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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="description" content="Krisoye Smith - Quantitative Researcher & Data Scientist specializing in ML/AI for quantitative finance and predictive modeling">
<title>Krisoye Smith - Quantitative Researcher & Data Scientist</title>
<link rel="stylesheet" href="style.css">
</head>
<body>
<!-- Navigation -->
<nav class="navbar">
<div class="container">
<div class="nav-brand">Krisoye Smith</div>
<ul class="nav-menu">
<li><a href="#home" class="nav-link">Home</a></li>
<li><a href="#about" class="nav-link">About</a></li>
<li><a href="#projects" class="nav-link">Projects</a></li>
<li><a href="#contact" class="nav-link">Contact</a></li>
</ul>
<div class="hamburger">
<span></span>
<span></span>
<span></span>
</div>
</div>
</nav>
<!-- Hero Section -->
<section id="home" class="hero">
<div class="container">
<div class="hero-content">
<div class="hero-text">
<h1 class="hero-title"><span class="highlight">Krisoye Smith</span></h1>
<p class="hero-subtitle">Quantitative Researcher | Data Scientist | AI/ML Engineer</p>
<p class="hero-description">
15+ years applying machine learning and AI techniques to quantitative finance,
building predictive models across global equity markets, and developing
high-performance tools for data science workflows.
</p>
<div class="hero-buttons">
<a href="#projects" class="btn btn-primary">View My Work</a>
<a href="#contact" class="btn btn-secondary">Get In Touch</a>
</div>
</div>
<div class="hero-image">
<img src="Krisoye-headshot.png" alt="Krisoye Smith - Quantitative Researcher">
</div>
</div>
</div>
</section>
<!-- About Section -->
<section id="about" class="about">
<div class="container">
<h2 class="section-title">About Me</h2>
<div class="about-content">
<div class="about-text">
<p>
I'm a Quantitative Researcher with expertise in designing, prototyping,
and deploying predictive models across global equity universes. My work spans
quantitative finance, fraud detection, alpha/risk modeling, and building scalable ML/AI systems.
</p>
<p>
Most recently, as Head of Quantitative Research at Victory Capital (Sophus Capital), I maintained the Global EM alpha factor model.
I've also led data science teams at Macquarie Asset Management and CO-OP Financial Services,
driving innovation in predictive modeling and ML deployment.
</p>
<p>
I hold graduate degrees from NYU (MS Financial Engineering, MS Computer Science),
University of Chicago Booth (MBA), and Iowa State University (MS Statistics).
</p>
<p>
Builder of production AI systems including RAG pipelines, vector search engines, and
audio/document analysis servers deployed on GPU-accelerated infrastructure.
</p>
</div>
<div class="skills">
<h3>Technologies & Skills</h3>
<div class="skills-category">
<h4>Programming Languages</h4>
<div class="skills-grid">
<span class="skill-tag">Python</span>
<span class="skill-tag">R</span>
<span class="skill-tag">SQL</span>
</div>
</div>
<div class="skills-category">
<h4>Machine Learning & AI</h4>
<div class="skills-grid">
<span class="skill-tag">PyTorch</span>
<span class="skill-tag">scikit-learn</span>
<span class="skill-tag">XGBoost</span>
<span class="skill-tag">Random Forest</span>
<span class="skill-tag">Elastic Net</span>
<span class="skill-tag">GAM</span>
</div>
</div>
<div class="skills-category">
