The workshop in environmental
economics and data science

air quality machine learning inequality remote sensing visualization reproducibility health spatial data analysis truth

Join us in Portland, Oregon on March 29 and 30 (2019) for an intimate workshop focused on the methodological frontiers of environmental economics and data science.

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when + where

29–30 March 2019

The University of Oregon's White Stag Building
Portland, Oregon

If an awesome, methods-first conference is not enough, how about a great neighborhood in a terrific city?

Note: Applications closed 31 December 2018.
Please contact with any additional questions.

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speakers

Keynote speaker and presenters currently on the agenda.

Keynote speaker

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Meredith Fowlie

Environmental Economist
University of California, Berkeley

Presenters

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Kathy Baylis

Environmental Economist
University of Illinois Urbana-Champaign

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Jeff Chen

Chief Innovation Officer/Data Scientist
Bureau of Economic Analysis (BEA)

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Peter Christensen

Environmental Economist
University of Illinois

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Dan Goldberg

Atmospheric Scientist
Argonne National Laboratory

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Dan Hammer

Environmental Data Scientist
National Geographic Fellow; Earthrise Media

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Solomon Hsiang

Climate Economist
University of California, Berkeley

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Meha Jain

Environmental Informatics and Justice
University of Michigan

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Jacob LaRiviere

IO and Behavioral Economist
Microsoft; University of Washington

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Ivan Rudik

Environmental Economist
Cornell University

Topics

Some of the topics that the workshop will feature...

Remote sensing

Increasingly, environmental analysis relies upon mixtures of satellite monitors, in-situ monitors, and a number of other monitoring devices.

Geospatial analysis

High-resolution spatial data, spillovers, externalities, ...

Machine learning

It's hard to spell "data science" without "machine learning."

Visualization

Creating stunning, accurate, and informative graphics.

Workflow and transparency

Cloud-computing resources, data management tools for large datasets, laying the foundations for open/transparent science

Fun

And we plan to make time for fun... it is Portland, afterall. Donuts, coffee, pickles, ...