/ 用户感知 USER INSIGHT
/ 数据认知 DATA JUDGMENT
/ 技术理解 TECH LITERACY
/ AI 产品不止是功能想象，而是可验证的用户价值AI products are not just feature ideas,
but verifiable user value
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硕士 / MASTER
985
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工业设计硕士在读，以专业排名第 2 保研。M.Des. candidate in Industrial Design, admitted by recommendation with a No. 2 ranking in major performance.
研究重点：机器学习基础、复杂数据统计分析（SPSS / SQL）与系统性设计方法。Focus: machine learning fundamentals, advanced data analysis (SPSS / SQL), and systematic design methods.
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本科 / BACHELOR
211
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本科阶段由轻化工程转入工业设计，转入前专业排名第 1（GPA 3.85 / 4.0），毕业时工业设计专业排名第 2（GPA 3.77 / 4.0）。Transferred from Light Chemical Engineering to Industrial Design, ranking No. 1 before transfer and graduating No. 2 in Industrial Design.
成果：论文 2 篇、奖学金 10 项、设计奖 20+ 项、专利 5 项（发明 1 / 实用新型 3 / 外观 1）。Selected outcomes: 2 papers, 10 scholarships, 20+ design awards, and 5 patents.
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Project.01 PaperFlow
腾讯未来产品经理训练营
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围绕论文格式排版场景，针对规则繁琐、反馈修改成本高、格式规范门槛高等痛点，完成用户痛点分析、竞品调研、需求拆解、PRD 撰写、高保真原型设计与 Demo 验证，输出面向论文写作流程的 AI 自动排版工具方案。/Built an AI paper-formatting tool proposal for thesis writing workflows, covering user pain-point analysis, competitor research, requirement breakdown, PRD, hi-fi prototype, and demo validation.
独立负责人（产品 / 设计 / 全栈开发）/Independent lead (product / design / full-stack development)
内容：负责从问题定义到原型验证的完整产品推进，梳理格式识别、规范校验、错误定位、修改反馈等关键任务，提出“文档导入 - 自动解析 - 规范校验 - 一键纠排”的核心流程；基于 Figma、VS Code、Codex 与大模型 API 搭建可访问 Demo。/Led the product workflow from problem definition to prototype validation, defining document import, parsing, standards validation, correction, and feedback with a working LLM demo.
测试与产出：开展小范围用户测试，验证核心功能可用性、纠错流程顺畅度与识别结果可理解性；根据反馈增加“跳转上一步”等流程控制能力，形成覆盖需求分析、PRD、原型、Demo、测试反馈与迭代记录的产品闭环。/Ran small-scale user testing on feature usability, correction flow, and result clarity, then added back-step navigation and delivered a product loop across PRD, prototype, demo, feedback, and iteration.
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Project.02 AI 在线学习系统
北京理工大学科研项目
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针对 AI 在线学习系统特征与学生学习体验问题，参与研究框架及量化问卷设计，从功能适配、教学流程覆盖、认知易用性等维度量化用户体验，为需求识别与体验优化建立数据基础。/Helped design the research framework and quantitative survey, measuring AI learning-system experience through functional fit, teaching-process coverage, and cognitive usability.
核心研究员/Core researcher
数据洞察：共回收 384 份有效问卷并完成信效度检验；采用 PLS-SEM + ANN 分析系统特征、感知学习效果与学习满意度之间的关系，识别教学流程覆盖度为与感知学习效果关联最强的系统特征，并验证认知易用性与学习满意度显著直接关联。/Analyzed 384 valid questionnaires with PLS-SEM and ANN, identifying teaching-process coverage as the system feature most strongly associated with perceived learning outcomes and cognitive usability as directly associated with learning satisfaction.
研究转化：将统计结果映射为教学流程适配、认知易用性与学习效果反馈三类产品机会点，形成优化建议及优先级判断；部分成果发表于 Systems（2026），DOI：10.3390/systems14091100。/Translated findings into prioritized opportunities for process fit, cognitive usability, and learning feedback; partial results were published in Systems (2026), DOI: 10.3390/systems14091100.