<h4>GenAI & RAG</h4>
<div class="skills-grid">
<span class="skill-tag">LLMs</span>
<span class="skill-tag">RAG Pipelines</span>
<span class="skill-tag">Vector Databases</span>
<span class="skill-tag">Embeddings</span>
<span class="skill-tag">Claude API</span>
<span class="skill-tag">OpenAI Whisper</span>
<span class="skill-tag">MCP</span>
<span class="skill-tag">Prompt Engineering</span>
</div>
</div>
<div class="skills-category">
<h4>Quantitative Finance</h4>
<div class="skills-grid">
<span class="skill-tag">Factor Modeling</span>
<span class="skill-tag">Risk Management</span>
<span class="skill-tag">Portfolio Optimization</span>
<span class="skill-tag">Time Series</span>
<span class="skill-tag">FactSet</span>
<span class="skill-tag">Axioma</span>
</div>
</div>
<div class="skills-category">
<h4>Cloud & Infrastructure</h4>
<div class="skills-grid">
<span class="skill-tag">AWS</span>
<span class="skill-tag">Azure</span>
<span class="skill-tag">Databricks</span>
<span class="skill-tag">Linux</span>
</div>
</div>
<div class="skills-category">
<h4>Data Engineering</h4>
<div class="skills-grid">
<span class="skill-tag">pandas</span>
<span class="skill-tag">Arrow</span>
<span class="skill-tag">PySpark</span>
<span class="skill-tag">dplyr</span>
<span class="skill-tag">Airflow</span>
<span class="skill-tag">MLflow</span>
</div>
</div>
<div class="skills-category">
<h4>Tools & Platforms</h4>
<div class="skills-grid">
<span class="skill-tag">Git</span>
<span class="skill-tag">Docker</span>
<span class="skill-tag">LaTeX</span>
<span class="skill-tag">Jupyter</span>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Projects Section -->
<section id="projects" class="projects">
<div class="container">
<h2 class="section-title">Featured Projects</h2>
<div class="projects-grid">
<!-- Project Card: Knowledge Bank - FEATURED -->
<div class="project-card featured">
<div class="project-header">
<h3>Knowledge Bank <span style="font-weight: normal; font-size: 0.85em; opacity: 0.8;">— Semantic Search Engine</span></h3>
<div class="project-links">
<a href="https://github.com/krisoye/knowledge-bank-tools" target="_blank" class="project-link" aria-label="GitHub">
<svg width="20" height="20" viewBox="0 0 24 24" fill="currentColor">
<path d="M12 0c-6.626 0-12 5.373-12 12 0 5.302 3.438 9.8 8.207 11.387.599.111.793-.261.793-.577v-2.234c-3.338.726-4.033-1.416-4.033-1.416-.546-1.387-1.333-1.756-1.333-1.756-1.089-.745.083-.729.083-.729 1.205.084 1.839 1.237 1.839 1.237 1.07 1.834 2.807 1.304 3.492.997.107-.775.418-1.305.762-1.604-2.665-.305-5.467-1.334-5.467-5.931 0-1.311.469-2.381 1.236-3.221-.124-.303-.535-1.524.117-3.176 0 0 1.008-.322 3.301 1.23.957-.266 1.983-.399 3.003-.404 1.02.005 2.047.138 3.006.404 2.291-1.552 3.297-1.23 3.297-1.23.653 1.653.242 2.874.118 3.176.77.84 1.235 1.911 1.235 3.221 0 4.609-2.807 5.624-5.479 5.921.43.372.823 1.102.823 2.222v3.293c0 .319.192.694.801.576 4.765-1.589 8.199-6.086 8.199-11.386 0-6.627-5.373-12-12-12z"/>
</svg>
</a>
</div>
</div>
<p class="project-description">
ChromaDB-powered vector database for semantic search across 17+ source types. Full RAG pipeline
with sentence-transformer embeddings, multi-signal credibility scoring, staleness-aware retrieval,
and automated batch ingestion with intelligent routing across 12 extractors. Exposed as a REST API
(FastAPI) and MCP server.
