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David Li

EECS at UC Berkeley

I love data, and I build tools to handle it.

About

I’m an EECS student at UC Berkeley. Most of what I build starts as a question I want answered: what a defense forum has said about a jet over the years, how China’s industrial output compares with everyone else’s, how Shenzhen’s skyline compares to other cities.

The common thread is keeping the source attached. If a tool gives me an answer, I want to be able to click through to the post, report, or record it came from.

I’ve also done computer vision research at Boston University and UC Irvine.

Projects

  • SDF Observatory

    A searchable archive of 178,000 defense-forum posts, with AI-written subject pages and a research assistant that both cite their sources.

  • BurstSR

    An Android camera app that merges a handheld burst of RAW frames into one sharper, cleaner photo on the phone's GPU.

  • DJI Fly Mod Reconstruction

    Rebuilding an obfuscated DJI Fly mod as readable source that compiles back to the original.

  • SmsBot

    A self-hosted dashboard, encrypted archive, and command bot for my own Google Messages account.

  • HighriseDB

    A community-maintained map of 7,500+ skyscrapers in 283 cities, with a forum for every city and building and AI agents that draft sourced edits.

  • China Industry Visualizer

    An atlas of 4,380 source-linked metrics comparing China's industrial scale with other countries, most with year-by-year history.

Experience

  • SUMMER 2025

    Research Assistant

    SUMMER 2025

    Gong Lab (BU RISE)at Boston University

    Built a full-stack research tool for dataset exploration, transcript-video synchronization, annotation, and frame-level search, reducing a teammate’s clip-search workflow from three days to about 20 minutes. Co-authored BabyVLM-V2, accepted to CVPR 2026.

  • 2024 — 2025

    Research Assistant

    2024 — 2025

    Hayes Labat UC Irvine

    Developed parsers and evaluation/visualization tooling for PoseBench3D, a cross-dataset benchmark spanning Human3.6M, GPA, 3DPW, and SURREAL. Standardized MPJPE/PA-MPJPE reports for 18 pose-lifting methods, generating 100+ cross-dataset comparisons; co-authored a CVPR 2026 submission.