Autonomous AI research team: write a plan.md, spawn agents, train, verify, and deliver a clean ML repo.
Real-time TUI for monitoring cloud GPU training instances
C++/Python microstructure research engine for event-driven limit order book prediction and reproducible quantitative experiments.
A gated memory layer for trustworthy AI-assisted research workflows.
Skill中间件架构：AI时代研究能力的工程化封装（以固定收益研究为例）
Research-engineering portfolio: LLM evaluation, RAG, RLVR/post-training, machine learning, Rust systems, data engineering, and reproducible computation.
Automates hermetic environments (macOS/HPC) to eliminate drift. Provisions offline RAG (Gemma 2), compiles LaTeX manuscripts, and indexes local knowledge. Unifies infrastructure, writing, and inference into a single, audit-ready artifact.
Research prototype for trace-based observability and failure analysis in retrieval-augmented generation.
Factor-aware, physics-guided road-surface intelligence | 因子感知与物理引导的路面状态识别、摩擦可供性估计与可复现实验
Selected LLM, NLP, retrieval and vision-language research-engineering projects.
Event-driven quantitative research evidence engine with real-data risk studies, leakage-safe execution, corrected inference, and reproducible reports.
Open ML systems platform for training, profiling, evaluating, and serving AI models.
AI systems, quantitative research, and governed agent infrastructure.
Research-engineering lab for LLM post-training, behavior evaluation, regression detection, and reliability reporting.
AI-powered Research Assistant using Groq, Llama 3.1, LangChain, and Tool Calling to autonomously search the web, read webpages, and generate structured research reports.
Regime-aware portfolio using machine learning, financial time-series features, risk research engine with dynamic allocation, stress testing, factor diagnostics, reproducible research packages, and a Streamlit dashboard.
PyTorch benchmark for CTM-style adaptive computation, sparse-retrieval failure analysis, adaptive halting, and attention-supervised recovery.
ChronosLOB: Research platform for limit order book representation learning, calibration and execution-aware validation.
Evidence-first research engineering lab for reliable AI agents, evaluations, and ML systems.
Quantitative research and research engineering profile
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