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In this talk, I aim to illuminate the underemphasized yet critical dimension in machine learning: time. I contend that time harbors the potential to revolutionize machine learning methodologies and their applications in numerous domains from healthcare to engineering to finance. This presentation underscores the opportunities and challenges that emerge from integrating temporal dynamics into machine learning models, enriching prediction accuracy, inference robustness, causality, and conceptual understanding.
