Personal information
Full name: Carlos Mougan
User name: cmougan
Organization memberships
Not a member of any organization.
Awards
No awards received yet.
Volunteer background
Education: Master's on Msc Mathematical Modeling (Universidad Autonoma de Barcelona)
LinkedIn profile
Github profile
Portfolio
Volunteer availability
Start date: Aug. 24, 2020
End date: None
Hours available per week: 6
Volunteering interests
No stated preferences.
Skills
Skill
Beginner
Intermediate
Expert
Computer Science (Algorithms)
Python
SQL
Data in Text Files
Data in Relational Databases
Text Data (NLP)
Data > 1TB
Network/Graph Data
Geospatial
Experience working on real-world problems using data
Data Visualization
Experimental Methods (RCTs, A/B testing, etc.)
Social Science (Economics, Sociology, etc.)
Data Analysis
Neural Networks / Deep Learning
Time Series Models
Unsupervised models
Semi-Supervised models
Random Forests
Decision Trees
SVMs
Matlab
Julia

Volunteer projects

Project name Organization name Social impact area Project summary Task name Task status
Creating a well-being data layer using machine learning, satellite imagery and ground-truth data World Resources Institute Education Conducting economic surveys requires huge resources; thus, modern means of acquiring this information using publicly available data and open source technologies create the possibilities of replacing current processes. Satellite images can act as a proxy for existing data collection techniques such as surveys and census to predict the economic well-being of a region. The aim of the project is to build on a prototype that was created using Census data and LandSat data for India. In the next iteration, opportunities for Demographic Health Surveys, Open Street Map, Sentinel and nightlight data will be explored. The initial prototype created a model that had an accuracy of almost 70 percent. The aim is to create a model for India that can be adapted and scaled to other countries. Project scoping Started