Professor López de Prado Appointed Global Head of Quantitative Research and Development

Prof. López de Prado's department is tasked with applying a systematic, science-based approach to developing and implementing investment strategies. This multi-disciplinary team will draw on the latest scientific developments, in areas such as machine learning, big data and high-performance computing.

Prof. López de Prado is a recognized expert in financial machine learning. His innovations have covered and connected a wide range of subjects, including overfitting prevention, signal processing, quantum computing, stochastic optimal control, robust convex optimization, and market microstructure. These innovations have resulted in dozens of scientific articles in the leading academic journals, and 13 patents, most of which have been acquired by asset management companies. Prof. López de Prado has published several popular textbooks, including Advances in Financial Machine Learning (Wiley, 2018), and Machine Learning for Asset Managers (Cambridge University Press, 2020). He is a founding co-editor of The Journal of Financial Data Science, and the Social Science Research Network ranks him as one of the most-read authors in economics. A prolific collaborator, Prof. López de Prado has an Erdős #2 according to the American Mathematical Society.

Since 2011, Prof. López de Prado has been a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science), where he has conducted research on scientific supercomputing. In the year 1999 he received the National Award for Academic Excellence from the Government of Spain, and in 2019, The Journal of Portfolio Management named him "Quant of the Year." Last December, the U.S. Congress invited him to testify on AI policy.

At Cornell University, Prof. López de Prado teaches Financial Machine Learning within the Operations Research and Information Engineering program (ORIE 5256), and directs multiple student projects. In addition, he is a frequent speaker at PhD programs and Cornell seminars.