Program
Friday, 29 March 2019
| 08:30am | Welcome: Coffee, bagels, general greetings |
| 09:00am | Sol Hsiang |
| Generalizing Global Observation with Satellite Imagery and Machine Learning | |
| 09:30am | Presentation block (3×20min) |
| Andy Hultgren The impacts of climate change on global grain production, accounting for adaptation costs and benefits |
|
| Ariel Ortiz-Bobea A near-real time forecasting system of US crop yields. [slides] |
|
| Andrew Crane-Droesch Semiparametric neural networks in Keras/Tensorflow. [slides] |
|
| 10:30am | Coffee break |
| 10:45am | Dan Hammer |
| Last-mile environmental-data communication [slides] | |
| 11:15am | Presentation block (3×20min) |
| Esha Zaveri The Nitrogen Legacy: Long-Term Effects of Water Pollution on Human Capital |
|
| Ludovica Gazze and Olga Rostapshova From Water Cops to Smart Meters: Designing Water Conservation Policy in the New Era of Automated Enforcement |
|
| Eric Edwards Hydrologic Data in Water Economics: Institutions and Identification. [slides] |
|
| 12:15pm | Lunch |
| 01:00pm | Keynote: Meredith Fowlie |
| From Big Data to Big Decisions. [slides] | |
| 01:30pm | Coffee break |
| 01:45pm | Dan Goldberg |
| Using satellite data to estimate air pollutant emissions and concentrations at high spatiotemporal resolution. [slides] | |
| 02:15pm | Jeff Chen |
| Machine learning, weather predictions, and school closures. [slides] | |
| 02:45pm | Coffee break |
| 03:00pm | Kathy Baylis |
| Machine learning. [slides] | |
| • Machine learning in agricultural and applied economics | |
| • Identifying effects of farm subsidies onstructural change using neural networks | |
| 03:30pm | Jacob LaRiviere |
| Predicting Famine: The Importance of ML Model Interpretability and Back-End Data Structures | |
| LIME: Local Interpretable Model-Agnostic Explanations | |
| 04:00pm | Coffee break |
| 04:15pm | Lightning round #1 |
| 05:15pm | Break |
| 05:45pm | Reception: Drinks, tacos, chat |
Saturday, 30 March 2019
| 08:30am | Welcome2: Coffee, bagels, general greetings |
| 09:00am | Peter Christensen |
| Data Science in Field Experiments: Putting Bots to Work on Housing Discrimination | |
| 09:30am | Presentation block (3×20min) |
| Katherine Meckel Can Machine Learning Improve Targeting of Environmental Inspections? Evidence from EPA’s RCRA Program. [slides] |
|
| Sam Stolper Machine-Learning the Impacts of Behavioral Interventions: Evidence from Household Energy Use [slides] |
|
| Leslie Martin The Margins of Response to Road Use Prices |
|
| 10:30am | Coffee break |
| 10:45am | Lightning round #2 |
| 12:00pm | Lunch |
| 12:45am | Ivan Rudik |
| Valuing Science Policy: Dynamic Decision-Making With Generalized Bayesian Learning. [slides] | |
| 01:15pm | Presentation block (3×20min) |
| Antonio Bento A New Approach to Measuring Climate Change Impacts and Adaptation. [slides] |
|
| Felipe Jordan Land tenure reforms and sustainable development: Evidence from a natural experiment in Southern Chile |
|
| David McLaughlin Remote Sensing and Machine Learning to Study the Effect of Land Titling on Land Use in Mexico [slides] |
|
| 02:15pm | Coffee break |
| 02:30pm | Meha Jain |
| Using Satellite Data for Sustainable Agriculture. [slides] | |
| 03:00pm | Presentation block (2×20min) |
| Justin Kirkpatrick Peer Effects In Rooftop Solar Adoptions [slides] |
|
| Thanh Nguyen AI-based Solutions for Wildlife Security [slides] |
|
| 03:40pm | Wrap up! |
| 04:30pm | Meet up for drinks/food/chat |
| Old Town Brewing/Pizza | |
| 226 NW Davis St, Portland, Oregon | |
| 🍻 👋 |
Please contact Ed Rubin with any additional questions.