Portrait of Jian Tang

Jian Tang

Core Academic Member
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
AI for Science
Computational Biology
Generative Models
Graph Neural Networks
Large Language Models (LLM)
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
Principal supervisor :
PhD - Université de Montréal
PhD - Université de Montréal
Principal supervisor :
Collaborating researcher - Wuhan University
PhD - Université de Montréal
PhD - Université de Montréal

Publications

Session-Based Social Recommendation via Dynamic Graph Attention Networks
Zhiping Xiao
Yifan Wang
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.
Learning powerful policies and better dynamics models by encouraging consistency
COVI White Paper-Version 1.1
H. Alsdurf
T. Deleu
Prateek Gupta
Daphne Ippolito
R. Janda
Max Jarvie
Tyler Kolody
S. Krastev
Robert Obryk
D. Pilat
Nasim Rahaman
I. Rish
J. Rousseau
Abhinav Sharma
B. Struck … (see 3 more)
Yun William Yu