Portrait of Jian Tang

Jian Tang

Core Academic Member

tangjian@mila.quebec

Canada CIFAR AI Chair

Associate Professor, HEC Montréal, Department of Decision Sciences

Adjunct Professor, Université de Montréal, Department of Computer Science and Operations Research

Founder, BioGeometry

Research Topics

Computational Biology

Deep Learning

Generative Models

Graph Neural Networks

Molecular Modeling

Biography

Jian Tang is an Associate professor at HEC's Department of Decision Sciences. He is also an Adjunct professor at the Department of Computer Science and Operations Research at University of Montreal and a Core Academic member at Mila - Quebec AI Institute. He is a Canada CIFAR AI Chair and the Founder of BioGeometry, an AI startup that focuses on generative AI for antibody discovery. Tang’s main research interests are deep generative models and graph machine learning, and their applications to drug discovery. He is an international leader in graph machine learning, and LINE, his node representation method, has been widely recognized and cited more than five thousand times. He has also done pioneering work on AI for drug discovery, such as developing the first open-source machine learning frameworks for drug discovery, TorchDrug and TorchProtein.

Current Students

PhD - Université de Montréal

huiyu.cai@mila.quebec

Collaborating researcher

Farzaneh Heidari

PhD - Université de Montréal

Principal supervisor :

Guillaume Rabusseau

PhD - Université de Montréal

Research Intern - McGill University

stephen.lu@mila.quebec

PhD - Université de Montréal

Collaborating researcher

Sophie Xhonneux

PhD - Université de Montréal

Principal supervisor :

lpxhonneux@gmail.com

Collaborating researcher

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

zhaocheng.zhu@umontreal.ca

Publications

Contextualized Non-local Neural Networks for Sequence Learning

Pengfei Liu

Shuaichen Chang

Xuanjing Huang

Recently, a large number of neural mechanisms and models have been proposed for sequence learning, of which selfattention, as exemplified by… (see more) the Transformer model, and graph neural networks (GNNs) have attracted much attention. In this paper, we propose an approach that combines and draws on the complementary strengths of these two methods. Specifically, we propose contextualized non-local neural networks (CN3), which can both dynamically construct a task-specific structure of a sentence and leverage rich local dependencies within a particular neighbourhood.Experimental results on ten NLP tasks in text classification, semantic matching, and sequence labelling show that our proposed model outperforms competitive baselines and discovers task-specific dependency structures, thus providing better interpretability to users.

2019-07-17

Proceedings of the AAAI Conference on Artificial Intelligence (published)

Session-Based Social Recommendation via Dynamic Graph Attention Networks

Weiping Song

Zhiping Xiao

Yifan Wang

Laurent Charlin

Ming Zhang

Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their … (see more)users. Through these platforms, users can discover and create information that others will then consume. In that context, recommending relevant information to users becomes critical for viability. However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends. Moreover, the influencers may be context-dependent. That is, different friends may be relied upon for different topics. Modeling both signals is therefore essential for recommendations. We propose a recommender system for online communities based on a dynamic-graph-attention neural network. We model dynamic user behaviors with a recurrent neural network, and context-dependent social influence with a graph-attention neural network, which dynamically infers the influencers based on users' current interests. The whole model can be efficiently fit on large-scale data. Experimental results on several real-world data sets demonstrate the effectiveness of our proposed approach over several competitive baselines including state-of-the-art models.

2019-01-30

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (published)