I am interested in artificial intelligence and machine learning as practical tools for analysis, prediction, classification, automation, and decision support—especially in organizations that have historically lacked access to advanced technical capacity.
Theme
AI & Machine Learning
Contents
Applied rather than abstract
My interest in AI is primarily applied. I am less interested in treating artificial intelligence as an isolated technology than in understanding where it can strengthen existing organizational work. Useful applications may include automating repetitive analysis, identifying patterns in operational data, supporting forecasting, classifying records, or helping staff navigate complex information.
Human judgment remains central
AI systems can extend analytical capacity, but they do not eliminate the need for human judgment. Models reflect assumptions, training data, design choices, and institutional context. Their outputs therefore need to be interpreted alongside domain knowledge, organizational values, and the consequences of error.
Access to technical capacity
Advanced analytics has often been concentrated in large corporations, research institutions, and technology firms. I am interested in how nonprofit and public-serving organizations can gain practical access to these tools without requiring large technical departments. That means thinking about usability, governance, cost, staff capability, and responsible implementation.
Current direction
My work in this area includes machine-learning classification, forecasting, exploratory analytics, and AI-assisted analytical workflows. A continuing question is how organizations can use these tools to augment staff capacity while preserving transparency, accountability, and informed human decision-making.