Curriculum Vitae

Contact: wurz1990#gmail.com · wrz179#163.com

Education / 教育背景

• Postdoc, Computer Application Technology, Zhejiang University, Hangzhou, 2021 – 2023
  • 博士后,计算机应用技术,游戏 AI / 游戏用户画像;导师:高云君教授
• Ph.D., Computer Application Technology, University of Science and Technology of China (USTC), Hefei, 2013 – 2018
  • 博士,计算机应用技术,教育数据挖掘 / 能力水平建模;导师:陈恩红教授、刘淇教授
• Visiting Scholar, Data Science, University of Technology, Sydney, Australia, 2016 – 2017
  • 访问学者,数据科学,金融数据挖掘 / 风险评估;访问导师:徐贯东教授
• B.S., Computer Science and Technology, University of Science and Technology of China (USTC), Hefei, 2009 – 2013
  • 学士,计算机科学与技术

Work Experience / 工作经历

NetEase Fuxi AI Lab, Hangzhou (网易伏羲实验室,杭州) — Dec 2018 – Present

• Head of Collective Intelligence (群体智能负责人), Oct 2023 – Present
  • Lead R&D and deployment of AI Agents in games and human-robot collaboration; drive toB/toC innovation as business owner (12 full-time staff + 10+ interns; managing product, design, BD, operations, and full-stack engineering).
  • 负责游戏 AI Agent 与机器人人机协作方向的研发与业务落地,作为业务一号位牵头 toB/toC 创新业务拓展。
  • Empowered NetEase Leihuo game development with AI Agents and intelligent NPC gameplay, serving titles such as Justice (逆水寒), Naraka: Bladepoint (永劫无间) and others.
  • Built China’s first AI-native game from scratch (team building, design, R&D, marketing & publishing); achieved 80% next-day retention and launched on Steam — the first AI-native game in China with commercialization potential.
  • Built a human-AI-collaborative visual inspection solution (smart retail, smart inspection) on VLMs, reaching 30M annual service calls at 99% accuracy (±0.5%), in long-term partnership with top domestic partners.
• Head of User Profiling Research (用户画像研究负责人), Oct 2020 – Oct 2023
  • Led research and business support on game user profiling and trustworthy AI (managed ~10 full-time staff + 10+ interns).
  • 负责游戏智能画像与可信 AI 两大方向的课题研究与业务支撑。
  • Game bot/cheat detection via player behavior anomaly detection, with cheat-mitigation strategy guided by game-economy modeling.
  • Player–item recommendation & matching to lift revenue while protecting player flow experience.
  • Annotator-ability and annotation-noise modeling to deliver high-quality data at low cost on the Fuxi crowdsourcing platform.
  • Model interpretability & human-AI labor-incentive design via cooperative game theory, supporting model tuning, compliance, and healthy crowdsourcing operations.
• AI Researcher (人工智能研究员), Dec 2018 – Oct 2020
  • Led university collaboration and prediction-attribution research for the user-profiling direction.
  • 负责用户画像方向的高校合作与预测归因重点研究。
  • Connected dozens of domestic & international collaborators, producing multiple joint projects.
  • Built a general LTV-prediction framework for MMO games, achieving +2% 7-day retention in deployed scenarios.
  • Built general server-merge and whole-server metric prediction frameworks, deployed across popular NetEase MMOs.
  • Built churn-cause analysis frameworks deployed across multiple NetEase games at different lifecycle stages.
• Head of University Relations (产学研负责人), Oct 2021 – Present
  • Lead Leihuo/Fuxi University Relations (UR), advancing frontier research with top universities for mutual benefit.
  • 通过雷火伏羲 UR 与国内外顶尖高校学者合作推进前沿课题;运营 UR 平台,与 CCF / CAAI 等机构合作推进产学研项目。

Selected Publications / 代表性论文

80+ papers with ~3000 citations. See Google Scholar and the Publications page. Selected works:

Balance-Subsampled Stable Prediction Across Unknown Test Data

Kuang, Kun, Hengtao Zhang, Runze Wu, Fei Wu, Yueting Zhuang, and Aijun Zhang. "Balance-Subsampled Stable Prediction Across Unknown Test Data." ACM Transactions on Knowledge Discovery from Data (TKDD) 16, no. 3 (2021): 1-21.

