Jian Tang

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

Huiyu Cai

PhD - Université de Montréal

huiyu.cai@mila.quebec

Collaborating researcher

Farzaneh Heidari

PhD - Université de Montréal

Principal supervisor :

Guillaume Rabusseau

Jerry Lu

PhD - Université de Montréal

Shentong Mo

Collaborating researcher - Carnegie Mellon University

Chence Shi

PhD - Université de Montréal

Sophie Xhonneux

PhD - Université de Montréal

Principal supervisor :

Gauthier Gidel

lpxhonneux@gmail.com

Xinyu Yuan

PhD - Université de Montréal

xinyu.yuan@mila.quebec

Zhihao Zhan

PhD - Université de Montréal

Zuobai Zhang

PhD - Université de Montréal

zhaocheng.zhu@umontreal.ca

Jianan Zhao

PhD - Université de Montréal

PhD - Université de Montréal

Publications

The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges

Sitao Luan

Chenqing Hua

Qincheng Lu

Liheng Ma

Lirong Wu

Xinyu Wang

Minkai Xu

Xiao-Wen Chang

Doina Precup

Rex Ying

Stan Z. Li

Guy Wolf

Stefanie Jegelka

Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to b… (see more)e the main reason for the superiority of Graph Neural Networks (GNNs) over traditional Neural Networks (NNs) on graph-structured data, especially on node-level tasks. However, recent work has identified a non-trivial set of datasets where GNN's performance compared to the NN's is not satisfactory. Heterophily, i.e. low homophily, has been considered the main cause of this empirical observation. People have begun to revisit and re-evaluate most existing graph models, including graph transformer and its variants, in the heterophily scenario across various kinds of graphs, e.g. heterogeneous graphs, temporal graphs and hypergraphs. Moreover, numerous graph-related applications are found to be closely related to the heterophily problem. In the past few years, considerable effort has been devoted to studying and addressing the heterophily issue. In this survey, we provide a comprehensive review of the latest progress on heterophilic graph learning, including an extensive summary of benchmark datasets and evaluation of homophily metrics on synthetic graphs, meticulous classification of the most updated supervised and unsupervised learning methods, thorough digestion of the theoretical analysis on homophily/heterophily, and broad exploration of the heterophily-related applications. Notably, through detailed experiments, we are the first to categorize benchmark heterophilic datasets into three sub-categories: malignant, benign and ambiguous heterophily. Malignant and ambiguous datasets are identified as the real challenging datasets to test the effectiveness of new models on the heterophily challenge. Finally, we propose several challenges and future directions for heterophilic graph representation learning.

2024-07-12

ArXiv (preprint)

In-Context Learning, Can It Break Safety?

Sophie Xhonneux

David Dobre

Michael Noukhovitch

Gauthier Gidel

Dhanya Sridhar

2024-06-28

ICML.cc/2024/Workshop/NextGenAISafety (poster)

openreview.net

Augmenting Evolutionary Models with Structure-based Retrieval

Yining Huang

Zuobai Zhang

Debora Susan Marks

Pascal Notin

2024-06-17

ICML.cc/2024/Workshop/ML4LMS (poster)

openreview.net

GraphAny: A Foundation Model for Node Classification on Any Graph

Jianan Zhao

Hesham Mostafa

Mikhail Galkin

Michael M. Bronstein

Zhaocheng Zhu

Foundation models that can perform inference on any new task without requiring specific training have revolutionized machine learning in vis… (see more)ion and language applications. However, applications involving graph-structured data remain a tough nut for foundation models, due to challenges in the unique feature- and label spaces associated with each graph. Traditional graph ML models such as graph neural networks (GNNs) trained on graphs cannot perform inference on a new graph with feature and label spaces different from the training ones. Furthermore, existing models learn functions specific to the training graph and cannot generalize to new graphs. In this work, we tackle these two challenges with a new foundational architecture for inductive node classification named GraphAny. GraphAny models inference on a new graph as an analytical solution to a LinearGNN, thereby solving the first challenge. To solve the second challenge, we learn attention scores for each node to fuse the predictions of multiple LinearGNNs. Specifically, the attention module is carefully parameterized as a function of the entropy-normalized distance-features between multiple LinearGNNs predictions to ensure generalization to new graphs. Empirically, GraphAny trained on the Wisconsin dataset with only 120 labeled nodes can effectively generalize to 30 new graphs with an average accuracy of 67.26\% in an inductive manner, surpassing GCN and GAT trained in the supervised regime, as well as other inductive baselines.