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Project.03 智能可穿戴产品
华为 & 北京理工大学
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围绕智能可穿戴设备在生理监测、柔性传感、无感佩戴与智能反馈中的发展方向，开展前沿技术调研与用户场景研究，探索 AI 与智能硬件结合下的新一代可穿戴产品机会。/Researched frontier technologies and user scenarios for physiological monitoring, flexible sensing, seamless wearing, and intelligent feedback in AI-enabled wearables.
产品策划/Product planner
内容：负责前沿技术方向与应用场景的梳理和评估，调研汗液传感、柔性传感、LLM+智能纺织品等 10 余项技术，按技术原理、可监测信号、应用场景、体验收益与限制条件进行整理；结合睡眠、骑行等场景，分析其在连续监测、即时反馈和低负担佩戴中的落地可行性。/Evaluated 10+ technologies across principles, signals, scenarios, benefits, and constraints, then assessed feasibility in sleep and cycling scenarios.
产品转化：基于桌面研究搭建骑行用户行为模型，结合用户旅程图识别核心痛点，将异常动作反馈滞后、风险提醒不及时、佩戴干扰等问题转化为产品机会点，并参与下一代胸前可穿戴设备的概念方案策划与多场景交互方向讨论。/Built cycling behavior models, identified journey pain points, and translated delayed feedback, risk alerts, and wearing friction into product opportunities and concept directions.
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Project.04 “关怀辈至”创新用户体验设计
美的 & 江南大学
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围绕美的美居 App 空调模块的使用体验，针对功能入口分散、模式理解成本高、远程照护反馈不足等问题，开展竞品分析、可用性测试与问题归因研究，推动研究结果向功能策略和界面方案转化。/Studied the Midea SmartHome AC module through competitor analysis, usability testing, and issue diagnosis, then translated findings into feature strategy and interface solutions.
核心研究员/Core researcher
内容：负责竞品体验研究与测试方案设计，从用户体验五要素出发分析美的美居、米家、华为智慧生活、小度等产品的结构与功能差异；组织专家用户与普通用户可用性测试，完成任务脚本、测试规划、行为记录和数据分析，并使用 SUS、NASA-TLX、NPS 等量表评估产品可用性、任务负担和用户满意度。/Led competitor research and usability testing, including task scripts, behavior records, data analysis, and SUS, NASA-TLX, and NPS evaluation.
产品转化：基于测试结果完成问题分类与优先级梳理，将“看不懂模式、找不到高频控制、远程操作后缺少确认”等体验断点转化为功能策略，提出“关怀辈至”空调照护模式，围绕状态理解、控制聚合和安心确认组织核心界面链路。/Translated usability breaks into the Care for Elders AC mode, organizing the core interface around status understanding, control aggregation, and reassurance feedback.
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从用户任务、场景断点和情绪阻力出发定义问题，能够通过访谈、问卷、可用性测试和竞品拆解，把模糊需求转译为清晰的产品机会与功能优先级。Starts from user tasks, scenario breaks, and emotional friction, then translates ambiguous needs into product opportunities and feature priorities through research, surveys, usability testing, and competitor analysis.
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具备 Python、SQL、SPSS 等基础能力，重视用数据验证判断：从样本清洗、信效度检验、回归与聚类分析，到把结论转化为画像、指标和可比较的产品决策依据。Uses data to validate product judgment, from sample cleaning, reliability checks, regression, and clustering to personas, metrics, and comparable decision evidence.
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理解 AI 能力边界与产品落地约束，能够把大模型、数据、硬件传感和多模态交互等技术能力转译为用户可感知的功能流程，并通过原型验证方案可行性。Understands AI capability boundaries and delivery constraints, translating LLMs, data, sensors, and multimodal interaction into user-facing workflows and validating feasibility through prototypes.