Lijing Wang

Lijing Wang is an Assistant Professor of Data Science at the New Jersey Institute of Technology (NJIT). She specializes in interdisciplinary research at the intersection of artificial intelligence, epidemiology, and public health. Her work emphasizes data-driven approaches to forecasting infectious disease dynamics, integrating machine learning with theoretical epidemiological models.

Education:

• Ph.D. in Computer Science, University of Virginia (2021)
• M.S. in Computer Science, Chinese Academy of Sciences (2013)
• B.S. in Software Engineering, Dalian University of Technology (2010)

Research focuses on epidemic forecasting using ensemble modeling, graph neural networks, and causal inference. Key topics include:

• COVID-19 and influenza prediction using mobility data
• AI-driven disease surveillance systems
• Policy impact analysis of pharmaceutical/nonpharmaceutical interventions
• Cross-national epidemic modeling

Publications consistently address forecasting challenges through innovative methodological combinations - e.g., Bayesian ensemble techniques, causal graph approaches, and multi-scale human mobility analysis. Recent work emphasizes real-time prediction accuracy improvements for public health decision-making.

No scientific awards explicitly noted in text. Active in collaborative research involving public health agencies and international institutions.