</p>
<div class="project-tags">
<span class="tag">ChromaDB</span>
<span class="tag">sentence-transformers</span>
<span class="tag">RAG</span>
<span class="tag">FastAPI</span>
<span class="tag">FastMCP</span>
<span class="tag">Python</span>
</div>
</div>
<!-- Project Card: Document Analysis MCP -->
<div class="project-card">
<div class="project-header">
<h3>Document Analysis MCP</h3>
<div class="project-links">
<a href="https://github.com/krisoye/document-analysis-mcp" target="_blank" class="project-link" aria-label="GitHub">
<svg width="20" height="20" viewBox="0 0 24 24" fill="currentColor">
<path d="M12 0c-6.626 0-12 5.373-12 12 0 5.302 3.438 9.8 8.207 11.387.599.111.793-.261.793-.577v-2.234c-3.338.726-4.033-1.416-4.033-1.416-.546-1.387-1.333-1.756-1.333-1.756-1.089-.745.083-.729.083-.729 1.205.084 1.839 1.237 1.839 1.237 1.07 1.834 2.807 1.304 3.492.997.107-.775.418-1.305.762-1.604-2.665-.305-5.467-1.334-5.467-5.931 0-1.311.469-2.381 1.236-3.221-.124-.303-.535-1.524.117-3.176 0 0 1.008-.322 3.301 1.23.957-.266 1.983-.399 3.003-.404 1.02.005 2.047.138 3.006.404 2.291-1.552 3.297-1.23 3.297-1.23.653 1.653.242 2.874.118 3.176.77.84 1.235 1.911 1.235 3.221 0 4.609-2.807 5.624-5.479 5.921.43.372.823 1.102.823 2.222v3.293c0 .319.192.694.801.576 4.765-1.589 8.199-6.086 8.199-11.386 0-6.627-5.373-12-12-12z"/>
</svg>
</a>
</div>
</div>
<p class="project-description">
MCP server for PDF processing powered by Claude API. Multi-page iterative extraction,
document classification across 17+ content types, OCR support via Tesseract, and
hash-based caching to prevent re-processing.
</p>
<div class="project-tags">
<span class="tag">Claude API</span>
<span class="tag">FastMCP</span>
<span class="tag">pdfplumber</span>
<span class="tag">Tesseract</span>
<span class="tag">Python</span>
</div>
</div>
<!-- Project Card: Audio Analysis MCP -->
<div class="project-card">
<div class="project-header">
<h3>Audio Analysis MCP</h3>
<div class="project-links">
<a href="https://github.com/krisoye/audio-analysis-mcp" target="_blank" class="project-link" aria-label="GitHub">
<svg width="20" height="20" viewBox="0 0 24 24" fill="currentColor">
<path d="M12 0c-6.626 0-12 5.373-12 12 0 5.302 3.438 9.8 8.207 11.387.599.111.793-.261.793-.577v-2.234c-3.338.726-4.033-1.416-4.033-1.416-.546-1.387-1.333-1.756-1.333-1.756-1.089-.745.083-.729.083-.729 1.205.084 1.839 1.237 1.839 1.237 1.07 1.834 2.807 1.304 3.492.997.107-.775.418-1.305.762-1.604-2.665-.305-5.467-1.334-5.467-5.931 0-1.311.469-2.381 1.236-3.221-.124-.303-.535-1.524.117-3.176 0 0 1.008-.322 3.301 1.23.957-.266 1.983-.399 3.003-.404 1.02.005 2.047.138 3.006.404 2.291-1.552 3.297-1.23 3.297-1.23.653 1.653.242 2.874.118 3.176.77.84 1.235 1.911 1.235 3.221 0 4.609-2.807 5.624-5.479 5.921.43.372.823 1.102.823 2.222v3.293c0 .319.192.694.801.576 4.765-1.589 8.199-6.086 8.199-11.386 0-6.627-5.373-12-12-12z"/>
</svg>
</a>
</div>
</div>
<p class="project-description">
Production MCP server for audio analysis. Whisper transcription with word-level timestamps,
pyannote.audio speaker diarization, prosody analysis, and sentiment detection.
GPU-accelerated with low-VRAM mode for constrained hardware.
