I'm an AI/ML enthusiast currently pursuing my MS in Computer Science at Georgia Tech, deeply fascinated by the capabilities and complexities of Large Language Models (LLMs) and Natural Language Processing (NLP).

Driven by a curiosity that spans from the core mechanics of deep learning to the societal impacts of AI, my focus lies at the intersection of LLM Security, Agentic Systems, and building practical, efficient ML applications. I enjoy bridging the gap between rigorous research and hands-on implementation.

What I'm Exploring & Working On:

• 🧠 LLM Security & Robustness: Investigating vulnerabilities like backdooring in CodeAct agents (check out my Silent Sabotage project!) and exploring defenses like watermarking in Federated Learning settings. The ease with which current systems can be compromised is a critical area needing more attention!
• 🤖 Agentic AI: Exploring the potential and pitfalls of autonomous LLM agents – how they work, how they learn, and how to make them safe and reliable.
• 🛠️ Efficient & Applied NLP: Building useful tools, like the lightweight patent retrieval system I developed at Kili Technology (DeepIP project) or fine-tuning models like ModernBERT for specialized domains (ModernPatentBERT.
• ⚖️ Responsible AI: Thinking critically about fairness and bias, developing frameworks to evaluate LLM recommendations in sensitive contexts (LLM Bias Benchmark).
• 💡 Staying Curious: Constantly learning from research papers, communities like r/LocalLLaMA, and drawing broader context from sources like The Economist. I believe understanding diverse fields (like economics, demographics, or even cybersecurity history like Stuxnet) informs better AI development.

Check out my full portfolio for project deep dives: gauthierroy.github.io

Featured Projects:

• Silent Sabotage: Backdooring Code-Executing LLM Agents: Investigated novel attack vectors specific to CodeAct agents, achieving >99% attack success rate even with minimal poisoned data. Highlights the critical security risks in emerging agentic systems.
• ModernBERT for Patents: Faster Insights, Smarter Classification: Fine-tuned ModernBERT for patent classification, incorporating a novel hierarchical loss function and achieving SOTA performance while demonstrating >2x inference speedup. Includes the public release of the USPTO-3M dataset.
• Ethical AI Recommendations: Benchmarking LLM Bias: Developed a framework to evaluate bias (gender, nationality) in LLM recommender systems, particularly in challenging cold-start scenarios, using counterfactual analysis.

My Approach:

I tend to work experimentally and incrementally. I like starting with the simplest viable solution to understand the core problem, then building complexity and layering advanced techniques thoughtfully. My "PyTorch Journey" project reflects this – implementing models from scratch (NNs, CNNs to Transformers, GNNs, Diffusion) to solidify foundational understanding.

Beyond Code:

My curiosity extends beyond ML! Recently, I've been digging into the technical details and strategic failures of the Stuxnet virus and exploring how seemingly unrelated factors like building regulations drive architectural trends (like the rise of 5-over-1 buildings in the US). Finding connections across domains is always fascinating!

Let's Connect! 🤝

• I'm actively seeking Machine Learning Engineer roles or CIFRE PhD opportunities where I can tackle challenging problems in LLMs, security, or agentic AI.
• I'm always open to collaboration on projects related to my interests or participating in hackathons.
• Feel free to reach out via LinkedIn