Jian Tang

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

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Xixian Liu

Doctorat - Université de Montréal

Site web

Jiarui Lu

Doctorat - UdeM

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Gauthier Gidel

Jinxi Yang

Collaborateur·rice de recherche - Wuhan University

Xinyu Yuan

Doctorat - UdeM

Github

Zhihao Zhan

Doctorat - UdeM

Doctorat - UdeM

Doctorat - HEC

Jianan Zhao

Doctorat - UdeM

Site web

Github

Publications

Aligning Protein Conformation Ensemble Generation with Physical Feedback

Jiarui Lu

Xiaoyin Chen

Stephen Z. Lu

Aurelie Lozano

Vijil Chenthamarakshan

Payel Das

Protein dynamics play a crucial role in protein biological functions and properties, and their traditional study typically relies on time-co… (voir plus)nsuming molecular dynamics (MD) simulations conducted in silico. Recent advances in generative modeling, particularly denoising diffusion models, have enabled efficient accurate protein structure prediction and conformation sampling by learning distributions over crystallographic structures. However, effectively integrating physical supervision into these data-driven approaches remains challenging, as standard energy-based objectives often lead to intractable optimization. In this paper, we introduce Energy-based Alignment (EBA), a method that aligns generative models with feedback from physical models, efficiently calibrating them to appropriately balance conformational states based on their energy differences. Experimental results on the MD ensemble benchmark demonstrate that EBA achieves state-of-the-art performance in generating high-quality protein ensembles. By improving the physical plausibility of generated structures, our approach enhances model predictions and holds promise for applications in structural biology and drug discovery.

2025-07-14

International Conference on Machine Learning (Accept (poster))

proceedings.mlr.press

DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads

Kailu Song

Yumin Zheng

Bowen Zhao

David H. Eidelman

Jun Ding

The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecula… (voir plus)r states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets.

2025-07-03

Nature Communications (publié)

Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Models

Zhanke Zhou

Zhaocheng Zhu

Xuan Li

Mikhail Galkin

Xiao Feng

Sanmi Koyejo

Bo Han

2025-06-29

ICML.cc/2025/Workshop/R2-FM (poster)

Efficient Regression-Based Training of Normalizing Flows for Boltzmann Generators

Oscar Davis

Michael Bronstein

Avishek Joey Bose

Simulation-free training frameworks have been at the forefront of the generative modelling revolution in continuous spaces, leading to large… (voir plus)-scale diffusion and flow matching models. However, such modern generative models suffer from expensive inference, inhibiting their use in numerous scientific applications like Boltzmann Generators (BGs) for molecular conformations that require fast likelihood evaluation. In this paper, we revisit classical normalizing flows in the context of BGs that offer efficient sampling and likelihoods, but whose training via maximum likelihood is often unstable and computationally challenging. We propose Regression Training of Normalizing Flows (RegFlow), a novel and scalable regression-based training objective that bypasses the numerical instability and computational challenge of conventional maximum likelihood training in favour of a simple

2025-06-10

ICML.cc/2025/Workshop/GenBio (spotlight)

Cosmic Ray Muon Polarization to Facilitate Atmospheric Neutrino Physics

Mingchen Sun

Shihan Zhao

Rui-Xuan Gao

He-Sheng Liu

Aiyu Bai

Atmospheric neutrinos (ATNs) offer a paradigm for understanding neutrino properties, while it is critical to quantify uncertainties in flux … (voir plus)modeling. Since ATNs are produced simultaneously with cosmic ray muons, precision measurements of cosmic ray muons, including arrival direction, energy spectra, and spin polarization, will help reduce ATN production uncertainties and facilitate atmospheric neutrino physics. This letter proposes using an array strategy to measure the spin polarization of cosmic ray muons, thereby strengthening the emergent synergies between cosmic ray and atmospheric neutrino physics. Constraints on long-standing atmospheric neutrino flux uncertainties at the percentage level in a few-GeV energy range are achievable within one year using a

2025-05-19

ArXiv (prépublication)

arxiv.org

Self-Evolving Curriculum for LLM Reasoning

Nicolas Gontier

Ehsan Kamalloo

2025-05-19

ArXiv (prépublication)

arxiv.org

Towards Protein Sequence & Structure Co-Design with Multi-Modal Language Models

Stephen Zhewen Lu

Jiarui Lu

Hongyu Guo

Proteins perform diverse biological functions, governed by the intricate relationship between their sequence and three-dimensional structure… (voir plus). While protein language models (PLMs) have demonstrated remarkable success in functional annotation and structure prediction, their potential for sequence-structure co-design remains underexplored. This limitation arises from pre-training objectives that favor masked token prediction over generative modeling. In this work, we systematically explore sampling strategies to enhance the generative capabilities of PLMs for co-design. Notably, we introduce a ranked iterative decoding with re-masking scheme, enabling PLMs to generate sequences and structures more effectively. Benchmarking ESM3 across multiple scales, we demonstrate that using PLMs effectively at sampling time for co-design tasks can outperform specialized architectures that lack comparable scaling properties. Our work advances the field of computational protein design by equipping PLMs with robust generative capabilities tailored to sequence-structure interdependence.