2024-05-30

ArXiv (preprint)

GraphAny: A Foundation Model for Node Classification on Any Graph

Jianan Zhao

Hesham Mostafa

Mikhail Galkin

Michael M. Bronstein

Zhaocheng Zhu

2024-05-30

ArXiv (preprint)

The 1st International Workshop on Graph Foundation Models (GFM)

Haitao Mao

Jianan Zhao

Xiaoxin He

Zhikai Chen

Qian Huang

Zhaocheng Zhu

Micheal Bronstein

Xavier Bresson

Bryan Hooi

Haiyang Zhang

Xianfeng Tang

Luo Chen

Jiliang Tang

2024-05-13

Companion Proceedings of the ACM on Web Conference 2024 (published)

Dynamic System Modeling Using a Multisource Transfer Learning-Based Modular Neural Network for Industrial Application

Haoshan Duan

Xi Meng

JunFei Qiao

Establishing an accurate model of dynamic systems poses a challenge for complex industrial processes. Due to the ability to handle complex t… (see more)asks, modular neural networks (MNN) have been widely applied to industrial process modeling. However, the phenomenon of domain drift caused by operating conditions may lead to a cold start of the model, which affects the performance of MNN. For this reason, a multisource transfer learning-based MNN (MSTL-MNN) is proposed in this study. First, the knowledge-driven transfer learning process is performed with domain similarity evaluation, knowledge extraction, and fusion, aiming to form an initial subnetwork in the target domain. Then, the positive transfer process of effective knowledge can avoid the cold start problem of MNN. Second, during the data-driven fine-tuning process, a regularized self-organizing long short-term memory algorithm is designed to fine-tune the structure and parameters of the initial subnetwork, which can improve the prediction performance of MNN. Meanwhile, relevant theoretical analysis is given to ensure the feasibility of MSTL-MNN. Finally, the effectiveness of the proposed method is confirmed by two benchmark simulations and a real industrial dataset of a municipal solid waste incineration process. Experimental results demonstrate the merits of MSTL-MNN for industrial applications.

2024-05-01

IEEE Transactions on Industrial Informatics (published)

8-inch Wafer-scale Epitaxial Monolayer MoS2.

Hua Yu

Liangfeng Huang

Lanying Zhou

Yalin Peng

Xiuzhen Li

Peng Yin

Jiaojiao Zhao

M. Zhu

Shuopei Wang

Jieying Liu

Hongyue Du

Songge Zhang

Yuchao Zhou

Nianpeng Lu

Kaihui Liu

Na Li

Guangyu Zhang

2024-04-29

Advances in Materials (published)

8-inch Wafer-scale Epitaxial Monolayer MoS2.

Hua Yu

Liangfeng Huang

Lanying Zhou

Yalin Peng

Xiuzhen Li

Peng Yin

Jiaojiao Zhao

M. Zhu

Shuopei Wang

Jieying Liu

Hongyue Du

Songge Zhang

Yuchao Zhou

Nianpeng Lu

Kaihui Liu

Na Li

Guangyu Zhang

Large-scale, high-quality, and uniform monolayer MoS2 films are crucial for their applications in next-generation electronics and optoelectr… (see more)onics. Epitaxy is a mainstream technique for achieving high-quality MoS2 films and has been demonstrated at a wafer scale up to 4-inch. In this study, we report the epitaxial growth of 8-inch wafer-scale highly oriented monolayer MoS2 on sapphire with excellent spatial homogeneity, using a specially designed vertical chemical vapor deposition (VCVD) system. Field effect transistors (FETs) based on the as-grown 8-inch wafer-scale monolayer MoS2 film were fabricated and exhibited high performances, with an average mobility and an on/off ratio of 53.5 cm2V-1s-1 and 107, respectively. In addition, batch fabrication of logic devices and 11-stage ring oscillators were also demonstrated, showcasing excellent electrical functions. Our work may pave way of MoS2 in practical industry-scale applications. This article is protected by copyright. All rights reserved.

