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

Stephen Lu

Research Intern - McGill University

stephen.lu@mila.quebec

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

Minghao Xu

Collaborating researcher

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

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 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

F. 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

Z. 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

X. 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

Y. 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

G. Lin

Shengxin Lin

Tao Lin

Ivano Lippi

Yang Liu

Haidong Liu

Hao 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

Yan 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)

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

Zhaofeng Wu

Chi Xu

Bo Xu

Zhongming Wei

Chao Zhao

Zhanguo Wang

Chunlai Xue

2024-03-29

Nature Communications (published)

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 … (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)

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

2024-03-11

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

2024-03-04

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

Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations

Jiarui Lu

Zuobai Zhang

Bozitao Zhong

Chence Shi

The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consum… (see more)ing molecular dynamics (MD) simulations *in silico*. Recently, generative models has been leveraged as a surrogate sampler to obtain conformation ensembles with orders of magnitude faster and without requiring any simulation data (a "zero-shot" inference). However, being agnostic of the underlying energy landscape, the accuracy of such generative model may still be limited. In this work, we explore the few-shot setting of such pre-trained generative sampler which incorporates MD simulations in a tractable manner. Specifically, given a target protein of interest, we first acquire some seeding conformations from the pre-trained sampler followed by a number of physical simulations in parallel starting from these seeding samples. Then we fine-tuned the generative model using the simulation trajectories above to become a target-specific sampler. Experimental results demonstrated the superior performance of such few-shot conformation sampler at a tractable computational cost.

2024-03-04

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

Structure-Informed Protein Language Model

Zuobai Zhang

Jiarui Lu

Vijil Chenthamarakshan

Aurelie Lozano

Payel Das

Protein language models are a powerful tool for learning protein representations through pre-training on vast protein sequence datasets. Ho… (see more)wever, traditional protein language models lack explicit structural supervision, despite its relevance to protein function. To address this issue, we introduce the integration of remote homology detection to distill structural information into protein language models without requiring explicit protein structures as input. We evaluate the impact of this structure-informed training on downstream protein function prediction tasks. Experimental results reveal consistent improvements in function annotation accuracy for EC number and GO term prediction. Performance on mutant datasets, however, varies based on the relationship between targeted properties and protein structures. This underscores the importance of considering this relationship when applying structure-aware training to protein function prediction tasks. Code and model weights will be made available upon acceptance.

2024-03-04

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

Heterogeneous ensemble prediction model of CO emission concentration in municipal solid waste incineration process using virtual data and real data hybrid-driven

Runyu Zhang

Heng Xia

Jiakun Chen

Wen Yu

JunFei Qiao

2024-03-01

Journal of Cleaner Production (published)

Iterative Graph Self-Distillation

Hanlin Zhang

Shuai Lin

Weiyang Liu

Pan Zhou

Xiaodan Liang

Eric P. Xing

Recently, there has been increasing interest in the challenge of how to discriminatively vectorize graphs. To address this, we propose a met… (see more)hod called Iterative Graph Self-Distillation (IGSD) which learns graph-level representation in an unsupervised manner through instance discrimination using a self-supervised contrastive learning approach. IGSD involves a teacher-student distillation process that uses graph diffusion augmentations and constructs the teacher model using an exponential moving average of the student model. The intuition behind IGSD is to predict the teacher network representation of the graph pairs under different augmented views. As a natural extension, we also apply IGSD to semi-supervised scenarios by jointly regularizing the network with both supervised and self-supervised contrastive loss. Finally, we show that fine-tuning the IGSD-trained models with self-training can further improve graph representation learning. Empirically, we achieve significant and consistent performance gain on various graph datasets in both unsupervised and semi-supervised settings, which well validates the superiority of IGSD.

2024-03-01

IEEE Transactions on Knowledge and Data Engineering (published)

Deep Equilibrium Models For Algorithmic Reasoning

Sophie Xhonneux

Yu He

Andreea Deac

Gauthier Gidel

In this blogpost we discuss the idea of teaching neural networks to reach fixed points when reasoning. Specifically, on the algorithmic reas… (see more)oning benchmark CLRS the current neural networks are told the number of reasoning steps they need. While a quick fix is to add a termination network that predicts when to stop, a much more salient inductive bias is that the neural network shouldn't change it's answer any further once the answer is correct, i.e. it should reach a fixed point. This is supported by denotational semantics, which tells us that while loops that terminate are the minimum fixed points of a function. We implement this idea with the help of deep equilibrium models and discuss several hurdles one encounters along the way. We show on several algorithms from the CLRS benchmark the partial success of this approach and the difficulty in making it work robustly across all algorithms.

2024-02-16

ICLR.cc/2024/BlogPosts (published)