Personal information
Full name: Brenda Jimenez
User name: bcjg2367
Organization memberships
Not a member of any organization.
Awards
First 500 volunteers
Volunteer background
Education: Master's on Mathematics (NYU)
Github profile
Volunteer availability
Start date: June 22, 2020
End date: None
Hours available per week: 20
Volunteering interests
No stated preferences.
Skills
Skill
Beginner
Intermediate
Expert
Python
Computer Science (Algorithms)
C/C++/C#
Data in Text Files
Data in Relational Databases
SQL
Social Science (Economics, Sociology, etc.)
Experience working on real-world problems using data
Data Visualization
Data Analysis
Decision Trees
SVMs
Random Forests
Time Series Models
Regression Discontinuity
Instrumental Variables
Matlab
R
Stata

Volunteer projects

Project name Organization name Social impact area Project summary Task name Task status
Identifying economic and financial incentives for forest and landscape restoration in Latin America using Natural Language Processing World Resources Institute Education Forest and landscape restoration is a cross-cutting agenda that traverses sectors such as agriculture, forestry, water and natural resources. While this cross-cutting nature makes restoration an attractive policy measure for carbon sequestration, mitigation, and adaptation, it complicates policy analysis. The sheer volume of text impedes researchers and decision makers from identifying misalignment and monitoring evolving policy and agenda shifts. Analyzing such a large corpus of documents exacerbates policy analysis’ transparency, objectivity, access, and scalability. Our proposal is to standardize and scale policy analysis, alignment, and agenda setting with natural language processing (NLP). A previous proof-of-concept we developed demonstrated the utility of NLP to quickly summarize agenda-specific information from policies. The aim of this project would be to identify financial and economic incentives to support enabling conditions for Nature Based Solutions. Supervised NLP and data exploration Started
Identifying economic and financial incentives for forest and landscape restoration in Latin America using Natural Language Processing World Resources Institute Education Forest and landscape restoration is a cross-cutting agenda that traverses sectors such as agriculture, forestry, water and natural resources. While this cross-cutting nature makes restoration an attractive policy measure for carbon sequestration, mitigation, and adaptation, it complicates policy analysis. The sheer volume of text impedes researchers and decision makers from identifying misalignment and monitoring evolving policy and agenda shifts. Analyzing such a large corpus of documents exacerbates policy analysis’ transparency, objectivity, access, and scalability. Our proposal is to standardize and scale policy analysis, alignment, and agenda setting with natural language processing (NLP). A previous proof-of-concept we developed demonstrated the utility of NLP to quickly summarize agenda-specific information from policies. The aim of this project would be to identify financial and economic incentives to support enabling conditions for Nature Based Solutions. Create project Repo Pending review