2024-04-29

Advances in Materials (published)

Model-independent Approach of the JUNO 8B Solar Neutrino Program

Jun-Zhang Zhao

B. Yue

Haoqi Lu

Yufeng Li

J. Ling

Zeyuan Yu

Angel Abusleme

Thomas Adam

Shakeel Ahmad

Rizwan Ahmed

Sebastiano Aiello

Muhammad Akram

Abid Aleem

Tsagkarakis Alexandros

Fengpeng An

Q. An

Giuseppe Andronico

Nikolay Anfimov

Vito Antonelli

Tatiana Antoshkina … (see 480 more)

Burin Asavapibhop

J. Andr'e

Didier Auguste

Weidong Bai

Nikita Balashov

Wander Baldini

Andrea Barresi

Davide Basilico

Eric Baussan

Marco Bellato

Antonio Bergnoli

Thilo Birkenfeld

Sylvie Blin

D. Blum

Simon Blyth

Anastasia Bolshakova

Mathieu Bongrand

Clément Bordereau

Dominique Breton

Augusto Brigatti

Riccardo Brugnera

Riccardo Bruno

Antonio Budano

Jose Busto

I. Butorov

Anatael Cabrera

Barbara Caccianiga

Hao Cai

Xiao Cai

Yanke Cai

Zucong Cai

Riccardo Callegari

Antonio Cammi

Agustin Campeny

C. Cao

Guofu Cao

Jun Cao

Rossella Caruso

C. Cerna

Chi Chan

Jinfan Chang

Yun Chang

Guoming Chen

Pingping Chen

Po-An Chen

Shaomin Chen

Xurong Chen

Yixue Chen

Yu Chen

Zhiyuan Chen

Zikang Chen

Jie Cheng

Yaping Cheng

Alexander Chepurnov

Alexey Chetverikov

Davide Chiesa

Pietro Chimenti

Artem Chukanov

Gérard Claverie

Catia Clementi

Barbara Clerbaux

Marta Colomer Molla

Selma Conforti Di Lorenzo

Daniele Corti

Flavio Dal Corso

Olivia Dalager

C. Taille

Z. Y. Deng

Ziyan Deng

Wilfried Depnering

Marco Diaz

Xuefeng Ding

Yayun Ding

Bayu Dirgantara

Sergey Dmitrievsky

Tadeas Dohnal

Dmitry Dolzhikov

Georgy Donchenko

Jianmeng Dong

Evgeny Doroshkevich

Marcos Dracos

Frédéric Druillole

Ran Du

S. X. Du

Stefano Dusini

Martin Dvorak

Timo Enqvist

H. Enzmann

Andrea Fabbri

D. Fan

Lei Fan

Jian Fang

Wen Fang

Marco Fargetta

Dmitry Fedoseev

Zheng-hao Fei

Li-Cheng Feng

Qichun Feng

R. Ford

Amélie Fournier

H. Gan

Feng Gao

Alberto Garfagnini

Arsenii Gavrikov

Marco Giammarchi

Nunzio Giudice

Maxim Gonchar

G. Gong

Hui Gong

Yuri Gornushkin

A. Gottel

Marco Grassi

Maxim Gromov

Vasily Gromov

M. H. Gu

Xiang Gu

Yunting Gu

M. Guan

Yuduo Guan

Nunzio Guardone

Cong Guo

Jingyuan Guo

Wanlei Guo

Xinheng Guo

Yuhang Guo

Paul Hackspacher

Caren Hagner

Ran Han

Yang Han

Miao He

W. He

Tobias Heinz

Patrick Hellmuth

Yue-kun Heng

Rafael Herrera

Yuenkeung Hor

Shaojing Hou

Yee Hsiung

Bei-Zhen Hu

Hang Hu

Jianrun Hu

Jun Hu

Shouyang Hu

Tao Hu

Yuxiang Hu

Zhuojun Hu

Guihong Huang

Hanxiong Huang

Kaixuan Huang

Wenhao Huang

Xinglong Huang

X. T. Huang

Yongbo Huang

Jiaqi Hui

L. Huo

Wenju Huo

Cédric Huss

Safeer Hussain

Ara Ioannisian

Roberto Isocrate

Beatrice Jelmini

Ignacio Jeria

Xiaolu Ji

Huihui Jia

Junji Jia

Siyu Jian

Di Jiang

Wei Jiang

Xiaoshan Jiang

X. Jing

Cécile Jollet

L. Kalousis

Philipp Kampmann

Li Kang

Rebin Karaparambil

Narine Kazarian

Amina Khatun

Khanchai Khosonthongkee

Denis Korablev

K. Kouzakov

Alexey Krasnoperov

Nikolay Kutovskiy

Pasi Kuusiniemi

Tobias Lachenmaier

Cecilia Landini

Sébastien Leblanc

Victor Lebrin

F. Lefèvre

