Research

Research Interests

  • Data Mining, Health Data Science, AI for Healthcare, Drug Discovery, and Development
  • Real-World Data Analysis, Real-world Evidence, Electronic Health Records Mining
  • Long COVID, Post-Acute Sequelae of COVID-19 (PASC), Alzheimer's Disease, Mental Heath
  • Deep Learning, Deep Generative Models, Causal Inference, High-throughput Trial Emulation
  • Graph Mining, Graph Neural Networks, Complex Systems, Network Science, Social Media Analysis

Research Experiences

  • May 2022 - Present, Instructor in Population Health Sciences, Weill Medical College of Cornell University
  • February 2019 - Apirl 2022, Weill Cornell Medicine, Cornell University, working with Prof. Fei Wang
  • March 2020 - January 2021, Boehringer Ingelheim Pharmaceuticals, Inc., Postdoctoral Fellowship, Biostatistics & Data Science Americas
  • July 2018 - October 2018, WeChat, Tencent Rhino-Bird Elite Training Program
  • June 2017 - May 2018, Center for Complex Network Research, Visiting scholar, working with Prof. Albert-László Barabási
  • Summer 2015 & 2016, working with visiting Prof. Christos Faloutsos from Carnegie Mellon University
  • Summer 2014 & 2015, working with visiting Prof. Chaoming Song from Miami University
  • September 2013 - January 2019 , Department of Computer Science, Tsinghua University. Advisors Prof. Wenwu Zhu and Prof. Peng Cui
  • March 2011 - May 2012, Department of Computer Science, Sun Yat-sen University. Advisor: Prof. Jiwu Huang

Education

Tutorials

T2 Recent Advances on Graph Analytics and Its Applications in Healthcare
Fei Wang (Cornell University); Peng Cui (Tsinghua University); Jian Pei (Simon Fraser University); Yangqiu Song (Hong Kong University of Science and Technology); Chengxi Zang (Cornell University).
KDD 2020 Tutorial on August 23rd
[Webpage]
[PDF]
T1 Differential Deep Learning on Graphs and its Applications
Chengxi Zang (Cornell University) and Fei Wang (Cornell University).
AAAI 2020 Tutorial on February 7, 2020, New York City, NY.
[Webpage]
[PDF]

Publications

Journal Papers

J6 Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes
Hao Zhang, Chengxi Zang, Zhenxing Xu, Yongkang Zhang, Jie Xu, Jiang Bian, Dmitry Morozyuk, Dhruv Khullar, Yiye Zhang, Anna Nordvig, Edward Schenck, Elizabeth Shenkman, Russel Rothman, Jason Block, Kristin Lyman, Mark Weiner, Thomas Carton, Fei Wang, Rainu Kaushal
Nature Medicine (2022).
[PDF]
J5 Building the ModelChallenges and Considerations of Developing and Implementing Machine Learning Tools for Clinical Laboratory Medicine Practice
Yang, He S., Daniel D. Rhoads, Jorge Sepulveda, Chengxi Zang, Amy Chadburn, and Fei Wang.
Archives of Pathology & Laboratory Medicine (2022).
[PDF]
J4 Development of a screening algorithm for borderline personality disorder using electronic health records
Chengxi Zang, Marianne Goodman, Zheng Zhu, Lulu Yang, Ziwei Yin, Zsuzsanna Tamas, Vikas Mohan Sharma, Fei Wang, and Nan Shao
Scientific reports 12, no. 1 (2022): 1-12.
[PDF]
J3 Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients
Tingyi Wanyan, Hossein Honarvar, Suraj K. Jaladanki, Chengxi Zang, Nidhi Naik, Sulaiman Somani, Jessica K. De Freitas, Ishan Paranjpe, Akhil Vaid, Jing Zhang,Riccardo Miotto, Zhangyang Wang, Girish N. Nadkarni, Marinka Zitnik, Ariful Azad, Fei Wang, Ying Ding, Benjamin S. Glicksberg
Cell Patterns, 2021.
[PDF]
J2 Exploring the collective human behavior in cascading systems: a comprehensive framework
Yunfei Lu, Linyun Yu, Tianyang Zhang, Chengxi Zang, Peng Cui, Chaoming Song & Wenwu Zhu.
Knowledge and Information Systems (KAIS), 2020.
[PDF]
J1 On Power Law Growth of Social Networks.
Chengxi Zang, Peng Cui, Christos Faloutsos, Wenwu Zhu.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018
[PDF]
[Code]