Build Your Own Bundle-A Neural Combinatorial Optimization Method

Deng, Qilin, Kai Wang, Minghao Zhao, Runze Wu*, Yu Ding, Zhene Zou, Yue Shang, Jianrong Tao, and Changjie Fan. "Build Your Own Bundle-A Neural Combinatorial Optimization Method." In Proceedings of the 29th ACM International Conference on Multimedia, pp. 2625-2633. 2021.

Bilateral filtering graph convolutional network for multi-relational social recommendation in the power-law networks

Zhao, Minghao, Qilin Deng, Kai Wang, Runze Wu*, Jianrong Tao, Changjie Fan, Liang Chen, and Peng Cui. "Bilateral filtering graph convolutional network for multi-relational social recommendation in the power-law networks." ACM Transactions on Information Systems (TOIS) 40, no. 2 (2021): 1-24.

Globally Optimized Matchmaking in Online Games

Deng, Qilin, Hao Li, Kai Wang, Zhipeng Hu, Runze Wu*, Linxia Gong, Jianrong Tao, Changjie Fan, and Peng Cui. "Globally Optimized Matchmaking in Online Games." In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2753-2763. 2021.

Find Your Organization in MMORPGs

Deng, Qilin, Minghao Zhao, Kai Wang, Runze Wu*, Xudong Shen, Jianrong Tao, and Changjie Fan "Find Your Organization in MMORPGs," in IEEE Transactions on Games, 2021, doi: 10.1109/TG.2021.3104319.

Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation

Wang, Kai, Zhene Zou, Qilin Deng, Jianrong Tao, Runze Wu*, Changjie Fan, Liang Chen, and Peng Cui. "Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, pp. 4427-4435. 2021.

NeuralAC: Learning Cooperation and Competition Effects for Match Outcome Prediction

Gu, Yin, Qi Liu, Kai Zhang, Zhenya Huang, Runze Wu, and Jianrong Tao. "NeuralAC: Learning Cooperation and Competition Effects for Match Outcome Prediction." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, pp. 4072-4080. 2021.

Prior Aided Streaming Network for Multi-task Affective Analysis

Zhang, Wei, Zunhu Guo, Keyu Chen, Lincheng Li, Zhimeng Zhang, Yu Ding, Runze Wu, Tangjie Lv, and Changjie Fan. "Prior Aided Streaming Network for Multi-task Affective Analysis." In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3539-3549. 2021.

Deep Behavior Tracing with Multi-level Temporality Preserved Embedding

Wu, Runze, Hao Deng, Jianrong Tao, Changjie Fan, Qi Liu, and Liang Chen. "Deep Behavior Tracing with Multi-level Temporality Preserved Embedding." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2813-2820. 2020.

Match Tracing: A Unified Framework for Real-time Win Prediction and Quantifiable Performance Evaluation

Wang, Kai, Hao Li, Linxia Gong, Jianrong Tao, Runze Wu, Changjie Fan, Liang Chen, and Peng Cui. "Match Tracing: A Unified Framework for Real-time Win Prediction and Quantifiable Performance Evaluation." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2781-2788. 2020.

Personalized Bundle Recommendation in Online Games

Deng, Qilin, Kai Wang, Minghao Zhao, Zhene Zou, Runze Wu, Jianrong Tao, Changjie Fan, and Liang Chen. "Personalized Bundle Recommendation in Online Games." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2381-2388. 2020.

