机械设计制造及其自动化MECHANICAL DESIGN, MANUFACTURING & AUTOMATION
面向智能制造、精密机械与人工智能交叉方向，关注机械系统设计、智能装备与视觉算法的融合应用。
Working across intelligent manufacturing, precision machinery, and artificial intelligence, with an interest in linking mechanical systems, intelligent equipment, and vision algorithms.
结构、数据与实验并不是分离的模块。我的学习经历让我逐渐形成一种工作方式：从真实系统出发，用可验证的模型和实现把不同学科连接起来。
Structure, data, and experiments are not isolated modules. My work starts from real systems and connects disciplines through verifiable models and implementations.
机械设计制造及其自动化专业的学习让我理解结构、运动、制造约束与可实现性。
My major provides a grounding in structure, motion, manufacturing constraints, and practical realization.
医学图像分类训练使我接触数据整理、标注、模型训练与基础调试。
Medical image classification introduced data curation, annotation, model training, and basic debugging.
卫星遥感反演项目让我认识到物理模型、观测数据与科研代码需要在同一闭环中被检验。
Satellite inversion taught me to test physical models, observations, and research code within one loop.
每个项目以“输入—处理—输出”方式拆解，重点呈现我在其中承担的工作、形成的能力与下一步思考。
Each project is presented as input, process, and output, with my contribution, capabilities gained, and the next question made explicit.
面向家庭服务场景，团队完成能够扫地、拖地、夹取并送达物品的机器人。系统支持语音和手机蓝牙控制，我负责从功能设想到实体制作的结构方案与实现。
For home-service scenarios, our team built a robot capable of sweeping, mopping, grasping, and delivering items, controlled by voice or Bluetooth. I led the structural concept and physical implementation.
能力增量：CAPABILITY GAIN: 项目让我认识到，系统设计需要在机构可行性、制造成本、操作体验与团队协同间持续权衡；我也形成了从需求到可验证原型的推进习惯。The project trained me to balance mechanism feasibility, fabrication cost, user experience, and team coordination, while moving from requirements to a testable prototype.
围绕宫颈细胞学图像分类任务，我参与图像整理、样本标注和深度学习模型训练，完成了模型训练流程，并在实践中理解数据质量与实验规范对结果的影响。
For cervical cytology image classification, I contributed to image curation, sample annotation, and deep-learning model training, completing the training workflow and learning the impact of data quality and experimental rigor.
能力增量：CAPABILITY GAIN: 比起把模型训练视为一次性运行，我更重视数据预处理、评价标准和错误分析。它也让我看到算法进入真实应用前必须面对的数据偏差与可解释性问题。Rather than seeing training as a one-time run, I now emphasize preprocessing, evaluation criteria, and error analysis, including data bias and interpretability before deployment.
在卫星遥感气体反演项目中，我围绕科研代码和反演流程进行学习，理解观测数据、辐射传输、物理约束与数值计算之间的关系，并完成相关结果的运行与整理。
In satellite gas inversion, I studied the research code and inversion workflow, connecting observations, radiative transfer, physical constraints, and numerical computation, then ran and organized the resulting outputs.
能力增量：CAPABILITY GAIN: 我认识到科研代码并不是黑箱：理解公式、数据流和参数含义，才能判断输出是否可靠。这种从机理到实现的阅读方式也影响了我对智能制造研究的期待。Research code is not a black box: understanding equations, data flow, and parameters is necessary to judge output reliability. This mechanism-to-implementation perspective informs my future research goals.
以机械设计制造及其自动化为主线，在联合培养与跨学科项目中扩展光学、测控、电子、遥感与人工智能知识。
Built on Mechanical Design, Manufacturing and Automation, expanded through joint training and interdisciplinary work in optics, instrumentation, electronics, remote sensing, and AI.
机械设计制造及其自动化专业。本科前五学期 GPA 4.724/5.0，专业排名 2/295，预计 2027 年毕业。Mechanical Design, Manufacturing and Automation. GPA 4.724/5.0 and rank 2/295 in the first five semesters; expected graduation in 2027.
修读传感器及测试技术、工程热力学、工程光学、环境遥感与机器人设计制作等课程。Coursework in sensors and testing, thermodynamics, engineering optics, environmental remote sensing, and robotics.
希望在研究生阶段围绕真实机械系统的智能化开展系统学习与研究。I hope to study intelligence for real mechanical systems in graduate school.
我希望以精密机械结构为基础，将传感、机器视觉和人工智能融入机械系统的感知、决策与执行过程。
I hope to build on precision mechanical structures and integrate sensing, machine vision, and AI into the perception, decision, and action of mechanical systems.
我不把这个方向理解为简单地给传统机械系统增加算法。机械精度、传感误差、环境变化、数据质量与实时控制需要一起被考虑；真正有价值的成果，应当稳定、可解释、可复现地运行在真实设备上。
I do not see this field as simply adding algorithms to conventional machines. Mechanical accuracy, sensing errors, environmental change, data quality, and real-time control must be considered together. Valuable research should run on real equipment with stability, interpretability, and reproducibility.
结构精度决定系统能够达到的物理上限，也是可靠执行的基础。
Structural accuracy defines the physical ceiling and grounds reliable execution.
通过测量、误差分析与补偿，建立对系统和环境状态的可靠认知。
Use measurement, error analysis, and compensation to form reliable system awareness.
以物理模型作为约束或先验，提升数据驱动方法的可靠性、解释性与泛化能力。
Use physical models as constraints or priors to improve reliability, interpretability, and generalization.
在家庭、制造或服务场景中实现任务规划、可靠执行与自然的人机交互。
Enable task planning, reliable execution, and intuitive interaction in home, manufacturing, and service scenarios.
稳定节奏、团队意识和对真实使用需求的关注，也是我参与科研与工程项目的重要基础。
Consistency, teamwork, and attention to real user needs are also foundations of how I approach research and engineering.
足球和篮球是我长期保持的兴趣。团队运动让我习惯在快速变化的环境中观察、沟通和承担角色，也帮助我在高强度学习中维持稳定节奏。比赛中的协作并不只是分工，而是根据现场状态及时调整，这与工程项目中的系统联调和团队推进有相似之处。
Football and basketball have been long-term interests. Team sports train me to observe, communicate, and assume responsibility in changing situations, while maintaining a steady rhythm through intensive study. Their real-time coordination resembles systems integration in engineering projects.
累计 82.1 小时的志愿服务让我更愿意从使用者角度理解需求。对我而言，工程设计不仅要证明技术能够实现，也要考虑它是否真正方便、可靠，并能解决具体问题。
My 82.1 hours of volunteer service strengthened my willingness to understand needs from the user perspective. Engineering should not only prove a function is possible, but also be useful, reliable, and responsive to a concrete problem.
竞赛结果从不同侧面证明力学基础、工程实践与团队组织能力。证书可直接放大查看。
These outcomes evidence mechanics fundamentals, engineering practice, and team leadership. Certificates can be enlarged directly.
家庭服务机器人项目Home service robot project
2025
2025
期待参与面向真实问题的科研与工程实践。研究兴趣包括智能制造、精密机械、机器人系统与人工智能交叉应用。
Open to research and engineering opportunities grounded in real-world problems. My interests include intelligent manufacturing, precision machinery, robotic systems, and applied artificial intelligence.