Sep 23–26, 2019
Ying Yau

Ying Yau
Chief Data Scientist, AllianceBernstein

Jeffrey is a Distinguished Data Scientist at WalmartLabs, where leads data science for the store technology department. His prior roles include the Chief Data Scientist at AllianceBernstein, a global asset-management firm, Vice President and Head of Data Science at Silicon Valley Data Science, and senior leadership position at Charles Schwab Corporation and KPMG. He has also taught econometrics, statistics, and machine learning at UC Berkeley, Cornell, NYU, University of Pennsylvania, and Virginia Tech. Jeffrey is active in the data science community and often speaks at data science conferences and local events. He has many years of experience in applying a wide range of econometric and machine learning techniques to create analytic solutions for financial institutions, businesses, and policy institutions. Jeffrey holds a Ph.D. and an M.A. in Economics from the University of Pennsylvania and a B.S. in Mathematics and Economics from UCLA.

Sessions

11:20am12:00pm Wednesday, September 25, 2019
Location: 1A 06/07
Secondary topics:  Deep Learning, Financial Services, Temporal data and time-series analytics
Ying Yau (AllianceBernstein)
Time series forecasting techniques can be applied in a wide range of scientific disciplines, business scenarios, and policy settings. This session discusses the application of deep learning techniques to time series forecasting and compares them to time series statistical models when forecasting time series with trends, multiple seasonality, regime switch, and exogenous series. Read more.

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