Conference Papers

C13 SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records
Chengxi Zang and Fei Wang.
2021 IEEE International Conference on Data Mining (ICDM'21). (Acceptance Rate 98/990 =9.9%, Regular Paper)
[PDF]
[Code]
C12 MoFlow: An Invertible Flow Model for Generating Molecular Graphs
Chengxi Zang and Fei Wang.
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Accept Rate 216/1279 = 16.89%, Full Paper)
[PDF]
[Slides]
[YouTube]
[Code]
[Demo!]
C11 Neural Dynamics on Complex Networks
Chengxi Zang and Fei Wang.
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Accept Rate 216/1279 = 16.89%, Full Paper)
[PDF]
[Slides]
[YouTube]
[Code]
C10 Recent Advances on Graph Analytics and Its Applications in Healthcare
Fei Wang , Peng Cui , Jian Pei , Yangqiu Song , Chengxi Zang
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
[PDF]
[Webpage]
C9 Dynamical Origins of Distribution Functions
Chengxi Zang, Peng Cui, Wenwu Zhu, and Fei Wang.
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Accept Rate (110 oral + 60 poster)/1200 = 14.2%, Oral)
[PDF]
C8 Uncovering Pattern Formation of Information Flow.
Chengxi Zang, Peng Cui, Chaoming Song, Wenwu Zhu, and Fei Wang.
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Accept Rate (110 oral + 60 poster)/1200 = 14.2%, Poster)
[PDF]
C7 Fates of Microscopic Social Ecosystems: Keep Alive or Dead?
Haoyang Li, Peng Cui, Chengxi Zang, Tianyang Zhang, Wenwu Zhu, and Yishi Lin.
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Accept Rate (110 oral + 60 poster)/1200 = 14.2%, Oral)
[PDF]
C6 Uncovering the Co-driven Mechanism of Social and Content Links in User Churn Phenomena
Yunfei Lu, Lingyun Yu, Peng Cui, Chengxi Zang, Renzhe Xu, Yihao Liu, Lei Li, and Wenwu Zhu.
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Accept Rate (45 oral + 100 poster)/700 = 20.7%, Poster, Applied Data Science Track)
[PDF]
C5 Learning and Interpreting Complex Distributions in Empirical Data
Chengxi Zang, Peng Cui, Wenwu Zhu.
KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Accept Rate 181/983 = 18.4%, Oral)
[PDF]
[PPT]
C4 Collective Human Behavior in Cascading System: Discovery, Modeling and Applications.
Yunfei Lu, Linyun Yu, Tianyang Zhang, Chengxi Zang, Peng Cui, Chaoming Song, and Wenwu Zhu.
2018 IEEE International Conference on Data Mining (ICDM'18). (Acceptance Rate 84/948 =8.86%, Full Paper, Best Paper Candidate)
[PDF]
C3 Long Short Memory Process: Modeling Growth Dynamics of Microscopic Social Connectivity.
Chengxi Zang, Peng Cui, Christos Faloutsos, Wenwu Zhu.
KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Accept Rate 131/748 = 17.5%, Oral)
[PDF]
[PPT]
[Poster]
[Code]
[Video 1 or 2]
C2 Quantifying Structural Patterns of Information Cascades.
Chengxi Zang, Peng Cui, Chaoming Song, Christos Faloutsos, Wenwu Zhu.
International World Wide Web Conference (WWW), Poster, 2017. (Accept Rate 67/166 = 40.4%)
[PDF]
[PPT]
[Poster]
C1 Beyond Sigmoids: the NetTide Model for Social Network Growth, and its Applications.
Chengxi Zang, Peng Cui, Christos Faloutsos.
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Accept Rate 142/784 = 18.1%)
[PDF]
[PPT]
[Poster]
[Code]

Workshop Papers & Others

W6 Visualizing Deep Graph Generative Models for Drug Discovery
Karan Yang, Chengxi Zang, Fei Wang
2020 KDD Workshop on Applied Data Science for Healthcare
[PDF]
[Webpage]
W5 MoFlow: An Invertible Flow Model for Molecular Graph Generation
Chengxi Zang, Fei Wang
2020 ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (INNF+2020)
[PDF]
[Webpage]
W4 Neural Dynamics on Complex Networks
Chengxi Zang and Fei Wang.
2020 AAAI Deep Learning on Graphs: Methodologies and Applications (DLGMA’20) workshop (Best Paper Award)
[PDF]
[Webpage]
W3 Causation-Driven Visualizations for Insurance Recommendation
Zhixiu Liu, Chengxi Zang, Kun Kuang, Hao Zou, Hu Zheng, and Peng Cui
IEEE International Conference on Multimedia & Expo (ICME), 2019, the Cross-media Analysis for Semantic Knowledge Reasoning and Transfer Workshop.
[PDF]
W2 Modeling the Dynamics of WeChat Social System.
Chengxi Zang, Peng Cui, Linyun Yu, Tianyang Zhang, Wenwu Zhu, Hao Ye.
Draft
[PDF]
W1 Structural Patterns of Information Cascades and their Implications for Dynamics and Semantics.
Chengxi Zang, Peng Cui, Chaoming Song, Christos Faloutsos, Wenwu Zhu.
arXiv Draft
[PDF]

Ph.D. Dissertation

D1 Data-Driven Dynamical Modeling of Complex Social Systems (复杂社交系统的数据驱动动力学建模研究)
Chengxi Zang
Defense Committee Members: Prof. Xindong Wu, Prof. Jianyong Wang, Prof. Jie Tang, Prof. Huawei Shen, Prof. Dan Li, and Prof. Wenwu Zhu
[PDF]
[Webpage]

News and Media