Research Portfolio

Building Transparent Systems

I focus on the assurance of generative AI systems, from dynamic evaluation of retrieval architectures to mechanistic assurance and monitoring of reasoning systems. Below is a closer look at selected works and publications.

Citations

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Recent Work

2024Completed

DynaRAG

A dynamic optimization framework that fine-tunes chunking strategies in retrieval-augmented generation systems to boost answer faithfulness and latency.

Executed 30,000+ experimental configurations across chunking, retrieval, and generation parameters
Developed a custom Seq2BoW evaluator that achieved 95% accuracy when scoring generative answers
Delivered an optimization loop that surpassed GPT-4o by up to 34% on faithfulness and context precision

Retrieval Augmented GenerationLM EvalOptimization

2025Completed

MERIT: Mechanistic Explainability of Reasoning Integrity and Transparency

Mechanistic interpretability framework for identification of domain-specific reasoning features over varying task complexities.

Developed six metrics to quantitatively track LLM reasoning output quality
Identified feature fracturing behavior into specialized sub-domains over increasing task difficulty
Discovered discrete feature-controlled reasoning modalities (e.g. calculative vs verbose) and demonstrated steering efficacy

Mechanistic InterpretabilityAI AssuranceLLM Reasoning

2025 - PresentIn progress

Resurgens

An AI system for reconstructing the Etruscan language using contrastive phonological modeling and unsupervised clustering.

Aligned Latin, Greek, and Etruscan data to hypothesize missing or uncertain forms
Implemented phonological constraints that guide generation toward linguistically plausible reconstructions
Built curated corpora tailored for low-resource modeling and evaluation

Computational LinguisticsLow-Resource NLPContrastive Modeling