Jian Tang

farzaneh.heidari@mila.quebec

Biographie

Jian Tang est professeur agrégé au département de sciences de la décision de HEC. Il est aussi professeur associé au département informatique et recherche opérationnelle (DIRO) de l'Université de Montréal et un membre académique principal à Mila – Institut québécois d’intelligence artificielle. Il est titulaire d'une chaire de recherche en IA Canada-CIFAR et le fondateur de BioGeometry, une entreprise en démarrage spécialisée dans l'IA générative pour la découverte d'anticorps. Ses principaux domaines de recherche sont les modèles génératifs profonds, l'apprentissage automatique des graphes et leurs applications à la découverte de médicaments. Il est un leader international dans le domaine de l'apprentissage automatique des graphes, et son travail représentatif sur l'apprentissage de la représentation des nœuds, LINE, a été largement reconnu et cité plus de 5 000 fois. Il a également réalisé de nombreux travaux pionniers sur l'IA pour la découverte de médicaments, notamment le premier cadre d'apprentissage automatique à source ouverte pour la découverte de médicaments, TorchDrug et TorchProtein.

Étudiants actuels

Huiyu Cai

Doctorat - UdeM

huiyu.cai@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Rabusseau

Xixian Liu

Doctorat - Université de Montréal

xixian.liu@mila.quebec

Jiarui Lu

Doctorat - UdeM

jiarui.lu@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Gauthier Gidel

lpxhonneux@gmail.com

Jinxi Yang

Collaborateur·rice de recherche - Wuhan University

jinxi.yang@mila.quebec

Xinyu Yuan

Doctorat - UdeM

xinyu.yuan@mila.quebec

Zhihao Zhan

Doctorat - UdeM

zhihao.zhan@mila.quebec

Zuobai Zhang

Doctorat - UdeM

zuobai.zhang@mila.quebec

Doctorat - HEC

ya-shi.zhang@mila.quebec

Jianan Zhao

Doctorat - UdeM

jianan.zhao@mila.quebec

Publications

Augmenting Evolutionary Models with Structure-based Retrieval

Yining Huang

Zuobai Zhang

Debora Susan Marks

Pascal Notin

2024-06-16

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

NPA: Improving Large-scale Graph Neural Networks with Non-parametric Attention.

Wentao Zhang

Guochen Yan

Yu Shen

Yang Ling

Yangyu Tao

Bin CUI

Recent works show great interest in designing Graph Neural Networks (GNNs) that scale to large graphs. While previous work focuses on design… (voir plus)ing advanced sampling techniques for existing GNNs, the design of non-parametric GNNs, an orthogonal direction for scalable performance, has aroused lots of concerns recently. For example, nearly all top solutions in the Open Graph Benchmark leaderboard are non-parametric GNNs. Despite their high predictive performance and scalability, non-parametric GNNs still face two limitations. First, due to the propagation of over-smoothed features, they suffer from severe performance degradation along with the propagation depth. More importantly, they only consider the graph structure and ignore the feature influence during the non-parametric propagation, leading to sub-optimal propagated features. To address these limitations, we present non-parametric attention (NPA), a plug-and-play module that is compatible with non-parametric GNNs, to get scalable and deep GNNs simultaneously. We have deployed NPA in Tencent with the Angel platform, and we further evaluate NPA on both real-world datasets and large-scale industrial datasets. Experimental results on seven homophilic graphs (including the industrial Tencent Video graph) and five heterophilic graphs demonstrate NPA enjoys high performance -- achieves large performance gain over existing non-parametric GNNs, deeper architecture -- improves non-parametric GNNs with large model depth, and high scalability -- can support large-scale graphs with low time costs. Notably, it achieves state-of-the-art performance on the large ogbn-papers100M dataset.

