Contextualized Machine Learning
Review on adaptive, interpretable, and generalizable AI systems
Contextualized Machine Learning Research
Towards Adaptive, Interpretable, and Generalizable AI Systems
Collaboration with the Adaptive Inference Lab at University of Wisconsin–Madison
Project Overview
Contributing to a comprehensive review on contextualized machine learning and its role in creating more adaptive, interpretable, and generalizable AI systems, with a focus on foundation models.
Research Focus
📚 Literature Review: Comprehensive analysis of context-aware methods in machine learning
🔍 Interpretability: Understanding how context improves model interpretability
🧩 AdaptInfer Framework: Contributing to the development of contextual awareness in data-driven modeling
🌐 Foundation Models: Exploring context in large-scale AI systems
Key Concepts
Contextualized ML refers to machine learning approaches that:
- Adapt to different environments and scenarios
- Incorporate situational and temporal information
- Improve generalization across domains
- Enhance model interpretability through context
Applications
The research explores applications across:
- Healthcare and clinical decision-making
- Personalized recommendations
- Transfer learning and domain adaptation
- Time-series forecasting
- Multi-task learning
Collaboration
Advisor: Dr. Ben Lengerich, University of Wisconsin–Madison
Duration: September 2024 - September 2025
Repository: GitHub - AdaptInfer/context-review
Impact
This review aims to:
- Synthesize current understanding of contextualized ML
- Identify gaps and future research directions
- Provide framework for developing context-aware AI systems
- Bridge theory and practical applications
Contributing to advancing the field of contextualized machine learning for more robust and interpretable AI systems.