基本信息
浏览量:80
职业迁徙
个人简介
Xiaocheng works at the intersection of reinforcement learning, optimization, control and operation research at DiDi AI Labs. He has published more than five top-conference papers in the field during his first two years at DiDi. Additionally, his work on joint optimization of order dispatching and reposition via reinforcement learning won Best Demo Awards at NeurIPS 2018. He and his team received Daniel H. Wagner prize for Excellence in Operations Research practice at INFORMS 2019.
Xiaocheng is also an Apache Committer contributing to Apache MADlib — a distributed in-database machine learning library. He received his PhD degrees in Optimization and Machine Learning advised by Prof. Katya Scheinberg. During his graduate study, he has been actively engaged in analyzing and designing novel optimization theories and algorithms for large-scale problems arising from various applications and in particular those in Artificial Intelligence. His work in fast and scalable training algorithms and convergence theory was presented at NIPS 2013 and was accepted as a journal paper by Mathematical Programming.
Xiaocheng is also an Apache Committer contributing to Apache MADlib — a distributed in-database machine learning library. He received his PhD degrees in Optimization and Machine Learning advised by Prof. Katya Scheinberg. During his graduate study, he has been actively engaged in analyzing and designing novel optimization theories and algorithms for large-scale problems arising from various applications and in particular those in Artificial Intelligence. His work in fast and scalable training algorithms and convergence theory was presented at NIPS 2013 and was accepted as a journal paper by Mathematical Programming.
研究兴趣
论文作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn