CS Ph.D., Purdue University, Indiana University
LLMs/MLLMs | AI Agentic Systems | Agent Orchestration & Harnesses | RAG | NLP & CV | GenAI | Industrial & MedAI | XAI & Robustness | mmWave & Embedded
Builder of compilers, LLM systems, distributed labs, embedded prototypes, and from-scratch engineering projects.
Homepage: billzi2016.github.io · GitHub Project · Docs
Google Scholar: Ziqian Bi
-
AI-Solar-Panel-Defect-Detection-Deployment · Docs
Solar-cell and photovoltaic-panel defect detection workspace with dataset preparation, YOLO detection experiments, anomaly detection, deployment helpers, and bilingual MkDocs documentation. -
Gomoku-AI · Demo · Docs
Browser-based Rust/Wasm Gomoku AI with bitboards, VCF search, alpha-beta pruning, iterative deepening, Web Worker parallelism, and local CPU execution. -
Chinese-Chess-AI · Demo Browser-based Xiangqi AI powered by the ElephantEye C++ engine compiled to WebAssembly, with local UCCI search analysis and no backend dependency.
-
LLM-Benchmark-Studio
Local-first LLM evaluation platform with Django, Vue, PostgreSQL, RabbitMQ, Celery, SSE, and multi-provider model execution. -
Build-DeepSeek-Step-by-Step
Notebook-first deep dive into modern LLM architecture, covering tokenization, attention, GQA, MLA, MoE, training, and alignment. -
blockchain-theory From-scratch implementations of blockchain primitives and consensus mechanisms, including PoW, PoS with slashing, Merkle trees, ECDSA signatures, and the UTXO model.
-
Apple-Silicon-LLM-Engine-from-Scratch
GPT-2 inference engine rebuilt across NumPy, PyTorch MPS, and C++/Metal stages for Apple Silicon. -
distributed-paxos-raft-lab
Distributed consensus lab with FastAPI node simulation, Vue dashboards, Docker orchestration, and cluster control workflows. -
GPU-Test-and-Polite-Scheduler
Practical shared-GPU toolkit for environment checks, stress tests, communication diagnostics, and tmux-based polite scheduling. -
Leetcode-All-Languages-Best-Solutions · Docs
Multi-language LeetCode solution workspace with generated Markdown outputs, difficulty/range organization, and a MkDocs documentation site.
An AI agent that controls reasoning level to save tokens while still producing accurate answers, using the large language model (LLM) gpt-oss 120b through an Ollama host.
Languages:
C, C++, Java, Python, Python3, C#, JavaScript
TypeScript, PHP, Swift, Kotlin, Dart, Go, Ruby
Scala, Rust, Racket, Erlang, Elixir. -
homemade-risc-v-64-vector-linux-emulator · Docs
From-scratch educational 64-bit RISC-V emulator with Vector extensions and Linux boot support.
-
Industrial-Query-Agent
Industrial query agent project for structured domain question answering, retrieval-oriented workflows, and practical agentic system design. -
Chinese-Chess-AI-Pro · Demo Advanced browser-based Xiangqi AI with C++ WebAssembly, NNUE evaluation, and multi-threaded Web Workers via SharedArrayBuffer.
-
Othello-AI · Demo · Docs
Browser-based Rust/Wasm Othello AI with bitboards, alpha-beta search, exact endgame search, stability-aware evaluation, and Web Worker parallelism. -
automl-autodl · Docs
Unified tabular AutoML and AutoDL workspace for Home Credit risk modeling, with shared preprocessing, classical ML, PyTorch models, and documentation pages. -
C-Core-Compiler · Docs
C-like compiler project covering lexical analysis, parsing, semantic checks, IR, code generation, and documentation. -
python-git-reproduction · Docs
Reproducible Python and Git workflow documentation project with MkDocs pages and repository-level maintenance notes. -
DeepChrInteract-v2 · Docs
Deep learning project for chromatin interaction modeling with a Sphinx documentation site. -
MIMO-FMCW-Radar-Simulator-Multiprocess · Docs
Multiprocess MIMO FMCW radar simulation project with public documentation pages. -
mmwave-fmcw-cascade-mimo-sensing-platform · Docs
mmWave FMCW cascade MIMO sensing platform with documentation for radar signal processing and system workflows. -
mmlock-fmcw-radar-deep-security · Docs
FMCW radar deep-security project for mmWave sensing, authentication, and documentation-driven maintenance. -
whisper-meeting-transcription-translation-and-summary
Offline subtitle generation and bilingual translation tool for local audio and video files, accelerated with MLX on Apple Silicon and powered by local LLM workflows. -
MPSBoost · Docs · PyPI Fast gradient-boosted decision trees for Apple Silicon, with a lightweight sklearn-style Python API powered by MPS and Metal.
- homemade-risc-v-64-vector-linux-emulator · Docs
- homemade-cpu-raytracer
- C-Core-Compiler · Docs
- python-git-reproduction · Docs
- Self-Hosting-C-Core-Compiler
- Homemade-CPython
- MIMO-FMCW-Radar-Simulator-Multiprocess · Docs
- mmwave-fmcw-cascade-mimo-sensing-platform · Docs
- mmlock-fmcw-radar-deep-security · Docs
- billzi2016.github.io · Website · Docs
Google Scholar profile snapshot
This maintenance work turns experimental, research-oriented, and long-running repositories into clearer public-facing projects. It includes reorganizing repository structures, rewriting and standardizing README files, establishing consistent project entry points, and adding practical explanations for installation, execution, architecture, datasets, experiments, and expected outputs. English and Chinese documentation are kept aligned where bilingual presentation is useful.
The documentation layer has also been strengthened across repositories through clearer navigation, cross-page links, mobile-friendly layouts, stable asset paths, and GitHub Pages deployment. Depending on the needs of each project, the public-facing documentation now converges around three delivery styles: MkDocs documentation sites, Sphinx documentation sites, and fully custom static sites built with HTML, CSS, and JavaScript.
The maintained portfolio spans compilers and runtime systems, LLM and agent engineering, computer vision and medical imaging, radar simulation and mmWave sensing, reproducible research workflows, and browser-based game AI such as Gomoku and Othello. The goal is to make each repository easier to inspect, understand, reproduce, and maintain over time, even for visitors who have not yet run the project locally. The examples below show several representative documentation and presentation patterns used in this ongoing cleanup.
MkDocs documentation site and repository README / documentation system: python-git-reproduction
- Site: https://billzi2016.github.io/python-git-reproduction/
- Repository: https://github.com/billzi2016/python-git-reproduction
MkDocs documentation site for python-git-reproduction Repository README and documentation system for python-git-reproduction
Sphinx documentation site: DeepChrInteract-v2
- Site: https://billzi2016.github.io/DeepChrInteract-v2/
- Repository: https://github.com/billzi2016/DeepChrInteract-v2
Sphinx documentation site for DeepChrInteract-v2
Static HTML/CSS/JS site: billzi2016.github.io
Static personal site billzi2016.github.io
Curated project README example: Awesome Flow Matching
Curated project README example from Awesome Flow Matching
Recently, I have been refactoring and polishing my GitHub Pages presence, using Codex / Claude Code within a Human-in-the-Loop workflow to turn older repositories into more formal, better-documented, and more navigable project sites. The screenshots below summarize the past month's usage and maintenance activity.
The work is not about blindly generating pages. It follows a Spec-First and Review-Driven process, with Test-Driven Development (TDD), Spec-Driven Development (SDD), and Continuous Integration / Continuous Delivery (CI/CD) practices used to improve readability, cross-platform support, maintainability, long-term maintenance efficiency, security, and stability across legacy repositories. AI assistance greatly accelerates the overall pace of restructuring and cleanup, and when dealing with large, interdependent documentation sets, it has a natural advantage in structural organization, terminology alignment, and cross-document revision, which helps reduce the risk of updating one section while leaving related documentation behind. At the same time, project decisions, acceptance, consolidation, and final editorial control remain Human-in-the-Loop.
Cross-model token consumption Codex usage dashboard showing 18,935,155,313 tokens consumed across models
Conversation turns by model Codex usage dashboard showing 61,090 conversation turns by model
Lines of code generated Codex usage dashboard showing 3,429,823 lines of code generated over time