Building products at the edge of what AI can do. 在智能的边界处,定义下一个产品
你好,我是袁泽华。目前在浙江大学攻读人工智能硕士,本科毕业于华中科技大学网络空间安全学院。
我关注 AI 技术如何从模型、系统与数据,走向真实可用的产品体验。
从联邦学习、医疗 AI,到第一个 AI 产品的构建与落地,我始终对“技术如何被人理解、信任并使用”这件事感兴趣。
紧跟 AI 前沿进展,试图跟上模型能力上限被不断推高的速度,也理解这些能力真正能进入哪些场景。
尝试利用最先进的模型 API,定义并构建属于自己的第一个 AI 产品,从问题、交互到真实可用的体验。
持续思考、提问、交流与记录,把每天遇到的困惑和新理解,慢慢整理成更清晰的判断。
关注大模型应用、联邦学习与医疗 AI,也参与需求定义、方案设计和技术表达相关工作。
AI · Product从网络安全与计算机系统出发,建立了对数据、隐私与复杂技术系统的底层理解。
EducationWorked on FedPMR, a personalized prototype-based federated learning framework for multi-center accelerated MRI reconstruction. The system supports cross-institution model collaboration while keeping raw medical data local.
Result: Validated on three public datasets and one private dataset from The First Affiliated Hospital, Zhejiang University. Communication cost was reduced from 236.70 Mb to 8 Kb, with stronger reconstruction performance on heterogeneous sites.
Authors: Jing Wang, Tong Wang, Siyuan Luo, Xiaoran Guo, Jiayi Yin, Jing Li, Zehua Yuan, Gang Yu, Bo Lin.
Built a knowledge-base Q&A system over PDF, DOCX, and Markdown documents using OpenAI APIs.
Design: Combined semantic chunking, embeddings, vector retrieval, and prompt design to reduce omissions and hallucinations in complex document Q&A, turning raw materials into an interactive knowledge base.
Designed a FedAvg-based multi-client training framework for privacy-sensitive behavior recognition, enabling model collaboration without moving local data.
Balance: Focused on the tradeoff between model performance, data locality, privacy protection, and deployability.
Contributed to a smart greenhouse digitization project, from county-level rollout planning to technical implementation design.
Delivery: Integrated data flows from two greenhouses, a compact weather station, and the ULAND IoT enablement platform, turning environmental monitoring and weather alerts into planting decision support.
Supported front-desk service and information-system maintenance at a university counseling center. The experience shaped my understanding that good systems should be efficient, privacy-aware, and humane.
关注大模型应用、联邦学习与医疗 AI。既想理解模型内部发生了什么,也在意它最终能为谁解决什么问题。技术深度是产品判断力的底座。
好的产品决策来自对用户、技术与商业三者之间张力的持续感知。我喜欢拆解一个产品"为什么这样设计",比它"做了什么"更有意思。
AI 能力需要好的界面才能被感知到。关注 AI 产品的交互范式——如何设计提示词、反馈、置信度表达,让智能变得可信赖而非神秘。
我也喜欢把复杂概念讲清楚。作为浙大 SOFTMAX 宣讲团成员,面向中学生与公共部门人员分享大模型、Agent、AI 治理与数据合规,让技术从术语回到人的理解里。
AI 产品最难的不是模型,是人。用户如何建立对 AI 的信任、在哪里放弃、对“足够好”的阈值在哪——这些比 benchmark 更值得研究。
合唱和舞台训练让我对协作、节奏和表达有一种身体层面的理解。技术之外,我也珍惜那些关于声音、秩序与共振的经验。
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如果你对某个话题有相似的兴趣,或者只是想聊聊,欢迎写信给我。
I reply to every thoughtful message. Don't hesitate to reach out.