Machine Learning in Finance with Charles Elkan

Machine learning as an academic research field is over 60 years old. So why is there so much excitement about it nowadays in the business world? What can technology do now that was impossible ten years ago? In what ways are humans still fundamentally superior? If we want to apply machine learning in trading or in banking, where are the best opportunities? What are ten different traps to avoid falling into? How should an applied machine learning project be directed and organized? Should we use deep learning?

Charles Elkan is Managing Director and Global Head of Machine Learning at Goldman Sachs in New York. From 2014 to 2018 Elkan was the first Amazon Fellow, leading a team of over 30 scientists and engineers in Seattle, Palo Alto, and New York to conduct research and development in machine learning for both e-commerce and cloud computing.

Elkan is an adjunct professor of computer science at UC San Diego, where he was previously a tenured full professor. Elkan earned his doctorate at Cornell University and his undergraduate degree at Cambridge University. His students have gone on to faculty positions at universities that include Columbia and Stanford, and to leading industry positions that include managing the largest app store in the world.

Elkan is also the founder and CEO of a fintech company that has not yet been announced publicly.

Charles Elkan:

Professor Vincent R. Nijs:

Rady School of Management: