function researcher() {
    console.log("Decoding financial markets with AI and machine learning");
    return "Assistant Professor @ Saint Louis University";
}

Grounded in cutting-edge technology and financial theory, my research agenda spans three core areas: FinTech, Machine Learning, and Investment Management. I develop explainable AI models to uncover hidden patterns in mutual funds, hedge funds, and financial analyst behavior. My work bridges the gap between traditional finance and modern machine learning, with a particular focus on how artificial intelligence can enhance investment decision-making and risk management.

As an educator, I teach blockchain technology, cryptocurrency, and finance to both undergraduate and graduate students. I'm passionate about making complex financial concepts accessible through hands-on learning experiences. Beyond the classroom, I actively research the intersection of AI and finance, exploring how machine learning can decode the complexities of financial markets.

1. "Can Machines Understand Human Skills? Insights from Analyst Selection" under invited re-submission at Journal of Finance [SSRN]

2. "Decoding Mutual Fund Performance: Dynamic Return Patterns via Deep Learning", New version [SSRN]

Customized GPT for Teaching — A suite of Socratic educational agents for finance, blockchain, and business courses, structured around a DESIGN → IMPLEMENT → EVALUATE framework. Instructors use an automated prompt builder to deploy discipline-specific GPTs; student interactions are assessed via CognitoAI, a six-dimension evaluation platform with anti-cheating detection and instructor analytics. [Repository] | [Demo]

Blockchain Demo Hub — Curated interactive demonstrations covering hash functions, digital signatures, proof-of-work mining, and Bitcoin transactions for undergraduate and graduate instruction.

The Next Phase of AI Agents — Slides introducing multi-agent design for academic research: from single-agent limitations to orchestrated conductor-delegate systems, MCP integrations, context window constraints, and the HAHA (Human→AI→Human→AI) oversight method. [Slides]

Research Paper Factory — A 29-agent orchestration system for the complete academic research lifecycle — from data extraction through publication and revision — built as VS Code GitHub Copilot custom agents. Five department conductors (Strategy, Data Extraction, Data Analysis, Paper Writing, Revision) delegate to 14 specialist subagents and 11 shared services through a conductor-delegate pattern with mandatory approval gates, cross-session memory, SHA256 data versioning, and multi-model diversity (Opus for reasoning, Codex for code, Sonnet for review, Gemini for search). [Repository]

You talk to 6 conductors with natural language via @AgentName in Copilot Chat. They manage everything below — including a meta-agent (@Workflow-Manager) that lets you modify the system itself with plain English. Requires VS Code with GitHub Copilot.

Profile: CV | Research Gate | Google Scholar

Affiliations: Saint Louis University, Finance Department

© 2026 Norman (Xuxi) Guo. Bridging AI and Finance for better investment decisions.