Chengxi Zang

Chengxi Zang (臧承熙), Ph.D.

Postdoctoral Associate in Healthcare Policy and Research, Weill Cornell Medicine, Cornell University

Dr. Chengxi Zang is currently a postdoctoral research associate in the Weill Cornell Medicine, Cornell University. He got his Ph.D. from Tsinghua University in January 2019 with an Excellent Ph.D. Award at Tsinghua University (top 3 in the CS Department). He has worked extensively on mining and learning the Structures and Dynamics of complex social and biological systems. For example, he studied the human and social dynamics of WeChat at a billion scale, and he also investigated millions of information cascades in Tencent Weibo. Currently, his focus is AI for Healthcare and Drug Discovery. His research won ICDM'18 Best Paper Candidate and Best Paper Award at AAAI'20 Workshop on Deep Learning on Graphs.

[PDF] C.V. (Updated in September 2020)

What's new

Upcoming KDD 2020 events of mine:
- Our tutorial "Recent Advances on Graph Analytics and Its Applications in Healthcare" will start on August 23rd, 8:00 AM PDT (11:00 AM EDT). Click and find the materials and virtual location!
- A spotlight presentation of our system on Visualizing Deep Graph Generative Models for Drug Discovery in KDD 2020 Workshop on Applied Data Science for Healthcare on August 24, 2020!
- An oral presentaion of our full research paper MoFlow: An Invertible Flow Model for Generating Molecular Graphs will start on August 25th, 4:00 PM PDT (7:00 PM EDT)!
- Another oral presentaion of our full research paper Neural Dynamics on Complex Networks will start on August 27th, 10:00 AM PDT (1:00 PM EDT)!
Two workshop papers on drug disocvery has been accepted by ICML 2020 Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models and KDD 2020 Workshop on Applied Data Science for Healthcare respectively. See our presentation there.
Two papers has been accepted by KDD 2020 as full papers! One is on continuous-time Graph Neural Networks (GNN), and the other is on de novo drug design by flow-based generative GNN model. 2020-05-15
Our tutorial "Recent Advances on Graph Analytics and Its Applications in Healthcare" has been accepted by KDD 2020, curated and presented by: 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). 2020-06-01
Our Paper Neural Dynamics on Complex Networks won the Best Paper Award in AAAI 2020 Deep Learning on Graphs: Methodologies and Applications (DLGMA’20) workshop (February 8, 2020, New York City, NY).
My tutorial on Differential Deep Learning on Graphs and its Applications. Held at AAAI-20 (February 7, 2020, New York City, NY).

Research Interests

Graph Neural Networks, Graph Mining, Structured & Dynamic Data
Deep Learning, Deep Generative Models
AI for Healthcare, Drug Discovery
Social Networks, Network Science, Complex Systems

Research Experience

March 2020 - Present, Boehringer Ingelheim Pharmaceuticals, Inc., Biostatistics & Data Science Americas
February 2019 - Present, Weill Cornell Medicine, Cornell University, working with Prof. Fei Wang
June 2017 - May 2018, Center for Complex Network Research, 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

February 2019 - Present, Postdoctoral Research Associate, Weill Cornell Medicine, Cornell University
August 2013 - January 2019 , Doctor of Engineering, Tsinghua University, Department of Computer Science and Technology (Excellent Ph.D. Award in Tsinghua University, top 3 in the Department of Computer Science, and top 3% in all 2279 Ph.D.s)
September 2009 - July 2013, Bachelor of Science, Sun Yat-Sen University, Department of Computer Science and Technology (GPA 4.1/5, Rank 1st/100)

Tutorials

KDD 2020 tutorial "Recent Advances on Graph Analytics and Its Applications in Healthcare" on August 23rd, curated by 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).
AAAI 2020 tutorial Differential Deep Learning on Graphs and its Applications on February 7, 2020, New York City, NY, curated by Chengxi Zang and Fei Wang (Cornell University).