R. Lei

Rupert Leitner

Jason Leung

Daozheng Li

Demin Li

Fei Li

Fule Li

Gaosong Li

Huiling Li

Mengzhao Li

Min Li

Nan Li

Qingjiang Li

Ruhui Li

Ruiting Lei

Shanfeng Li

Tao Li

Teng Li

Weidong Li

Wei-guo Li

Xiaomei Li

Xiaonan Li

Xinglong Li

Yi Li

Yichen Li

Zepeng Li

Zhaohan Li

Zhibing Li

Ziyuan Li

Zonghui Li

Hao Liang

Jiaming Yan

Ayut Limphirat

Gen Lin

Shengxin Lin

Tao Lin

Ivano Lippi

Yang Liu

Haidong Liu

H. Liu

Hongbang Liu

Hongjuan Liu

Hongtao Liu

Hui Liu

Jianglai Liu

Jinchang Liu

Min Liu

Qian Liu

Q. Liu

Runxuan Liu

Shubin Liu

Shulin Liu

Xiaowei Liu

Xiwen Liu

Yong Liu

Yunzhe Liu

Alexey Lokhov

Paolo Lombardi

Claudio Lombardo

K. Loo

Chuan Lu

Jingbin Lu

Junguang Lu

Shuxiang Lu

Bayarto Lubsandorzhiev

Sultim Lubsandorzhiev

Livia Ludhova

Arslan Lukanov

Daibin Luo

F. Luo

Guang Luo

Shu Luo

Wu Luo

Xiaojie Luo

Vladimir Lyashuk

B. Ma

Bing Ma

R. Q. Ma

Si Ma

Xiaoyan Ma

Xubo Ma

Jihane Maalmi

Jingyu Mai

Yury Malyshkin

Roberto Carlos Mandujano

Fabio Mantovani

Francesco Manzali

Xin Mao

Yajun Mao

S. Mari

F. Marini

Cristina Martellini

Gisèle Martin-chassard

Agnese Martini

Matthias Mayer

Davit Mayilyan

Ints Mednieks

Yu Meng

Anselmo Meregaglia

Emanuela Meroni

David J. Meyhofer

Mauro Mezzetto

Jonathan Andrew Miller

Lino Miramonti

Paolo Montini

Michele Montuschi

Axel Muller

M. Nastasi

D. Naumov

Elena Naumova

Diana Navas-Nicolas

Igor Nemchenok

Minh Thuan Nguyen Thi

Alexey Nikolaev

F. Ning

Zhe Ning

Hiroshi Nunokawa

Lothar Oberauer

Juan Pedro Ochoa-Ricoux

Alexander Olshevskiy

Domizia Orestano

Fausto Ortica

Rainer Othegraven

A. Paoloni

Sergio Parmeggiano

Y. P. Pei

Nicomede Pelliccia

Anguo Peng

Yu Peng

Yuefeng Peng

Z-R Peng

Frédéric Perrot

P. Petitjean

Fabrizio Petrucci

Oliver Pilarczyk

Luis Felipe Piñeres Rico

Artyom Popov

Pascal Poussot

Ezio Previtali

Fazhi Qi

M. Qi

Sen Qian

X. Qian

Zhen Qian

Hao-xue Qiao

Zhonghua Qin

S. Qiu

Gioacchino Ranucci

Neill Raper

A. Re

Henning Rebber

Abdel Rebii

Mariia Redchuk

Bin Ren

Jie Ren

Barbara Ricci

Mariam Rifai

Mathieu Roche

Narongkiat Rodphai

Aldo M. Romani

Bedřich Roskovec

X. Ruan

Arseniy Rybnikov

Andrey Sadovsky

Paolo Saggese

Simone Sanfilippo

Anut Sangka

Utane Sawangwit

Julia Sawatzki

Michaela Schever

Cédric Schwab

Konstantin Schweizer

Alexandr Selyunin

Andrea Serafini

Giulio Settanta

M. Settimo

Zhuang Shao

V. Sharov

Arina Shaydurova

Jingyan Shi

Yanan Shi

Vitaly Shutov

Andrey Sidorenkov

Fedor Šimkovic

Chiara Sirignano

Jaruchit Siripak

Monica Sisti

Maciej Slupecki

Mikhail Smirnov

Oleg Smirnov

Thiago Sogo-Bezerra

Sergey Sokolov

Julanan Songwadhana

Boonrucksar Soonthornthum

Albert Sotnikov

Ondvrej vSr'amek

Warintorn Sreethawong

A. Stahl

Luca Stanco

Konstantin Stankevich

Duvsan Vstef'anik

Hans Steiger

Jochen Steinmann

Tobias Sterr

M. Stock

Virginia Strati

Alexander Studenikin

Jun Su

Shifeng Sun

Xilei Sun

Yongjie Sun Sun

Yongzhao Sun

Zhengyang Sun

Narumon Suwonjandee

Michal Szelezniak

Qiang Tang

Quan Tang

Xiao Tang

