Developed Socioeconomic Embedding and Thesis Supervision Plan

• Day: 2025-02-27
• Time: 03:00 to 05:15
• Project: Teaching
• Workspace: WP 1: Strategic / Growth & Development
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Socioeconomic Embeddings, Thesis Supervision, Autoencoder, Feature Selection, Machine Learning

Description

Session Goal: The main objective was to develop a comprehensive plan for training machine learning models using socioeconomic data embeddings, and to outline a structured supervision plan for thesis development.

Key Activities:

• Explored training methodologies for Random Forest models on autoencoder-generated embeddings, focusing on high-dimensional socioeconomic data.
• Developed an iterative approach for enhancing embeddings using census and survey data, refining features to improve model accuracy.
• Discussed the importance of feature addition order in autoencoders and strategies for optimal feature selection.
• Finalized a feature subset for socioeconomic embeddings to create a stable low-dimensional representation.
• Analyzed the dimensionality of datasets with mixed variable types and the need for higher dimensionality in encoded representations.
• Outlined a thesis plan comparing feature selection techniques (RF, PCA, Autoencoders) for income prediction.
• Incorporated time and spatial data into the thesis model, and evaluated the need for Fourier features in time series analysis.
• Developed a thesis supervision plan, detailing essential tasks for students to ensure data quality and effective modeling processes.

Achievements:

• Established a detailed framework for socioeconomic data embedding and analysis.
• Created a structured thesis supervision plan to guide students in their research efforts.

Pending Tasks:

• Further exploration of frequency spectrum analysis in autoencoder embeddings to detect seasonal effects.
• Continuation of thesis development and refinement of the outlined methodologies.

Evidence

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