Publications

2020

1	Neural Dynamics on Complex Networks Chengxi Zang and Fei Wang. ACM Conference on Knowledge Discovery and Data Mining (KDD), 2020. (Accept Rate 216/1279 = 16.89%, Full Paper)	[PDF] [Slides] [YouTube] [Code]
2	MoFlow: An Invertible Flow Model for Generating Molecular Graphs Chengxi Zang and Fei Wang. ACM Conference on Knowledge Discovery and Data Mining (KDD), 2020. (Accept Rate 216/1279 = 16.89%, Full Paper)	[PDF] [Slides] [YouTube] [Code]
3	Recent Advances on Graph Analytics and Its Applications in Healthcare Fei Wang , Peng Cui , Jian Pei , Yangqiu Song , Chengxi Zang KDD 2020 Tutorial	[PDF] [Webpage]
4	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]
5	Neural Dynamics on Complex Networks Chengxi Zang and Fei Wang. AAAI 2020 Deep Learning on Graphs: Methodologies and Applications (DLGMA’20) workshop (Best Paper Award)	[PDF] [Webpage]
6	Differential Deep Learning on Graphs and its Applications Chengxi Zang and Fei Wang. AAAI 2020 Tutorial	[PDF] [Webpage]
7	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]

2019

1	Dynamical Origins of Distribution Functions Chengxi Zang, Peng Cui, Wenwu Zhu, and Fei Wang. ACM Conference on Knowledge Discovery and Data Mining (KDD), 2019. (Accept Rate (110 oral + 60 poster)/1200 = 14.2%, Oral)	[PDF]
2	Uncovering Pattern Formation of Information Flow. Chengxi Zang, Peng Cui, Chaoming Song, Wenwu Zhu, and Fei Wang. ACM Conference on Knowledge Discovery and Data Mining (KDD), 2019. (Accept Rate (110 oral + 60 poster)/1200 = 14.2%, Poster)	[PDF]
3	Fates of Microscopic Social Ecosystems: Keep Alive or Dead? Haoyang Li, Peng Cui, Chengxi Zang, Tianyang Zhang, Wenwu Zhu, and Yishi Lin. ACM Conference on Knowledge Discovery and Data Mining (KDD), 2019. (Accept Rate (110 oral + 60 poster)/1200 = 14.2%, Oral)	[PDF]
4	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. ACM Conference on Knowledge Discovery and Data Mining (KDD), 2019. (Accept Rate (45 oral + 100 poster)/700 = 20.7%, Poster, Applied Data Science Track)	[PDF]
5	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]

2018

1	Learning and Interpreting Complex Distributions in Empirical Data Chengxi Zang, Peng Cui, Wenwu Zhu. ACM Conference on Knowledge Discovery and Data Mining (KDD), 2018. (Accept Rate 181/983 = 18.4%, Oral)	[PDF] [PPT]
2	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]
3	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]
4	Modeling the Dynamics of WeChat Social System. Chengxi Zang, Peng Cui, Linyun Yu, Tianyang Zhang, Wenwu Zhu, Hao Ye. Draft	[PDF]

2017

1	Long Short Memory Process: Modeling Growth Dynamics of Microscopic Social Connectivity. Chengxi Zang, Peng Cui, Christos Faloutsos, Wenwu Zhu. ACM Conference on Knowledge Discovery and Data Mining (KDD), 2017. (Accept Rate 131/748 = 17.5%, Oral)	[PDF] [PPT] [Poster] [Code] [Video 1 or 2]
2	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]
3	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]

2016

1	Beyond Sigmoids: the NetTide Model for Social Network Growth, and its Applications. Chengxi Zang, Peng Cui, Christos Faloutsos. ACM Conference on Knowledge Discovery and Data Mining (KDD), 2016. (Accept Rate 142/784 = 18.1%)	[PDF] [PPT] [Poster] [Code]