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Marcos López de Prado has over 20 years of experience developing investment strategies with the help of machine learning algorithms and supercomputers. He has recently sold his patents to AQR Capital Management, where he was a principal and AQR’s first head of machine learning. Marcos also founded and led Guggenheim Partners’ Quantitative Investment Strategies business, where he developed high-capacity investment algorithms that consistently delivered superior risk-adjusted returns, receiving up to $13 billion in assets. Concurrently with the management of investments, between 2011 and 2018 Marcos was a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). He has published dozens of scientific articles on machine learning and supercomputing in the leading academic journals, and SSRN ranks him as the most-read author in economics. Among several monographs, Marcos is the author of the graduate textbook Advances in Financial Machine Learning (Wiley, 2018). Marcos earned a PhD in financial economics (2003), a second PhD in mathematical finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University, where he teaches a financial machine learning course at the School of Engineering. In 2019, he received the ‘Quant of the Year Award’ from The Journal of Portfolio Management.