Chence Shi 「史晨策」

I am a second-year PhD student at Montreal Institute for Learning Algorithms (Mila) supervised by Prof. Jian Tang. Before that, I received my B.S. in Computer Science from Peking University, advised by Prof. Ming Zhang.

Email: chence.shi [at] umontreal.ca / chenceshi1105 [at] gmail.com

Github   /   Google Scholar     

profile photo
Research Topics

My research interests lie at the intersection of generative models, geometric deep learning, graph representation learning, and drug discovery.

News

[Sep. 2021]  New!!  One paper was accepted at NeurIPS 2021.

[Aug. 2021]   New!!  We released a powerful and flexible machine learning platform, TorchDrug, for drug discovery.    [Homepage]    [Github]    [Twitter]    [Google Colab]

[May. 2021]  New!!  Our paper Learning Gradient Fields for Molecular Conformation Generation was accepted as a Long-talk paper at ICML 2021.

[May. 2021]  New!!  Three papers were accepted at ICML 2021.

Publications
Predicting Molecular Conformation via Dynamic Graph Score Matching
Shitong Luo*,  Chence Shi*,  Minkai Xu,  Jian Tang 
35th Conference on Neural Information Processing Systems (NeurIPS 2021)  
[PDF] Coming soon!
Learning Gradient Fields for Molecular Conformation Generation
Chence Shi*,  Shitong Luo*,  Minkai Xu,  Jian Tang 
38th International Conference on Machine Learning (ICML 2021)  
Long talk [top 3.0%]
[PDF]    [Code]    [Slides]
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
Minkai Xu,  Wujie Wang,  Shitong Luo,  Chence Shi,  Yoshua Bengio,  Rafael Gomez-Bombarelli,  Jian Tang 
38th International Conference on Machine Learning (ICML 2021)  
[PDF]    [Code]
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction
Hangrui Bi*,  Hengyi Wang*,  Chence Shi,  Connor Coley,  Jian Tang,  Hongyu Guo 
38th International Conference on Machine Learning (ICML 2021)  
[PDF]    [Code]
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
Yutong Xie,  Chence Shi,  Hao Zhou,  Yuwei Yang,  Weinan Zhang,  Yong Yu,  Lei Li 
9th International Conference on Learning Representations (ICLR 2021)  
Spotlight Presentation
[PDF]    [Code]
A Graph to Graphs Framework for Retrosynthesis Prediction
Chence Shi,  Minkai Xu,  Hongyu Guo,  Ming Zhang,  Jian Tang 
37th International Conference on Machine Learning (ICML 2020)  
[PDF]    [Code]    [Video Recording]    [Slides]
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Chence Shi*,  Minkai Xu*,  Zhaocheng Zhu,  Weinan Zhang,  Ming Zhang,  Jian Tang 
8th International Conference on Learning Representations (ICLR 2020)  
[PDF]    [Code]    [Video Recording]    [Slides]
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
Weiping Song,  Chence Shi,  Zhiping Xiao,  Zhijian Duan,  Yewen Xu,  Ming Zhang,  Jian Tang 
28th International Conference on Information and Knowledge Management (CIKM 2019)  
[PDF]    [Code]    [Slides]

(* equal contribution)

Open-source Library

TorchDrug: A powerful and flexible machine learning platform for drug discovery.
[Homepage]    [Github]    [Twitter]    [Google Colab]

Recommender Systems: Code base on different recommendation topics, a comprehensive reading list and a set of bechmark data sets.
[Github]

Professional Services

Program Committee member / Reviewer: ICML 2021, NeurIPS 2021, ICLR 2022

Awards

IVADO Excellence Scholarships - Msc (20,000 CAD per year), 2020



Updated at Sep. 2021