阿布扎比大学 | 阿联酋阿布扎比人工智能大学招收全奖博士，年薪20.9W

导师简介

Dr. Zhiqiang was a senior research scientist with Baidu Research at China. He holds the PhD from the school of computer engineering, Nanyang Technological University, Singapore. 

Dr. Zhiqiang research interests lie at the intersection of numerical computation, stochastic optimization, and Riemannian optimization. He is also interested in deep learning, clustering, community detection, topic modeling, etc. His recent ongoing works are about faster alternating least-squares for CCA, comprehensively tight analysis of gradient descent for PCA, accelerated inexact power methods, and Riemannian search for eigenvector computation. 

Dr. Zhiqiang was serving as a reviewer for several academic activities of IJCAI, AAAI, ICLR, ICML, NeurIPS in various years. He also has industrial experiences about automatic optical inspection (AOI) for TFT-LCD panels and solar wafers, data analytics for airlines and insurances.

Zhiqiang Xu and Ping Li. A Comprehensively Tight Analysis of Gradient Descent for PCA. NeurIPS 2021
Zhiqiang Xu and Ping Li. On the Riemannian Search for Eigenvector Computation. JMLR 2021
Zhiqiang Xu, Dong Li, Weijie Zhao, Xing Shen, Tianbo Huang, Xiaoyun Li, and Ping Li. Agile and Accurate CTR Prediction Model Training for Massive-Scale Online Advertising Systems. SIGMOD 2021
Zhiqiang Xu and Ping Li. On the Faster Alternating Least-Squares for CCA. AISTATS 2021
Jiehuan Luo, Xin Cao, Xike Xie, Qiang Qu, Zhiqiang Xu, and Christian S. Jensen. Efficient Attribute-Constrained Co-Located Community Search. ICDE 2020
Yingxue Zhou, Belhal Karimi, Jinxing Yu, Zhiqiang Xu, Ping Li. Towards Better Generalization of Adaptive Gradient Methods. NeurIPS 2020
Zhiqiang Xu and Ping Li. A Practical Algorithm for Computing Dominant Generalized Eigenspace. UAI 2019
Zhiqiang Xu and Ping Li. Towards Practical Alternating Least-Squares for CCA. NeurIPS 2019
Zhiqiang Xu. Gradient descent meets shift-and-invert preconditioning for eigenvector computation. NeurIPS 2018
Zhiqiang Xu and Xin Gao. On Truly Block Eigensolvers via Riemannian Optimization. AISTATS 2018
Zhiqiang Xu, Yiping Ke, and Xin Gao. A Fast Stochastic Riemannian Eigensolver. UAI 2017
Zhiqiang Xu, Peilin Zhao, Jianneng Cao, and Xiaoli Li. Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization. ICML 2016
Zhiqiang Xu, Yiping Ke, Yi Wang, Hong Cheng, and James Cheng. A Model-based Approach to Attributed Graph Clustering. SIGMOD 2012

学术主页：

https://scholar.google.com/citations?user=0R20iBMAAAAJ&hl=en

1. 研究方向包括但不限于以下方面的理论和应用：优化， 强化学习， 深度学习，图学习；

2. 适合但不限于以下专业背景的学生申请：计算机，数学，统计，自动化，电子工程。

2. 热爱研究，诚实，踏实，勤恳；

2. 博士/硕士学生：GPA（4分制）最低 3.2，有雅思或者托福成绩（如果没有，可以后续补齐）；

3. 扎实的数学基础或者编程能力。

博士硕士全额奖学金，并且每年分别给 20.9 万和 16.7 万人民币（按当前汇率，免税），免费住宿和每年往返机票。

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