基本信息
周峰，博士，中国人民大学统计学院，电子邮箱：feng.zhou[@]ruc[DOT]edu[DOT]cn，个人主页
教育背景
2016-2019 新南威尔士大学，工学博士
2011-2014 中国科学院大学，工学硕士
2007-2011 北京林业大学，工学学士
工作经历
2022-至今 中国人民大学统计学院
2020-2022 清华大学计算机系，博士后
2021.04-2021.12 琶洲实验室，助理研究员（双聘）
2020.01-2020.06 悉尼科技大学，研究助理
研究领域
随机过程
Poisson process、Cox process、Hawkes process
贝叶斯非参数
Gaussian process、Dirichlet process
贝叶斯推断
Markov chain Monte Carlo、variational inference
元学习/多任务学习/联邦学习
Bayesian meta learning、Bayesian multitask learning、Bayesian federated learning
招生信息
详见个人主页
近期论文
- Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen, “Fair Representation Learning with Unreliable Labels”, AISTATS 2023.
- Feng Zhou, Quyu Kong, Zhijie Deng, Jichao Kan, Yixuan Zhang, Cheng Feng, Jun Zhu, “Efficient Inference for Dynamic Flexible Interactions of Neural Populations”, Journal of Machine Learning Research, 2022.
- Zhijie Deng, Feng Zhou, Jun Zhu, “Accelerated Linearized Laplace Approximation for Bayesian Deep Learning”, NeurIPS 2022.
- Xuhui Fan, Bin Li, Feng Zhou, Scott A Sisson, “Continuous-Time Edge Modelling Using Non-Parametric Point Processes”, NeurIPS 2021.
- Feng Zhou, Yixuan Zhang, Jun Zhu, “Efficient Inference of Flexible Interaction in Spiking-neuron Networks”, ICLR 2021.
- Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen, “Efficient EM-Variational Inference for Nonparametric Hawkes Process”, Statistics and Computing, 2021.
- Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang and Fang Chen, “Bias-Tolerant Fair Classification”, ACML 2021.
- Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya and Fang Chen, “Efficient Inference for Nonparametric Hawkes Processes Using Auxiliary Latent Variables”, Journal of Machine Learning Research, 2020.
- Simon Luo, Feng Zhou, Lamiae Azizi, Mahito Sugiyama, “Learning Joint Intensity in a Multivariate Poisson Process on Statistical Manifolds”, NeurIPS 2020 Workshop: Deep Learning through Information Geometry.
讲授课程
-《最优化方法》
-《数据科学专题：时空点过程》
-《现代优化方法》
获奖情况
国家自然科学基金青年科学基金项目
中国博士后国际交流计划引进项目
中国博士后科学基金面上资助
中国博士后科学基金特别资助
Basic Information
Feng Zhou, PhD, School of Statistics, Renmin University of China, email: feng.zhou at ruc.edu.cn
Education
2016-2019 University of New South Wales, Ph.D. Computer of Science
2011-2014 University of Chinese Academy of Sciences, M.S. Electrical Engineering
2007-2011 Beijing Forestry University, B.S. Electrical Engineering
Work Experience
2022-Now School of Statistics, Renmin University of China
2020-2022 Department of Computer Science, Tsinghua University, Postdoctoral Fellow
2021.04-2021.12 Pazhou Lab, Assistant Researcher
2020.01-2020.06 University of Technology Sydney, Research Assistant
Research Area
Stochastic Process, Bayesian nonparametric, Bayesian inference, Bayesian meta learning/multitask learning/federated learning
Recent Publication
- Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen, “Fair Representation Learning with Unreliable Labels”, AISTATS 2023.
- Feng Zhou, Quyu Kong, Zhijie Deng, Jichao Kan, Yixuan Zhang, Cheng Feng, Jun Zhu, “Efficient Inference for Dynamic Flexible Interactions of Neural Populations”, Journal of Machine Learning Research, 2022.
- Zhijie Deng, Feng Zhou, Jun Zhu, “Accelerated Linearized Laplace Approximation for Bayesian Deep Learning”, NeurIPS 2022.
- Xuhui Fan, Bin Li, Feng Zhou, Scott A Sisson, “Continuous-Time Edge Modelling Using Non-Parametric Point Processes”, NeurIPS 2021.
- Feng Zhou, Yixuan Zhang, Jun Zhu, “Efficient Inference of Flexible Interaction in Spiking-neuron Networks”, ICLR 2021.
- Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen, “Efficient EM-Variational Inference for Nonparametric Hawkes Process”, Statistics and Computing, 2021.
- Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang and Fang Chen, “Bias-Tolerant Fair Classification”, ACML 2021.
- Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya and Fang Chen, “Efficient Inference for Nonparametric Hawkes Processes Using Auxiliary Latent Variables”, Journal of Machine Learning Research, 2020.
- Simon Luo, Feng Zhou, Lamiae Azizi, Mahito Sugiyama, “Learning Joint Intensity in a Multivariate Poisson Process on Statistical Manifolds”, NeurIPS 2020 Workshop: Deep Learning through Information Geometry.
Courses
- Optimization
- Special Topics in Data Science: Spatio-temporal Point Processes