2024-06-08

SIGMOD Conference Companion (publié)

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

Foundation models such as GPT-4 for natural language processing (NLP), Flamingo for computer vision (CV), have set new benchmarks in AI by d… (voir plus)elivering state-of-the-art results across various tasks with minimal task-specific data. Despite their success, the application of these models to the graph domain is challenging due to the relational nature of graph-structured data. To address this gap, we propose the Graph Foundation Model (GFM) Workshop, the first workshop for GFMs, dedicated to exploring the adaptation and development of foundation models specifically designed for graph data. The GFM workshop focuses on two critical questions: (1) How can the underlying capabilities of existing foundation models be effectively applied to graph data? (2) What foundational principles should guide the creation of models tailored to the graph domain? Through a curated set of panel sections, keynote talks, and paper presentations, our workshop intends to catalyze innovative approaches and theoretical frameworks for Graph Foundation Models (GFMs). We target a broad audience, encompassing researchers, practitioners, and students, and aim to lay the groundwork for the next wave of breakthroughs in integrating graph data with foundation models.

2024-05-12

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

Str2str: A Score-Based Framework for Zero-Shot Protein Conformation Sampling

Jiarui Lu

Bozitao Zhong

Zuobai Zhang

The dynamic nature of proteins is crucial for determining their biological functions and properties, for which Monte Carlo (MC) and molecula… (voir plus)r dynamics (MD) simulations stand as predominant tools to study such phenomena. By utilizing empirically derived force fields, MC or MD simulations explore the conformational space through numerically evolving the system via Markov chain or Newtonian mechanics. However, the high-energy barrier of the force fields can hamper the exploration of both methods by the rare event, resulting in inadequately sampled ensemble without exhaustive running. Existing learning-based approaches perform direct sampling yet heavily rely on target-specific simulation data for training, which suffers from high data acquisition cost and poor generalizability. Inspired by simulated annealing, we propose Str2Str, a novel structure-to-structure translation framework capable of zero-shot conformation sampling with roto-translation equivariant property. Our method leverages an amortized denoising score matching objective trained on general crystal structures and has no reliance on simulation data during both training and inference. Experimental results across several benchmarking protein systems demonstrate that Str2Str outperforms previous state-of-the-art generative structure prediction models and can be orders of magnitude faster compared to long MD simulations. Our open-source implementation is available at https://github.com/lujiarui/Str2Str

2024-05-06

International Conference on Learning Representations (Accept (poster))

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Dominique Beaini

Shenyang Huang

Joao Alex Cunha

Zhiyi Li

Gabriela Moisescu-Pareja

Oleksandr Dymov

Samuel Maddrell-Mander

Callum McLean

Jama Hussein Mohamud

Michael Craig

Cristian Gabellini

Kerstin Klaser

Josef Dean

Cas Wognum … (voir 14 de plus)

Maciej Sypetkowski

Hadrien Mary

Therence Bois

Andrew Fitzgibbon

Błażej Banaszewski

Chad Martin

Dominic Masters

Recently, pre-trained foundation models have shown significant advancements in multiple fields. However, the lack of datasets with labeled f… (voir plus)eatures and codebases has hindered the development of a supervised foundation model for molecular tasks. Here, we have carefully curated seven datasets specifically tailored for node- and graph-level prediction tasks to facilitate supervised learning on molecules. Moreover, to support the development of multi-task learning on our proposed datasets, we created the Graphium graph machine learning library. Our dataset collection encompasses two distinct categories. Firstly, the TOYMIX category modifies three small existing datasets with additional data for multi-task learning. Secondly, the LARGEMIX category includes four large-scale datasets with 344M graph-level data points and 409M node-level data points from ∼5M unique molecules. Finally, the ultra-large dataset contains 2,210M graph-level data points and 2,031M node-level data points coming from 86M molecules. Hence our datasets represent an order of magnitude increase in data volume compared to other 2D-GNN datasets. In addition, recognizing that molecule-related tasks often span multiple levels, we have designed our library to explicitly support multi-tasking, offering a diverse range of multi-level representations, i.e., representations at the graph, node, edge, and node-pair level. We equipped the library with an extensive collection of models and features to cover different levels of molecule analysis. By combining our curated datasets with this versatile library, we aim to accelerate the development of molecule foundation models. Datasets and code are available at https://github.com/datamol-io/graphium.

