Daniel Lee

Stanford CS & AI. Building the future of intelligent systems.

About Me

Computer Science and AI student at Stanford. I work on research systems and build tools that advance how we understand and interact with technology.

You can reach me at: leedan [at] stanford [dot] edu

Experience across world-class labs, startups, and research groups.

Experience

  1. Undergraduate Student Researcher
    Stanford Artificial Intelligence Laboratory (SAIL) Stanford, CA
    Jun 2025 – Present
    Exploring AI agents for research applications (Zou Group).
  2. Software Engineer Intern
    Modern Nose Clinic Salem, OR (Hybrid)
    May 2025 – Present
    Building tools to streamline prior authorization and patient price estimation for a specialty ENT clinic.
  3. Machine Learning Researcher
    Stanford University Department of Computer Science Stanford, CA
    Jan 2025 – May 2025
    Developed entropy-based system for detecting LLM hallucinations with high accuracy, leveraging information-theoretic features.
  4. Bioinformatics Intern
    Icahn School of Medicine at Mount Sinai New York, NY
    Oct 2022 – Jan 2025
    Built scalable Hi-C analysis pipeline for characterizing chromatin architecture in addiction-related pathways.
  5. Research Assistant
    Massachusetts General Hospital Remote
    Mar 2021 – Mar 2022
    Built Java-based simulation modeling embryonic growth and phylodynamics across species.
Research contributions in AI systems and computational biology.

Papers

ShED-HD: A Shannon Entropy Distribution Framework for Lightweight Hallucination Detection on Edge Devices

A new way to detect LLM hallucinations efficiently, even on edge devices.

AI SystemsEdge Computing

How Our Cells Become Our Selves: The Cellular Phylodynamic Examination of Growth and Development

How a single cell becomes a complex organism, using math to reveal the hidden rules of growth.

Computational BiologyMathematical Modeling
Selected projects at the intersection of AI, systems, and impactful technology.

Projects

Scopia Research

Full-stack academic research platform processing 2.8M+ papers with 94% search relevance accuracy. Engineered dual-mode search combining vector similarity and Retrieval-Augmented Generation (RAG), reducing query latency by 65%. Implemented asynchronous Python backend with Redis caching, cutting infrastructure costs by 40%.

Full-Stack DevelopmentRetrieval-Augmented Generation (RAG)PythonRedisReact

Cellular Phylodynamic Growth Simulation

Java simulation analyzing embryonic growth and phylodynamic patterns across 13 species, achieving 89.9% accuracy in cellular growth modeling. Implemented Universal Growth Equation achieving R² > 0.99 fit for developmental trajectories in 6 species. Published third author in BioRxiv on mathematical framework analyzing 2 trillion cells across development.

JavaSimulationMathematical ModelingBioinformatics

ShED-HD: Shannon Entropy Distribution for Hallucination Detection

Entropy-based system for detecting LLM hallucinations with high accuracy, leveraging information-theoretic features and scalable training methods. Built for edge devices and published in collaboration with Stanford CS.

PyTorchDeep LearningEdge AIInformation Theory