Machine Learning and Data Science for Economists

 

November 9-10, 2022

2 days, 8:30 AM – 4:30 PM
Grand Hyatt Seattle
Seattle, WA

This course introduces applied economists to new analytical methods, which lie at the intersection of traditional statistics, machine learning, and computer science, from the perspective of econometric analysis.  More course information

Please note: Proof of COVID-19 vaccination is required of all participants.

Registration:

NABE Member: $1,600

U.S. Government Employee: $1,675

Non-Member: $1,750


REGISTER HERE

OR

and email to [email protected]. Please note: a $25 processing fee will be added to registrations sent in via this form.

NOTE: To be eligible for a refund less $50 fee, registration cancellation must be received in writing by October 12, 2022. Refunds will not be permitted past that date. Registration transfers can be considered upon request; however, a $75 processing fee will be assessed for all registration transfers. NABE reserves the right to cancel the seminar if sufficient registration is not achieved. Questions? Please contact NABE at [email protected] or 202-463-6223.

Location 

Grand Hyatt Seattle
721 Pine Street
Seattle, WA 98101

NABE has secured a block of discounted rooms at the Grand Hyatt Seattle at the rate of $229/night (plus tax). To make a reservation, book online here. 

Please note: The deadline to make reservations is October 12, 2022, or when the block reaches capacity, whichever comes first.

 

About the Instructor:




Brian Quistorff is a Research Economist at the Bureau of Economic Analysis. Formerly, he was an Economist in the Office of the Chief Economist in the AI + Research division of Microsoft. This group combined Economists with traditional Data Scientists to address difficult challenges facing Microsoft. He worked across many products groups, including Office and Gaming, and on external engagements, including with the World Bank. He specializes in embedding Machine Learning into existing Econometric methods to both improve the quality of estimation/causal inference and to save time/reduce errors in model selection. He also works to open source generic tools to benefit the broader analytics community. He holds a PhD in Economics from the University of Maryland, a MA of Economics from the University of British Columbia, and a BS in Computer Science from Stanford University.