Alexander Tietzsch

Igor Tkachev

Tomas Tmej

M. Torri

K. Treskov

Andrea Triossi

Giancarlo Troni

Wladyslaw Trzaska

Cristina Tuve

Nikita Ushakov

Vadim Vedin

Giuseppe Verde

Maxim Vialkov

Benoit Viaud

Cornelius Moritz Vollbrecht

C. Volpe

Katharina von Sturm

Vit Vorobel

Dmitriy Voronin

Lucia Votano

Pablo Walker

Caishen Wang

Chung-Hsiang Wang

En Wang

Guoli Wang

Jian Wang

Jun Wang

Lucinda W. Wang

Meifen Wang

Meng Wang

Ruiguang Wang

Siguang Wang

Wei Wang

Wenshuai Wang

Xi Wang

Xiangyue Wang

Yangfu Wang

Yaoguang Wang

Yi Xing Wang

Yifang Wang

Yuanqing Wang

Yuman Wang

Zhe Wang

Zheng Wang

Zhimin Wang

Zongyi Wang

Apimook Watcharangkool

Wei Wei

Wenlu Wei

Yadong Wei

K. Wen

Kaile Wen

Christopher Wiebusch

S. Wong

Bjoern Wonsak

Diru Wu

Qun Wu

Zhi Wu

Michael Wurm

Jacques Wurtz

Christian Wysotzki

Yufei Xi

D. Xia

Xiang Xiao

Xiaochuan Xie

Yu-guang Xie

Z. P. Xie

Zhao-Liang Xin

Z. Xing

Benda D. Xu

Chengze Xu

Donglian Xu

Fanrong Xu

The physics potential of detecting 8B solar neutrinos will be exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model-… (see more)independent manner by using three distinct channels of the charged current (CC), neutral current (NC), and elastic scattering (ES) interactions. Due to the largest-ever mass of 13C nuclei in the liquid scintillator detectors and the expected low background level, 8B solar neutrinos are observable in the CC and NC interactions on 13C for the first time. By virtue of optimized event selections and muon veto strategies, backgrounds from the accidental coincidence, muon-induced isotopes, and external backgrounds can be greatly suppressed. Excellent signal-to-background ratios can be achieved in the CC, NC, and ES channels to guarantee the observation of the 8B solar neutrinos. From the sensitivity studies performed in this work, we show that JUNO, with 10 yr of data, can reach the 1σ precision levels of 5%, 8%, and 20% for the 8B neutrino flux, sin 2 θ 12 , and Δ m 21 2 , respectively. Probing the details of both solar physics and neutrino physics would be unique and helpful. In addition, when combined with the Sudbury Neutrino Observatory measurement, the world's best precision of 3% is expected for the measurement of the 8B neutrino flux.

2024-04-12

The Astrophysical Journal (published)

Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots

Chao Shen

Wenkang Zhan

Kaiyao Xin

Manyang Li

Zhenyu Sun

Hui Cong

Zhaofeng Wu

Chi Xu

Bo Xu

Zhongming Wei

Chao Zhao

Zhanguo Wang

Chunlai Xue

2024-03-29

Nature Communications (published)

Dual quantum spin Hall insulator by density-tuned correlations in TaIrTe4.

Thomas Siyuan Ding

Hongyu Chen

Anyuan Gao

Tiema Qian

Zumeng Huang

Zhe Sun

Xin Han

Alex Strasser

Jiangxu Li

Michael Geiwitz

Mohamed Shehabeldin

Vsevolod Belosevich

Zihan Wang

Yiping Wang

Kenji Watanabe

Takashi Taniguchi

David C. Bell

Ziqiang Wang

Liang Fu … (see 8 more)

Yang Zhang

Xiaofeng Qian

Kenneth S. Burch

Youguo Shi

Ni Ni

Guoqing Chang

Su-Yang Xu

Qiong Ma

2024-03-20

Nature (published)