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

huiyu.cai@mila.quebec

Collaborateur·rice de recherche

Farzaneh Heidari

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Rabusseau

Jerry Lu

Doctorat - UdeM

Stephen Lu

Stagiaire de recherche - McGill

stephen.lu@mila.quebec

Shentong Mo

Collaborateur·rice de recherche - Carnegie Mellon University

Chence Shi

Doctorat - UdeM

Sophie Xhonneux

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Gauthier Gidel

lpxhonneux@gmail.com

Minghao Xu

Collaborateur·rice de recherche

Xinyu Yuan

Doctorat - UdeM

xinyu.yuan@mila.quebec

zhaocheng.zhu@umontreal.ca

Zhihao Zhan

Doctorat - UdeM

Doctorat - UdeM

Jianan Zhao

Doctorat - UdeM

Doctorat - UdeM

Publications

Multi-objective PSO semi-supervised random forest method for dioxin soft sensor

Wen Xu

Heng Xia

Wen Yu

JunFei Qiao

2024-01-01

Eng. Appl. Artif. Intell. (publié)

Multi-reservoir ESN-based prediction strategy for dynamic multi-objective optimization

Cuili Yang

Danlei Wang

JunFei Qiao

Wen Yu

2024-01-01

Information Sciences (publié)

NOx emissions prediction for MSWI process based on dynamic modular neural network

Haoshan Duan

Xi Meng

JunFei Qiao

2024-01-01

Expert systems with applications (publié)

Online Measurement of Dioxin Emission in Solid Waste Incineration Using Fuzzy Broad Learning

Heng Xia

Wen Yu

JunFei Qiao

Dioxin (DXN) is a persistent organic pollutant produced from municipal solid waste incineration (MSWI) processes. It is a crucial environmen… (voir plus)tal indicator to minimize emission concentration by using optimization control, but it is difficult to monitor in real time. Aiming at online soft-sensing of DXN emission, a novel fuzzy tree broad learning system (FTBLS) is proposed, which includes offline training and online measurement. In the offline training part, weighted k-means is presented to construct a typical sample pool for reduced learning costs of offline and online phases. Moreover, the novel FTBLS, which contains a feature mapping layer, enhance layer, and increment layer, by replacing the fuzzy decision tree with neurons applied to construct the offline model. In the online measurement part, recursive principal component analysis is used to monitor the time-varying characteristic of the MSWI process. To measure DXN emission, offline FTBLS is reused for normal samples; for drift samples, fast incremental learning is used for online updates. A DXN data from the actual MSWI process is employed to prove the usefulness of FTBLS, where the RMSE of training and testing data are 0.0099 and 0.0216, respectively. This result shows that FTBLS can effectively realize DXN online prediction.

2024-01-01

IEEE Transactions on Industrial Informatics (publié)

Tree Broad Learning System for Small Data Modeling.

Heng Xia

Wen Yu

JunFei Qiao

Broad learning system based on neural network (BLS-NN) has poor efficiency for small data modeling with various dimensions. Tree-based BLS (… (voir plus)TBLS) is designed for small data modeling by introducing nondifferentiable modules and an ensemble strategy to the traditional broad learning system (BLS). TBLS replaces the neurons of BLS with the tree modules to map the input data. Moreover, we present three new TBLS variant methods and their incremental learning implementations, which are motivated by deep, broad, and ensemble learning. Their major distinction is reflected in the incremental learning strategies based on: 1) mean square error (mse); 2) pseudo-inverse; and 3) pseudo-inverse theory and stack representation. Therefore, this study further explores the domain of BLS based on the nondifferentiable modules. The simulations are compared with some state-of-the-art (SOTA) BLS-NN and tree methods under high-, medium-, and low-dimensional benchmark datasets. Results show that the proposed method outperforms the BLS-NN, and the modeling accuracy is remarkably improved with the small training data of the proposed TBLS.

