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Webinar: Deep Probabilistic Programming for finance

DPP webinar

Deep probabilistic programming (DPP) combines three fields: Bayesian statistics and machine learning, deep learning (DL), and probabilistic programming. In this webinar, our expert panel discussed DPP tools and related theory relevant for Bayesian forecasting and decision making with financial time series data and other types of financial data (e.g. limit order books, news etc).

Topics included:

  • How does DPP differ conceptually from frequentist statistics and machine learning?
  • Why represent probabilistic models as a computational graph?
  • What are the DPP tools, methodologies and applications that are most important for finance?
  • Is DPP the future for risk modeling using complex datasets?

The panel:

Moderator: Matthew Dixon
Assistant Professor of Finance, Illinois Institute of Technology

Dr. Matthew Dixon is a co-founder of the Thalesians and an Assistant Professor of Finance at the Illinois Institute of Technology, Chicago. Matthew holds a PhD in Applied Math from Imperial College and has held visiting appointments at Stanford and UC Davis. He has published over 20 peer-reviewed papers including a recent article on deep learning in Algorithmic Finance.

David Bellot
Senior Machine Learning Researcher, Liquid Capital Group

David is a senior machine learning researcher at Liquid Capital Group, based in London. His main focus has been high-frequency and the market micro-structure for many years now. David has a Ph.D. in Bayesian Machine Learning from University Henry Poincare and was a postdoc in machine learning at INRIA Grenoble and UC Berkeley before moving into finance.

Tyler Ward
Local Search Modeler, Google

Tyler Ward currently works at Google on local search. He was previously Head of Mortgage Modeling at Royal Bank of Canada and an Executive Director at Morgan Stanley where he worked for 10 years modeling non-agency mortgage products. He obtained his M.A. in Mathematics from New York University and holds a B.A. in Physics from Columbia University.

Elijah DePalma
Quantitative Research Analyst, Charles Schwab Investment Management

Mr. Elijah DePalma, Ph.D., serves as a Quantitative Research Analyst at Charles Schwab Investment Management, Inc. Mr. DePalma is responsible for supporting production processes, enhancing investment models and contributing to external thought leadership. Prior to joining Schwab in August 2016, Mr. DePalma worked at Thomson Reuters for 4 years, where he led research projects utilizing News & Social Media analytics for behavioral applications, delivering research presentations and providing onboarding support for investment management clients over a wide range of investment horizons. He has delivered research presentations at international conferences and quant seminars, and has worked with leading academic researchers at the forefront of data science and behavioral finance applications.

Yam Peleg
Founder, Deep Trading

Yam Peleg is the founder of Deep Trading ltd. He was a quantitative high frequency trader for more than four years and deep trading is his second startup. He is also a major contributor to the python community who spoke at python conferences around the world, including PyCon, PyData ,SciPy and more.

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