Abstract: 

Machine learning is changing our world at an accelerating pace. In this talk, we will discuss the latest developments in machine learning research viewed through the lens of the finance industry. There are issues that commonly arise in sophisticated applications of machine learning in finance: interpretability, causality, nonstationarity, sample efficiency, etc. Methods for addressing these issues are rapidly advancing; some are just on the cusp of being practical. We will review some recent papers on these topics and touch upon key applications in processing unstructured data, such as natural language understanding. Finally, we will look at some possible future directions for the application of machine learning methods in finance. The talk will conclude with a Q&A session.

Bio: 

Gary is the Head of Quant Technology Strategy in the Office of the CTO at Bloomberg. Prior to taking on this role, he created and headed the company’s Machine Learning Engineering group, leading projects at the intersection of computational linguistics, machine learning and finance, such as sentiment analysis of financial news, market impact indicators, statistical text classification, social media analytics, question answering, and predictive modeling of financial markets.

Prior to joining Bloomberg in 2007, Gary had earned degrees in physics, mathematics, and computer science from Boston University.

He is engaged in advisory roles with FinTech and Machine Learning startups and has worked at a variety of technology and academic organizations over the last 20 years. In addition to speaking regularly at industry and academic events around the globe, he is a member of the KDD Data Science + Journalism workshop program committee and the advisory board for the AI & Data Science in Trading conference series. He is also a co-organizer of the annual Machine Learning in Finance conference at Columbia University.