Xiaoning Wang
«««< ours I am currently an Associate Professor in the School of Data Science and Media Intelligence at Communication University of China. I obtained my Ph.D. degree from the School of Statistics, Renmin University of China in 2019, under the supervision of Prof. Yongjin Jin.
My research focuses on the intersection of sampling survey techniques, machine learning, and media data analytics. I am particularly interested in developing intelligent systems that leverage advanced AI technologies to solve complex problems in media and communication.
🔬 Current Research Projects
- Intelligent Teaching Assistant (Scholar Hero): An LLM+Agent system designed to enhance teaching and learning experiences. Try it out: scholarhero.cn
- AI-Powered Media Analytics: Developing algorithms for analyzing large-scale media data using machine learning and deep learning techniques
- Smart Survey Systems: Integrating sampling methods with AI to improve survey efficiency and accuracy
🎓 Education
======= I am an Associate Professor in the School of Data Science and Media Intelligence at the Communication University of China, focused on cultivating data-driven media talent. I earned my Ph.D. from the School of Statistics, Renmin University of China in 2019 under the supervision of Prof. Yongjin Jin. My teaching centers on building rigorous courses, guiding students through hands-on projects, and using intelligent tools to support collaborative learning.
Our team is developing Scholar Hero, an intelligent teaching assistant (LLM + Agent) that provides classroom demonstrations, coding guidance, and personalized feedback. Teachers and students are welcome to explore and share suggestions: scholarhero.cn.
教学特色与贡献
- 课程建设与更新：主讲数据科学导论、采样调查、媒体数据分析等课程，持续将统计学习、大模型与传媒场景结合，形成循序渐进的课程链条。
- 实践驱动：在每门课程中加入数据采集、可视化、建模和应用案例，鼓励学生完成真实项目和优秀作业展示，搭建面向传媒领域的项目库。
- 智能助教探索：依托 Scholar Hero 辅助课堂讲解、作业辅导和即时问答，打造开放的“教、学、研”互动生态。
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代表课程与资源
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🔍 Research Interests
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- 数据科学导论：覆盖数据采集、统计学习方法、自然语言处理、推荐算法与分布式计算，提供教材化讲义与案例代码，帮助学生从零起步进入数据科学。【F:_teaching/2021-0-data-science.md†L1-L35】
- 课程资料与优秀作业：整理 SVM、数据智慧原则、文本分析等幻灯片，并展示优秀报告，方便学生复习与二次创作。【F:_teaching/2021-0-data-science.md†L37-L57】
- 更多课程信息可在教学页面与学习资料合集获取。
学生培养
- 指导学生参与数据分析比赛与项目实践，形成多篇优秀报告和课堂视频案例，为后续年级提供参考。
- 鼓励跨学科合作，帮助传媒、统计与计算机背景学生在真实数据上完成创新性探索。
教育背景
- 2019，统计学博士，中国人民大学统计学院
- 2017，统计学硕士，中国人民大学统计学院
- 2013，应用数学学士，天津理工大学数学学院
研究兴趣
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LLM architecture, fine-tuning, prompt engineering, multi-agent systems
Complex sampling designs, survey methodology, data quality assessment
Supervised/unsupervised learning, deep learning, reinforcement learning
Text mining, social network analysis, sentiment analysis, content recommendation
Data fusion, missing data imputation, big data processing
📊 GitHub Stats
🌐 Connect with Me
- GitHub: @xiaoningwang
- Email: sdwangxiaoning@foxmail.com
- Scholar Hero: scholarhero.cn
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