</p>
<div class="project-tags">
<span class="tag">Whisper</span>
<span class="tag">pyannote.audio</span>
<span class="tag">FastMCP</span>
<span class="tag">PyTorch</span>
<span class="tag">CUDA</span>
</div>
</div>
<!-- Project Card: Fraud Detection - FEATURED -->
<div class="project-card featured">
<div class="project-header">
<h3>Credit Card Fraud Detection</h3>
<div class="project-links">
</div>
</div>
<p class="project-description">
<strong>CO-OP Financial Services:</strong> Real-time fraud prediction for credit card transactions
within 50ms for 100 Credit Union clients. Model detected 4-5% more fraud vs industry standard using
XGBoost and Random Forest on Azure Databricks with engineered behavioral features.
</p>
<div class="project-tags">
<span class="tag">Python</span>
<span class="tag">XGBoost</span>
<span class="tag">Azure Databricks</span>
<span class="tag">MLflow</span>
</div>
</div>
<!-- Project Card 1: etlr -->
<div class="project-card">
<div class="project-header">
<h3>etlr</h3>
<div class="project-links">
<a href="https://github.com/krisoye/etlr" target="_blank" class="project-link" aria-label="GitHub">
<svg width="20" height="20" viewBox="0 0 24 24" fill="currentColor">
<path d="M12 0c-6.626 0-12 5.373-12 12 0 5.302 3.438 9.8 8.207 11.387.599.111.793-.261.793-.577v-2.234c-3.338.726-4.033-1.416-4.033-1.416-.546-1.387-1.333-1.756-1.333-1.756-1.089-.745.083-.729.083-.729 1.205.084 1.839 1.237 1.839 1.237 1.07 1.834 2.807 1.304 3.492.997.107-.775.418-1.305.762-1.604-2.665-.305-5.467-1.334-5.467-5.931 0-1.311.469-2.381 1.236-3.221-.124-.303-.535-1.524.117-3.176 0 0 1.008-.322 3.301 1.23.957-.266 1.983-.399 3.003-.404 1.02.005 2.047.138 3.006.404 2.291-1.552 3.297-1.23 3.297-1.23.653 1.653.242 2.874.118 3.176.77.84 1.235 1.911 1.235 3.221 0 4.609-2.807 5.624-5.479 5.921.43.372.823 1.102.823 2.222v3.293c0 .319.192.694.801.576 4.765-1.589 8.199-6.086 8.199-11.386 0-6.627-5.373-12-12-12z"/>
</svg>
</a>
</div>
</div>
<p class="project-description">
A comprehensive R package for streamlined ETL operations. Provides tools for data transformation,
file management, cloud storage (S3) integration, and feature engineering. Built on dplyr, lubridate,
and arrow for efficient data processing pipelines.
</p>
<div class="project-tags">
<span class="tag">R</span>
<span class="tag">AWS S3</span>
<span class="tag">Arrow</span>
<span class="tag">ETL</span>
</div>
</div>
<!-- Project Card 2: mdlr -->
<div class="project-card">
<div class="project-header">
<h3>mdlr</h3>
<div class="project-links">
<a href="https://github.com/krisoye/mdlr" target="_blank" class="project-link" aria-label="GitHub">
<svg width="20" height="20" viewBox="0 0 24 24" fill="currentColor">
<path d="M12 0c-6.626 0-12 5.373-12 12 0 5.302 3.438 9.8 8.207 11.387.599.111.793-.261.793-.577v-2.234c-3.338.726-4.033-1.416-4.033-1.416-.546-1.387-1.333-1.756-1.333-1.756-1.089-.745.083-.729.083-.729 1.205.084 1.839 1.237 1.839 1.237 1.07 1.834 2.807 1.304 3.492.997.107-.775.418-1.305.762-1.604-2.665-.305-5.467-1.334-5.467-5.931 0-1.311.469-2.381 1.236-3.221-.124-.303-.535-1.524.117-3.176 0 0 1.008-.322 3.301 1.23.957-.266 1.983-.399 3.003-.404 1.02.005 2.047.138 3.006.404 2.291-1.552 3.297-1.23 3.297-1.23.653 1.653.242 2.874.118 3.176.77.84 1.235 1.911 1.235 3.221 0 4.609-2.807 5.624-5.479 5.921.43.372.823 1.102.823 2.222v3.293c0 .319.192.694.801.576 4.765-1.589 8.199-6.086 8.199-11.386 0-6.627-5.373-12-12-12z"/>
</svg>
</a>
</div>
</div>
<p class="project-description">
An R toolkit for streamlined model development, training, and forecasting at scale. Unified interface
for base R and parsnip models (glmnet, mgcv, rstanarm), with support for rolling/expanding windows,
walk-forward validation, and tidy exports to partitioned datasets.
