As data science transforms industries worldwide, the evolving landscape of the field is explored through a focus on integrating diverse data sources, from structured to unstructured, and leveraging emerging technologies. Key challenges such as data integration, ethical concerns, and privacy in data collection and usage are at the forefront of discussions. Interdisciplinary collaboration will drive exploration of the latest advancements in machine learning, AI, and their applications across areas like computational biology, natural language processing, and computer vision. Leading experts from academia and industry convene to foster robust dialogue to bridge the gap between theory and real-world solutions, covering topics from data ethics and privacy to machine learning and beyond.
Convened by
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Aaron Kaufman, Assistant Professor of Political Science, NYUAD
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Hanan Salam, Assistant Professor of Computer Science, NYUAD
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Pierre Youssef, Program Head of Mathematics; Associate Professor of Mathematics; Global Network Associate Professor of Mathematics, Courant Institute of Mathematical Sciences, NYUAD
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Keith Ross, Professor of Computer Science, NYUAD