2025-03-05

ICLR.cc/2025/Workshop/LMRL (publié)

Design of Ligand-Binding Proteins with Atomic Flow Matching

Junqi Liu

Shaoning Li

Chence Shi

Zhi Yang

2025-03-04

ICLR.cc/2025/Workshop/GEM (publié)

Origin of Nonlinear Circular Photocurrent in 2D Semiconductor
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Yanchong Zhao

Fengyu Chen

Jing Liang

Mohammad Saeed Bahramy

Mingwei Yang

Yao Guang

Xiaomei Li

Zheng Wei

Jiaojiao Zhao

Mengzhou Liao

Cheng Shen

Qinqin Wang

Rong Yang

Kenji Watanabe

Takashi Taniguchi

Zhiheng Huang

Dongxia Shi

Kaihui Liu

Zhipei Sun … (voir 3 de plus)

Ji Feng

Luojun Du

Guangyu Zhang

2025-02-25

Physical Review Letters (publié)

Origin of Nonlinear Circular Photocurrent in 2D Semiconductor MoS_{2}.

Yanchong Zhao

Fengyu Chen

Jing Liang

Mohammad Saeed Bahramy

Mingwei Yang

Yao Guang

Xiaomei Li

Zheng Wei

Jiaojiao Zhao

Mengzhou Liao

Cheng Shen

Qinqin Wang

Rong Yang

Kenji Watanabe

Takashi Taniguchi

Zhiheng Huang

Dongxia Shi

Kaihui Liu

Zhipei Sun … (voir 3 de plus)

Ji Feng

Luojun Du

Guangyu Zhang

2025-02-25

Physical Review Letters (publié)

Author Correction: Isospin competitions and valley polarized correlated insulators in twisted double bilayer graphene

Le Liu

Shihao Zhang

Yanbang Chu

Cheng Shen

Yuan Huang

Yalong Yuan

Jinpeng Tian

Yiru Ji

Rong Yang

Kenji Watanabe

Takashi Taniguchi

Dongxia Shi

Jianpeng Liu

Wei Yang

Guangyu Zhang

2025-02-11

Nature Communications (publié)

Rapid restoration of potent neutralization activity against the latest Omicron variant JN.1 via AI rational design and antibody engineering

Yunji Liao

Hang Ma

Zhenyu Wang

Shusheng Wang

Yang He

Yunsong Chang

Huifang Zong

Haoneng Tang

Lei Wang

Yong Ke

Huiyu Cai

Ping Li

Chen Hua

Aleksandra Drelich

Bi‐Hung Peng

Jason Hsu

Vivian Tat

Chien‐Te K. Tseng … (voir 22 de plus)

Jingjing Song

Yunsheng Yuan

Mingyuan Wu

Junjun Liu

Yali Yue

Xiaoju Zhang

Ziqi Wang

Yang Li

Jing Li

Xiaodan Ni

Hongshi Li

Yuning Xiang

Yanlin Bian

Baohong Zhang

Haiyang Yin

Dimiter S. Dimitrov

J Gilly

Lei Han

Hua Jiang

Yueqing Xie

Jianwei Zhu

The rapid evolution of the viral genome has led to the continual generation of new variants of SARS-CoV-2. Developing antibody drugs with br… (voir plus)oad-spectrum and high efficiency is a long-term task. It is promising but challenging to develop therapeutic neutralizing antibodies (nAbs) through in vitro evolution based on antigen–antibody binding interactions. From an early B cell antibody repertoire, we isolated antibody 8G3 that retains its nonregressive neutralizing activity against Omicron BA.1 and various other strains in vitro. 8G3 protected ACE2 transgenic mice from BA.1 and WA1/2020 virus infection without adverse clinical manifestations and completely cleared viral load in the lungs. Similar to most IGHV3–53 antibodies, the binding sites of 8G3 and ACE2 largely overlap, enabling competition with ACE2 for binding to RBD. By comprehensively considering the binding free energy changes of the antigen–antibody complexes, the biological environment of their interactions, and the evolutionary direction of the antibodies, we were able to select 50 mutants. Among them, 11 were validated by experiments showing better neutralizing activities. Further, a combination of four mutations were identified in 8G3 that increased its neutralization potency against JN.1, the latest Omicron mutant, by approximately 1,500-fold, and one of the mutations led to an improvement in activity against multiple variants to a certain extent. Together, we established a procedure of rapid selection of neutralizing antibodies with potent SARS-CoV-2 neutralization activity. Our results provide a reference for engineering neutralizing antibodies against future SARS-CoV-2 variants and even other pandemic viruses.

2025-02-04

Proceedings of the National Academy of Sciences (publié)