Research Insights

Technical writing on AI reasoning, interpretability, and building trustworthy systems.

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Research Foundation

Neuro-Symbolic AI and Causal Inference: My Approach to Legal Reasoning

How I'm combining neural pattern recognition with symbolic causal reasoning to build "glass box" legal AI systems. This article explains the technical foundation of my MS thesis work at Cornell—bridging interpretability with systematic reasoning through attention analysis and causal mapping.

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Fundamentals

Understanding Reasoning in Language Models

An introduction to how reasoning models work—from Chain-of-Thought to Tree-of-Thought approaches. Covers the fundamentals of o1, o3, and DeepSeek R1, along with current limitations including hallucination rates (33% for o3) that my research aims to address.

AI as Search: The Foundation of Intelligent Problem-Solving

From GPS navigation to game-playing computers—understanding how search algorithms underpin modern AI systems. Covers A*, minimax, alpha-beta pruning, and MCTS. Educational content from my teaching work at Cornell.

Data Discovery & Search Infrastructure

How do we find the right data in a world drowning in datasets? Exploring inverted indexes, query-by-example, Bloom filters, and federated data marketplaces. From coursework on data systems at Cornell.

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