Sep 23–26, 2019
Ying Yau

Ying Yau
Distinguished Data Scientist, Walmart Labs

Jeffrey Yau is a distinguished data scientist at WalmartLabs, where he leads data science for the store technology department. Previously, he was the chief data scientist at AllianceBernstein, a global asset-management firm; the vice president and head of data science at Silicon Valley Data Science, where he led a team of PhD data scientists helping companies transform their businesses using advanced data science techniques and emerging technology; the head of risk analytics at Charles Schwab; director of financial risk management consulting at KPMG; assistant director at Moody’s Analytics; and assistant professor of economics at Virginia Tech. Jeffrey’s 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 PhD and an MA in economics from the University of Pennsylvania and a BS in mathematics and economics from UCLA.

Sessions

11:20am12:00pm Wednesday, September 25, 2019
Location: 1A 06/07
Ying Yau (Walmart Labs)
Time series forecasting techniques can be applied in a wide range of scientific disciplines, business scenarios, and policy settings. Jeffrey Yau details 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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