Data Driven Learning

Data-driven learning focuses on using large datasets to train models that can predict outcomes, control systems, or discover causal relationships, often outperforming traditional methods. Current research emphasizes developing robust algorithms, such as neural networks (including transformers and LSTMs), Koopman operators, and kernel-based methods, to address challenges like data scarcity, spurious correlations, and the need for uncertainty quantification. This approach is proving impactful across diverse fields, from autonomous navigation and protein stability prediction to medical device control and video enhancement, by enabling more accurate and efficient solutions to complex problems.