Resource-Efficient Foundation Models for Code
Quantization, parameter-efficient fine-tuning, and distillation that make code-intelligence models cheap enough to deploy at developer scale — without sacrificing quality.
I work at the intersection of AI, NLP, and Software Engineering — building, evaluating, and rethinking AI-driven approaches to software practice, with an emphasis on explainability, efficiency, and trustworthiness. PhD from USI Lugano (2024); now advising six PhD students at William & Mary.
AI for code · Understanding what models do · Reliable evaluation of how they behave · Automation that practitioners can trust. Each letter is a multi-paper research line.
Quantization, parameter-efficient fine-tuning, and distillation that make code-intelligence models cheap enough to deploy at developer scale — without sacrificing quality.
Combining neural language models with grammars, type systems, and program analysis — plus feature-level explanations that move beyond opaque token-by-token predictions.
Causal reasoning, counterfactual analysis, and rigorous LLM-as-a-Judge methodologies that measure what matters in code AI — not what's easy to measure.
Designing autonomous agents that plan, reason, and execute multi-step software workflows — from issue triage to code review — with measurable end-to-end behavior.
Paper acceptances, grants, talks, and honors — current and recent.