Here are some of the key projects I have led or made significant contributions to. My work focuses on applying advanced AI and Machine Learning techniques to solve complex industrial and research problems.
AI Safety & Red-Teaming for Tool-Using LLM Agents
I lead Armada’s AI safety research program. Our latest work, The Poisoned Toolbox (under review at NeurIPS 2026), demonstrates a supply-chain attack on open-weight LLMs: a multi-stage PEFT pipeline (SFT-then-GRPO) that implants temporally triggered malicious tool calls while reinforcement learning preserves benign behavior everywhere else — evading standard benchmarks entirely.
Attack achieves 99.6% success with a 0.6% accidental-trigger rate on as few as 6,000 training examples.
Designed a four-layer defense framework: a cascaded runtime monitor achieving 100% detection at a 1.79% false-positive rate, plus weight-distribution analysis and high-temperature stochastic probing.
Shows that conditional backdoors evade benchmark-based evaluation but leave detectable fingerprints — motivating layered defenses for open-weight agent ecosystems.
Agentic AI Routing System for Industrial Processes
As the lead on this project at Armada, I architected a scalable agentic AI routing system designed for complex industrial applications. The system leverages advanced ML and optimization to translate complex data into actionable, natural-language insights for operators.
The distilled models outperform large-scale proprietary systems by over 10%.
The system is optimized for edge deployment, ensuring high performance in real-world industrial environments.
Improved overall efficiency of complex industrial processes by a projected 4.5%.
Deep Neural Network for Flight-Level Traffic Forecasting
At American Airlines, I led the development of a deep neural network forecasting engine to predict flight-level traffic for the Yield Management team. This involved using a multimodal approach to capture complex trends and seasonality.
Pioneered OPUS, an LLM framework to control robotic camera systems with natural language, achieving 20% higher task accuracy than large proprietary models.
Developed a novel multimodal architecture that reduces mean squared error by 10-15% across diverse tasks.
Introduced a Textual Question Answering architecture that employs on-demand visual grounding, achieving performance comparable to GPT-4o with a 400M parameter model.
Ontology Alignment and Clinical Data Processing
While at Truveta, I contributed to the mission of “Saving Lives with Data” by developing data-driven NLP approaches for clinical text.
Key Technologies: Large Language Models (LLMs), NLP, Ontology Alignment, Python.
Impact & Achievements:
Pre-trained and fine-tuned LLMs on clinical data, significantly increasing downstream training and inference speeds (fourfold and twofold, respectively).
Achieved state-of-the-art results in ontology alignment, with a 5% improvement in F1, Hit@1, and MRR.
Developed Truveta Mapper, a novel framework for unsupervised ontology alignment.