</p>
<div class="project-tags">
<span class="tag">R</span>
<span class="tag">Machine Learning</span>
<span class="tag">Parsnip</span>
<span class="tag">Backtesting</span>
</div>
</div>
<!-- Project Card 5: Volatility Trading -->
<div class="project-card">
<div class="project-header">
<h3>S&P 500 Systematic Volatility Signal</h3>
<div class="project-links">
</div>
</div>
<p class="project-description">
<strong>Principal Global Investors:</strong> Predictive volatility trading signal for S&P 500 used
in variable annuity hedging strategy. Delivered 21% better risk-adjusted returns (Sharpe Ratio)
from 2012-2016 vs peers by dynamically allocating between equity and cash targeting 15% max annual vol.
</p>
<div class="project-tags">
<span class="tag">Time Series</span>
<span class="tag">Risk Management</span>
<span class="tag">GARCH</span>
<span class="tag">Portfolio Optimization</span>
</div>
</div>
</div>
</div>
</section>
<!-- Contact Section -->
<section id="contact" class="contact">
<div class="container">
<h2 class="section-title">Get In Touch</h2>
<div class="contact-content">
<p class="contact-text">
I'm always interested in discussing quantitative research, data science opportunities,
and collaborating on innovative projects. Feel free to reach out!
</p>
<div class="contact-links">
<a href="mailto:krisoye@gmail.com" class="contact-link">
<svg width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
<path d="M4 4h16c1.1 0 2 .9 2 2v12c0 1.1-.9 2-2 2H4c-1.1 0-2-.9-2-2V6c0-1.1.9-2 2-2z"></path>
<polyline points="22,6 12,13 2,6"></polyline>
</svg>
krisoye@gmail.com
</a>
<a href="https://github.com/krisoye" target="_blank" class="contact-link">
<svg width="24" height="24" viewBox="0 0 24 24" fill="currentColor">
<path d="M12 0c-6.626 0-12 5.373-12 12 0 5.302 3.438 9.8 8.207 11.387.599.111.793-.261.793-.577v-2.234c-3.338.726-4.033-1.416-4.033-1.416-.546-1.387-1.333-1.756-1.333-1.756-1.089-.745.083-.729.083-.729 1.205.084 1.839 1.237 1.839 1.237 1.07 1.834 2.807 1.304 3.492.997.107-.775.418-1.305.762-1.604-2.665-.305-5.467-1.334-5.467-5.931 0-1.311.469-2.381 1.236-3.221-.124-.303-.535-1.524.117-3.176 0 0 1.008-.322 3.301 1.23.957-.266 1.983-.399 3.003-.404 1.02.005 2.047.138 3.006.404 2.291-1.552 3.297-1.23 3.297-1.23.653 1.653.242 2.874.118 3.176.77.84 1.235 1.911 1.235 3.221 0 4.609-2.807 5.624-5.479 5.921.43.372.823 1.102.823 2.222v3.293c0 .319.192.694.801.576 4.765-1.589 8.199-6.086 8.199-11.386 0-6.627-5.373-12-12-12z"/>
</svg>
GitHub
</a>
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