2024-05-06

International Conference on Learning Representations (Accept (poster))

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… (voir plus)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.

2024-04-30

IEEE Transactions on Industrial Informatics (publié)

8-inch Wafer-scale Epitaxial Monolayer MoS2.

Hua Yu

Liangfeng Huang

Lanying Zhou

Yalin Peng

Xiuzhen Li

Peng Yin

Jiaojiao Zhao

Min Zhu

Shuopei Wang

Jieying Liu

Hongyue Du

Songge Zhang

Yuchao Zhou

Nianpeng Lu

Kaihui Liu

Na Li

Guangyu Zhang

2024-04-28

Advances in Materials (publié)

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

Jun-Zhang Zhao

Bin 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 … (voir 477 de plus)

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

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

Dongsheng 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 Zhou

Yunting Gu

Mengyun 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

T. 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

Xiang 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

Rui Li

Shanfeng Li

Tao Li

Teng Li

Weidong Li

Wei-guo Li

Xiaomei Li

Xiao-Nan 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

Haidong Liu

Hongbang Liu

Hongjuan Liu

Hongtao Liu

H. Liu

Jianglai Liu

Jinchang Liu

Min Liu

Qian Liu

Qi Liu

Runxuan Liu

Shubin Liu

Shulin Liu

Xiaowei Liu

Xiwen Liu

Yang Liu

Yunzhe Liu

Alexey Lokhov

Paolo Lombardi

Claudio Lombardo

K. Loo

Chuan Lu

Jingbin Lu

Junguang Lu

Shuxian Du

Bayarto Lubsandorzhiev

Sultim Lubsandorzhiev

Livia Ludhova

Arslan Lukanov

Daibin Luo

Feng Luo

Guang Luo

Shu Luo

Wu Luo

Xiaojie Luo

Vladimir Lyashuk

Biao 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

Massimiliano Nastasi

D. Naumov

Elena Naumova

Diana Navas-Nicolas

Igor Nemchenok

Minh Thuan Nguyen Thi

Alexey Nikolaev

Feipeng Ning

Zhe Ning

Hiroshi Nunokawa

Lothar Oberauer

Juan Pedro Ochoa-Ricoux

Alexander Olshevskiy

Domizia Orestano

Fausto Ortica

Rainer Othegraven

Alessandro Paoloni

Sergio Parmeggiano

Y. P. Pei

Nicomede Pelliccia

Anguo Peng

Yuekun Heng

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

Xiangyang Qian

Zhen Qian

Hao-xue Qiao

Zhonghua Qin

Shoukang 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

Xianhui 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

Mariangela Settimo

Zhuang Shao

Vladislav 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 Wang

Yifang Wang

Yong Wang

Yuman Wang

Zhe Wang

Z. 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

Dongqin Xia

Xiang Xiao

Xiaochuan Xie

Yu-guang Xie

Zhangquan 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-… (voir plus)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.

2024-04-11

The Astrophysical Journal (publié)

arxiv.org

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

Chi Xu

Zhaofeng Wu

Bo Xu

Zhongming Wei

Chunlai Xue

Chao Zhao

Zhanguo Wang

2024-03-28

Nature Communications (publié)

arxiv.org

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 … (voir 8 de plus)

Yang Zhang

Xiaofeng Qian

Kenneth S. Burch

Youguo Shi

Ni Ni

Guoqing Chang

Su-Yang Xu

Qiong Ma

2024-03-19

Nature (publié)

arxiv.org

Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science

Xiangru Tang

Qiao Jin

Kunlun Zhu

Tongxin Yuan

Yichi Zhang

Wangchunshu Zhou

Meng Qu

Yilun Zhao

Zhuosheng Zhang

Arman Cohan

Zhiyong Lu

Mark Gerstein

2024-03-10

ICLR.cc/2024/Workshop/LLMAgents (poster)

F$^3$low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matching

Shaoning Li

Yusong Wang

Mingyu Li

Bin Shao

Nanning Zheng

Zhang Jian

2024-03-03

ICLR.cc/2024/Workshop/GEM (poster)