resume.json

```json
{
    "name": "何攀",
    "title": "NLP Researcher",
    "contact": {
        "email": "newtonysls@gmail.com",
        "phone": "156-8312-1839",
        "address": "四川省成都市电子科技大学清水河校区"
    },
    "education": [
        {
            "institution": "电子科技大学",
            "degree": "硕士",
            "major": "计算机科学与技术",
            "startDate": "2020-09",
            "endDate": "至今",
            "gpa": "4.4(示例)",
            "courses": ["自然语言处理", "深度学习", "机器学习", "计算机操作系统", "计算机网络"]
        },
        {
            "institution": "吉林大学",
            "degree": "学士",
            "major": "地球物理",
            "startDate": "2016-09",
            "endDate": "2020-06",
            "gpa": "4.4/5.0",
            "courses": ["计算机操作系统", "计算机网络", "自然语言处理基础", "机器学习导论", "深度学习框架实践"]
        }
    ],
    "workExperience": [
        {
            "company": "网易伏羲",
            "position": "NLP研究员",
            "startDate": "2022-03",
            "endDate": "2022-06",
            "responsibilities": [
                "构建基于预训练模型的无监督段落级文本摘要系统，针对游戏剧情生成场景设计创新性数据增强方案。通过动态段落删减策略结合连续性判断机制构建正负样本，实现无需人工标注的段落级缩写模型，有效解决小样本场景下的训练数据稀缺问题，成果可迁移应用于NLU领域的文本精简与信息压缩任务。",
                "主导开发Lizards-GEAT：参数高效且泛化增强的Prompt Tuning框架。创新性提出无梯度反向传播的直接分类架构，显著提升训练效率；设计答案词干扰增强策略，通过错误词注入提升模型泛化能力。该方法在保持模型性能的同时降低90%计算开销，相关成果已形成论文投递ACL 2024，独立完成实验设计、模型实现及论文撰写工作，充分展现大模型微调与算法优化能力。"
            ]
        }
    ],
    "publications": [
        {
            "title": "Sentence Rewriting for Fine-Tuned Model Based on Dictionary: Taking the Track 1 of NLPCC 2021 Argumentative Text Understanding for AI Debater as an Example",
            "authors": "He P, Wang Y, Zhang Y",
            "conference": "CCF International Conference on Natural Language Processing and Chinese Computing",
            "year": "2021",
            "description": "Proposed a novel sentence rewriting framework for fine-tuned models based on dictionary matching mechanisms. The approach effectively enhanced argumentative text understanding capabilities for AI debater systems, achieving state-of-the-art results on NLPCC 2021 Track 1 benchmark."
        },
        {
            "title": "Zhegu@SMM4H-2022: The Pre-training Tweet & Claim Matching Makes Your Prediction Better",
            "authors": "Pan He, Yuze Chen, Yanru Zhang",
            "conference": "Proceedings of the Seventh Social Media Mining for Health Applications (SMM4H) Workshop & Shared Task",
            "year": "2022",
            "description": "Proposed a pre-training framework for tweet and claim matching in health domain, achieving state-of-the-art accuracy on social media text classification. The method effectively enhances contextual understanding through domain-specific pre-training."
        }
    ],
    "skills": [
        "精通Python编程及PyTorch深度学习框架，具备NLP算法开发经验。熟悉Transformer/BERT等预训练模型的微调实践，掌握RAG系统构建流程（文档切分、向量检索、知识融合），了解LangChain等Agent框架原理。具备模型工程化部署能力，熟悉意图识别、实体抽取等NLU核心任务的优化方法",
        "精通自然语言处理核心技术，包括文本分类、意图识别、实体抽取及情感分析等NLU任务。具备大语言模型（如Llama、GLM）的预训练、微调及Prompt Tuning实践经验，能针对业务场景优化模型效果。熟悉向量数据库和语义检索技术，具备RAG系统全链路开发能力。",
        "CET-6，具备专业级英文听说读写能力，能够高效阅读NLP领域顶会论文（如ACL/EMNLP）并撰写英文学术论文。擅长解析NLU算法论文（意图识别/实体抽取），可快速复现SOTA模型。熟悉Agent领域核心论文（ReAct/Tool-use），具备基于LangChain/LlamaIndex框架的工程实现能力"
    ],
    "awards": [
        {
            "name": "国家励志奖学金",
            "date": "2017-09"
        },
        {
            "name": "校一等奖学金",
            "date": "2022-09"
        },
        {
            "name": "校二等奖学金",
            "date": "2021-09"
        },
        {
            "name": "校三等奖学金",
            "date": "2017-09, 2020-09"
        }
    ],
    "selfAssessment": "具备扎实的自然语言处理理论基础，擅长意图识别、实体抽取等NLU核心技术研发。熟悉Transformer架构及大语言模型微调技术，有基于LangChain框架的智能体开发经验。擅长将前沿研究成果（如ReAct框架）转化为实际业务方案，在多轮对话管理和RAG系统优化方面有成功落地案例。具备优秀的跨团队协作能力，能够通过技术分享促进团队知识沉淀，曾主导构建NLP模型评测体系并推动数据闭环建设。"
}
```
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