Hongzhi Shi 时鸿志

Profile

Ph.D. Student, Data Science & Intelligence Lab, Department of Electronic Engineering, Tsinghua University

Office: 10-202, Rohm Building, Haidian District, Beijing, China, 100084

Email: shz17@mails.tsinghua.edu.cn

Biography

I am a third-year Ph.D. in the Electronic Engineering Department of Tsinghua University, advised by Prof. Yong Li. I obtained my B.E. degree from the same department in 2017. Now, I am also a visiting scholar in the University of Southern California advised by Prof. Yan Liu.

My research interests include graph neural network, time series analysis and spatio-temporal data mining.

Education

  • Ph.D., Sept. 2017 - Present

Electronic Engineering Department, Tsinghua University, Beijing, China

Advisor: Prof. Yong Li


  • Visiting Scholar and Research Assistant, Apr. 2019- Present

Computer Science Department , University of Southern California, Los Angeles, CA, USA

Advisor: Prof. Yan Liu


  • B.E., Sept. 2013 - Jul. 2017

Electronic Engineering Department, Tsinghua University, Beijing, China

Publication


Conference Papers


  • Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network.

Hongzhi Shi, Quanming Yao, Qi Guo, Yaguang Li, Lingyu Zhang, Jieping Ye, Yong Li, Yan Liu.

IEEE International Conference on Data Engineering (ICDE), 2020. (Research track, Short paper)


  • State-Sharing Sparse Hidden Markov Models for Personalized Sequences. [pdf]

Hongzhi Shi, Chao Zhang, Quanming Yao, Yong Li, Funing Sun, Depeng Jin.

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019. (Research track, Oral paper, Acceptance rate≈9.2%)


  • Semantics-Aware Hidden Markov Model for Human Mobility. [pdf]

Hongzhi Shi, Hancheng Cao, Xiangxin Zhou, Yong Li, Chao Zhang, Vassilis Kostakos, Funing Sun, Fanchao Meng.

SIAM International Conference on Data Mining (SDM), 2019. (Full paper, Acceptance rate≈22.7%)


  • A Decomposition Approach for Urban Anomaly Detection Across Spatiotemporal Data.[pdf]

Mingyang Zhang, Tong Li, Hongzhi Shi, Yong Li, Pan Hui.

International Joint Conferences on Artificial Intelligence (IJCAI), 2019. (Full paper, Acceptance rate≈17.9%)


  • DeepDPM: Dynamic Population Mapping via Deep Neural Network.[pdf]

Zefang Zong, Jie Feng, Kechun Liu, Hongzhi Shi, Yong Li.

AAAI Conference on Artificial Intelligence (AAAI), 2019. (Full paper, Acceptance rate≈16.2%)


Journal Papers


  • Semantics-Aware Hidden Markov Model for Human Mobility.[pdf]

Hongzhi Shi, Yong Li, Hancheng Cao, Xiangxin Zhou, Chao Zhang, Vassilis Kostakos.

IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019. (Impact factor = 3.857, short version got accepted by SDM'19)


  • Discovering Periodic Patterns for Large Scale Mobile Traffic Data: Method and Applications.[pdf]

Hongzhi Shi, Yong Li.

IEEE transactions on mobile computing (TMC), 2018. (Impact factor = 4.098)


  • Big data driven mobile traffic understanding and forecasting: A time series approach.[pdf]

Fengli Xu, Yuyun Lin, Jiaxin Huang, Di Wu, Hongzhi Shi, Jeungeun Song, and Yong Li.

IEEE transactions on services computing (TSC), 2016. (Impact factor = 3.520)

Working Experiences

DiDi AI Labs | Research Intern | Beijing Nov 2018 – Mar 2019

Designed multi-perspective graph convolutional networks for predicting origin-destination traffic

Supervisor: Prof. Yan Liu


China Telecom Institute | Big Data Research Intern | Beijing Jul 2016 – Sep 2016

Processed the large-scale cellular traffic data in Shanghai and implemented an online population estimation algorithm

Supervisor: Dr. Jingbo Sun

Awards

KDD 2019 Student Travel Award Jul 2019

Mathematical Contest in Modeling: Honorable Mention Jan 2016

Technological Innovation Excellence Scholarship Sep 2014 – Jun 2015 & Sep 2015 – Jun 2016

China Undergraduate Physics Competition: First Prize Nationwide Dec 2014

Academic Excellence Scholarship (top 20%) Sep 2013 – Jun 2014

Silver Medal, Chinese Physics Olympiad Nov 2012

Skills

Programming Languages: Python, MATLAB, Java, C, C++

Big Data Platform Tools: Spark, Hadoop

Deep Learning Tools: Pytorch, Keras, TensorFlow

Other Tools: Git, Latex, Vim, Linux, MS Offices

Professional Services

Program Committee Member: AAAI 2020

Conference External Reviewer: KDD 2019, AAAI 2019, CIKM 2019, SDM 2019, IMC 2018, INFOCOM 2018, WWW 2017

Teaching Assistantship

Data and Algorithm Sep 2018 - Jan 2019