Real World Data

Real-world data (RWD) research focuses on leveraging data from real-world settings to train and evaluate machine learning models, addressing limitations of idealized datasets. Current efforts concentrate on developing methods to handle the complexities of RWD, including missing data, noise, and biases, often employing techniques like knowledge distillation, variational inference, and generative models (e.g., diffusion models) to improve model performance and robustness. This research is crucial for bridging the gap between theoretical advancements and practical applications across diverse fields, from healthcare and autonomous driving to finance and environmental sustainability, enabling more reliable and impactful AI systems.