XAI-Driven Explainable Multi-view Game Cheating Detection

Tao, Jianrong, Yu Xiong, Shiwei Zhao, Yuhong Xu, Jianshi Lin, Runze Wu, and Changjie Fan. "XAI-Driven Explainable Multi-view Game Cheating Detection." In 2020 IEEE Conference on Games (CoG 2020), pp. 144-151. IEEE, 2020.

Multi-source Data Multi-task Learning for Profiling Players in Online Games

Zhao, Shiwei, Runze Wu, Jianrong Tao, Manhu Qu, Hao Li, and Changjie Fan. "Multi-source Data Multi-task Learning for Profiling Players in Online Games." In 2020 IEEE Conference on Games (CoG 2020), pp. 104-111. IEEE, 2020.

Optmatch: Optimized matchmaking via modeling the high-order interactions on the arena

Gong, Linxia, Xiaochuan Feng, Dezhi Ye, Hao Li, Runze Wu, Jianrong Tao, Changjie Fan, and Peng Cui. "Optmatch: Optimized matchmaking via modeling the high-order interactions on the arena." In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2300-2310. 2020.

Mvan: Multi-view attention networks for real money trading detection in online games

Tao, Jianrong, Jianshi Lin, Shize Zhang, Sha Zhao, Runze Wu, Changjie Fan, and Peng Cui. "Mvan: Multi-view attention networks for real money trading detection in online games." In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2536-2546. 2019.

Confidence-aware matrix factorization for recommender systems

Wang, Chao, Qi Liu, Runze Wu, Enhong Chen, Chuanren Liu, Xunpeng Huang, and Zhenya Huang. "Confidence-aware matrix factorization for recommender systems." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1. 2018.

Fuzzy cognitive diagnosis for modelling examinee performance

Liu, Qi, Runze Wu, Enhong Chen, Guandong Xu, Yu Su, Zhigang Chen, and Guoping Hu. "Fuzzy cognitive diagnosis for modelling examinee performance." ACM Transactions on Intelligent Systems and Technology (TIST) 9, no. 4 (2018): 1-26.

Tracking knowledge proficiency of students with educational priors

Chen, Yuying, Qi Liu, Zhenya Huang, Le Wu, Enhong Chen, Runze Wu, Yu Su, and Guoping Hu. "Tracking knowledge proficiency of students with educational priors." In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 989-998. 2017.

Knowledge or gaming? Cognitive modelling based on multiple-attempt response

Wu, Runze, Guandong Xu, Enhong Chen, Qi Liu, and Wan Ng. "Knowledge or gaming? Cognitive modelling based on multiple-attempt response." In Proceedings of the 26th International Conference on World Wide Web Companion, pp. 321-329. 2017.

Collaborative learning team formation: a cognitive modeling perspective

Liu, Yuping, Qi Liu, Runze Wu, Enhong Chen, Yu Su, Zhigang Chen, and Guoping Hu. "Collaborative learning team formation: a cognitive modeling perspective." In International Conference on Database Systems for Advanced Applications, pp. 383-400. Springer, Cham, 2016.

Cognitive Modelling for Predicting Examinee Performance

Wu, Runze, Qi Liu, Yuping Liu, Enhong Chen, Yu Su, Zhigang Chen, and Guoping Hu. (2015) "Cognitive modelling for predicting examinee performance." In Twenty-Fourth International Joint Conference on Artificial Intelligence.

Skills / IT 技能

• Programming Languages: Python = Matlab > C++ > Java

Awards & Academic Service / 获奖和学术服务

• Best Paper Award, IEEE Conference on Games (CoG), 2020
• Hangzhou Class-C Talent (杭州市 C 类人才), 2025
• Reviewer / PC Member: NeurIPS, AAAI, IJCAI, KDD, WWW, Cell Patterns, AI Journal

Others / 其他

• Professional Title: Senior Engineer of Information Technology, Zhejiang Province (浙江省信息技术高级工程师,副高)
• Hobbies: cycling, long-distance running (骑行,长跑)
• Languages: Chinese (native); English — spoken (daily & academic communication), reading & writing (proficient)