My name is David and I am a Portuguese, twenty-two-year-old student, studying a master’s degree in Data Science and Advanced Analytics at NOVA Information Management School (IMS) in Lisbon, Portugal.
I am currently developing my thesis on document clustering and retrieval under a scientific research grant while working as a teaching assistant for the Data Mining master level course at NOVA IMS.
I am looking for new projects in the field of Data Science that can create value and impact to real persons and where I can collaborate and learn with other Data Scientists from around the globe.
Personal information
Full name: David Silva
User name: DavidSilva98
Organization memberships
Not a member of any organization.
Awards
No awards received yet.
Volunteer background
Volunteer availability
Start date: Sept. 7, 2020
End date: None
Hours available per week: 5
Volunteering interests
No stated preferences.
Skills
Skill
Beginner
Intermediate
Expert
Python
Java
Data in Relational Databases
Text Data (NLP)
SQL
Data in Text Files
Network/Graph Data
GIS tools
Data Visualization
Experience working on real-world problems using data
Data Analysis
Decision Trees
Neural Networks / Deep Learning
Unsupervised models
SVMs
Causal inference
Semi-Supervised models
Time Series Models
Random Forests
R
SAS
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. | Data gathering and documentation | Pending review |