Statement of Purpose

I am a Master’s student in Artificial Intelligence with a research-driven focus on machine learning systems, LLMs, and graph-based representations, particularly in the context of blockchain security and routing.

My primary interest lies in building theoretically grounded, system-aware AI models that operate under real-world constraints: scale, uncertainty, and adversarial settings.

Research Motivation

My work is motivated by a recurring observation: many modern AI systems perform well in isolation but degrade when embedded into complex, multi-agent or multi-source environments.

This has led me to focus on:

Graph-structured representations for reasoning over dependencies
LLM-assisted analysis in security-critical domains
System-level failure modes, not just model-level accuracy

I am particularly interested in problems where correctness, robustness, and interpretability matter more than marginal performance gains.

Current Work

I am currently involved in multiple research efforts, including:

Hierarchical graph representations for multi-chain blockchain routing, with an invited paper accepted at IEEE ICNC 2026
LLM-based vulnerability detection for smart contracts and DeFi systems, as a Research Assistant under Prof. Shucheng Yu
Ongoing projects at the intersection of graph learning, security, and optimization

Alongside research, I actively build production-grade systems, allowing me to validate theoretical ideas in realistic settings.

Academic Goals

My goal is to pursue a research-oriented career focused on:

Developing principled AI systems for complex, structured domains
Bridging theory with deployable machine learning infrastructure
Contributing to peer-reviewed research while maintaining strong engineering rigor

I aim to continue publishing in reputable conferences and journals, collaborate across disciplines, and work on problems with long-term technical relevance.

Perspective

I value:

Depth over breadth
Clarity over hype
Systems over demos

I am most interested in environments that reward careful thinking, rigorous experimentation, and intellectual honesty.

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