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Natural and technological complex systems frequently emerge from numerous basic elements that interact to produce intricate collective patterns. To comprehend these behaviors, we employ statistical, mathematical, and computational modeling approaches, blending process-oriented and data-centric strategies for deeper insights and forecasting capabilities.
Beyond machine learning and other data-focused methods, we apply reverse engineering alongside mathematical and statistical modeling to tackle biological and engineering challenges. While often labeled as model-free, data-driven approaches essentially fine-tune model parameters, meaning their effectiveness depends entirely on the foundational model's robustness.
Ongoing research initiatives under this theme:
Deciphering and forecasting neurological disorders like Alzheimer's and Parkinson's disease
Examining the mental health consequences of UK flood events
Developing spatio-temporal models for HIV, Cholera, and COVID-19 to guide transmission analysis and policy formulation
Anticipating pedestrian movements to enhance connected vehicle systems
Simulating protein movement during cellular membrane transport processes