH-sponsored AIM-AHEAD program
Dr. Ning actively mentors PhD and undergraduate students, with several successful PhD graduates including Songgaojun Deng and Chang. Her research is supported by multiple significant grants, including three NSF awards totaling over $1.5 million, NVIDIA GPU grants, and institutional support from Stevens Institute of Technology. She has served as Principal Investigator on projects exploring dynamic graph learning for event prediction, interpretable temporal event modeling, and domain-informed generative frameworks for medical knowledge learning.
Dr. Ning leads research within the Stevens Institute for Artificial Intelligence, where she contributes to initiatives in healthcare AI and socially responsible machine learning. Her lab focuses on developing frameworks that integrate domain knowledge with deep learning for applications in healthcare, social science, and finance. Current projects include deep graph learning for dynamic and heterogeneous data, transfer learning for domain adaptation, and machine learning methods for healthcare applications including personalized care and epidemic forecasting.
