Data Scientist | Kaggle Competitions Expert | Optimization & Control | Game Theory | ML/DL/RL
数据科学家 | Kaggle 竞赛专家 | 最优化与控制 | 博弈论 | 机器学习/深度学习/强化学习
English:
I'm a Data Scientist and Kaggle Competitions Expert with expertise in Optimization, Game Theory, and Control Algorithms. My work spans Machine Learning, Deep Learning, and Reinforcement Learning, combining mathematical foundations with practical AI applications. Recently I focus on Claude Code Skills, MCP Servers, Edge AI, and Quantitative Finance — building intelligent systems that solve complex decision-making problems in finance, robotics, and the real world.
简体中文：
我是一名数据科学家和 Kaggle 竞赛专家，专长于最优化设计、博弈论和控制算法。我的工作涵盖机器学习、深度学习和强化学习，将数学基础与实际 AI 应用相结合。近期聚焦于 Claude Code 技能包、MCP 服务器、边缘 AI 和量化金融 —— 构建智能系统来解决金融、机器人和现实世界中的复杂决策问题。
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Competitions Expert Feature Engineering Ensemble Modeling 竞赛专家 特征工程 集成建模 |
Convex Optimization Nonlinear Programming Portfolio Optimization 凸优化 非线性规划 投资组合优化 |
Nash Equilibrium Mechanism Design Multi-Agent Games 纳什均衡 机制设计 多智能体博弈 |
Optimal Control MPC / PID System Identification 最优控制 模型预测控制 系统辨识 |
Policy Gradient Q-Learning Multi-Agent RL 策略梯度 Q学习 多智能体强化学习 |
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Supervised / Unsupervised Learning | Feature Engineering | Ensemble Methods 监督/无监督学习 | 特征工程 | 集成方法 |
Neural Networks | Computer Vision | NLP / LLM Applications 神经网络 | 计算机视觉 | NLP / LLM 应用 |
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Claude Code 金融数据技能包，220+ 接口，用自然语言获取 A 股行情、财务、宏观数据。 Financial data skill for Claude Code — 220+ APIs for A-share market via natural language.
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OpenClaw Windows 一键离线安装包，不依赖海外网络，解压即用。 One-click offline installer for OpenClaw on Windows — no overseas network needed.
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面向数据科学的 Claude Code AI Agent 团队，覆盖分析、建模、可视化全流程。 A specialized team of Claude Code AI agents for data analysis, ML & visualization.
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将任意 MCP 服务器桥接到小智硬件，原生支持 STDIO/SSE/StreamableHTTP 三种传输。 Bridge any MCP server to Xiaozhi hardware with three native transports.
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基于 YOLOv8 的人脸识别监控系统，检测到特定人物自动锁定屏幕。 YOLOv8-based face-recognition monitor that auto-locks the screen on target detection.
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📈 SharpETF通过最大化夏普比率寻找最优 ETF 投资组合的量化优化工具。 Quantitative ETF portfolio optimizer via Sharpe-ratio maximization.
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