I research reasoning, interpretability, and safety in AI systems.
I recently completed my MS at Cornell advised by Claire Cardie, where I developed search-augmented reinforcement learning frameworks for legal AI. I defended my thesis on September 5th, 2025. Previously, I worked on AI safety evaluation and red-teaming at Invisible Technologies.
I'm interested in problems at the intersection of causal reasoning, model interpretability, ontology construction for LLMs, and building systems that make AI safety tools accessible beyond well-resourced institutions.
Selected Projects
LegalReasoner: A Framework for Legal Document Analysis and Reasoning
Developing AI systems that model explicit relationships between legal concepts, cases, and statutory provisions.
GraphRAG for Education: Addressing Knowledge Representation Gaps
Inspired by Harvard's work with CS50.ai, this AI teaching assistant maps conceptual relationships within educational content.
Automated Unit Test Generation: Making Reliable Code Less Painful
Built a systematic pipeline that generates Python unit tests from docstrings, then uses mutation testing to verify the tests actually catch bugs.
Research
Search-Augmented Reinforcement Learning for Legal Reasoning
MS Thesis, Cornell 2025 | Advisor: Claire Cardie | Defended September 5, 2025
- • First framework integrating legal database access during RL training episodes (not just inference)
- • Multi-task evaluation with jurisdictional compliance across all US legal systems
- • 10.5pp improvement on LegalBench through tool-assisted training with GRPO
Legal Reasoning Interpretability Framework
Cornell 2024-2025
- • Developed "glass box" framework combining fact extraction, causal mapping, and attention analysis
- • Enables transparent reasoning validation in high-stakes legal domains
- • Built evaluation protocols for professional acceptance of AI legal reasoning
AI Safety Evaluation & Red-Teaming
Invisible Technologies, 2022-2023
- • Developed red-teaming protocols and evaluation frameworks for LLM safety
- • Conducted adversarial testing: prompt injection, jailbreak attempts, bias detection
- • Trained safety evaluators on systematic vulnerability assessment
Research Interests
Problems I'm currently exploring:
📄 Read more about my approach to causal reasoning and neuro-symbolic AI in this article.
Teaching Experience
CS 2800: Discrete Structures
Graduate Teaching Assistant, Fall 2023 & Fall 2024
CS 1110: Introduction to Python
Graduate Teaching Assistant, Spring 2024
CS 1700: Elements of Artificial Intelligence
Graduate Teaching Assistant, Spring 2025
Current Focus
I'm forming a research group focused on democratizing access to AI safety tools evaluation frameworks, red-teaming protocols, and data infrastructure for resource-constrained domains. Current work spans legal reasoning, health data systems, and building open-source safety tooling.
I'm actively looking for research positions where I can contribute to interpretability, causal reasoning, and safety infrastructure development.
Let's Connect
If you’d like to discuss research ideas or explore AI safety and reasoning, I’d love to hear from you.
Email: daa238@cornell.edu