2024-01-01

IEEE Trans. Neural Networks Learn. Syst. (publié)

Zero-shot Logical Query Reasoning on any Knowledge Graph

Mikhail Galkin

Jincheng Zhou

Bruno Ribeiro

Zhaocheng Zhu

Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple KG completion and aims at answering compositional querie… (voir plus)s comprised of multiple projections and logical operations. Existing CLQA methods that learn parameters bound to certain entity or relation vocabularies can only be applied to the graph they are trained on which requires substantial training time before being deployed on a new graph. Here we present UltraQuery, an inductive reasoning model that can zero-shot answer logical queries on any KG. The core idea of UltraQuery is to derive both projections and logical operations as vocabulary-independent functions which generalize to new entities and relations in any KG. With the projection operation initialized from a pre-trained inductive KG reasoning model, UltraQuery can solve CLQA on any KG even if it is only finetuned on a single dataset. Experimenting on 23 datasets, UltraQuery in the zero-shot inference mode shows competitive or better query answering performance than best available baselines and sets a new state of the art on 14 of them.

2024-01-01

NeurIPS (publié)

arxiv.org

Giant Correlated Gap and Possible Room-Temperature Correlated States in Twisted Bilayer MoS_{2}.

Fanfan Wu

Qiaoling Xu

Qinqin Wang

Yanbang Chu

Lu Li

Jieying Liu

Jinpeng Tian

Yiru Ji

Le Liu

Yalong Yuan

Zhiheng Huang

Jiaojiao Zhao

Xiaozhou Zan

Kenji Watanabe

Takashi Taniguchi

Dongxia Shi

Gangxu Gu

Yang Xu

Lede Xian … (voir 3 de plus)

Wei Yang

Luojun Du

Guangyu Zhang

Moiré superlattices have emerged as an exciting condensed-matter quantum simulator for exploring the exotic physics of strong electronic co… (voir plus)rrelations. Notable progress has been witnessed, but such correlated states are achievable usually at low temperatures. Here, we report evidence of possible room-temperature correlated electronic states and layer-hybridized SU(4) model simulator in AB-stacked MoS_{2} homobilayer moiré superlattices. Correlated insulating states at moiré band filling factors v=1, 2, 3 are unambiguously established in twisted bilayer MoS_{2}. Remarkably, the correlated electronic state at v=1 shows a giant correlated gap of ∼126 meV and may persist up to a record-high critical temperature over 285 K. The realization of a possible room-temperature correlated state with a large correlated gap in twisted bilayer MoS_{2} can be understood as the cooperation effects of the stacking-specific atomic reconstruction and the resonantly enhanced interlayer hybridization, which largely amplify the moiré superlattice effects on electronic correlations. Furthermore, extreme large nonlinear Hall responses up to room temperature are uncovered near correlated electronic states, demonstrating the quantum geometry of moiré flat conduction band.

2023-12-18

Physical Review Letters (publié)

Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing

Shengchao Liu

Weili Nie

Chengpeng Wang

Jiarui Lu

Zhuoran Qiao

Ling Liu

Chaowei Xiao

Animashree Anandkumar

There is increasing adoption of artificial intelligence in drug discovery. However, existing studies use machine learning to mainly utilize … (voir plus)the chemical structures of molecules but ignore the vast textual knowledge available in chemistry. Incorporating textual knowledge enables us to realize new drug design objectives, adapt to text-based instructions and predict complex biological activities. Here we present a multi-modal molecule structure-text model, MoleculeSTM, by jointly learning molecules' chemical structures and textual descriptions via a contrastive learning strategy. To train MoleculeSTM, we construct a large multi-modal dataset, namely, PubChemSTM, with over 280,000 chemical structure-text pairs. To demonstrate the effectiveness and utility of MoleculeSTM, we design two challenging zero-shot tasks based on text instructions, including structure-text retrieval and molecule editing. MoleculeSTM has two main properties: open vocabulary and compositionality via natural language. In experiments, MoleculeSTM obtains the state-of-the-art generalization ability to novel biochemical concepts across various benchmarks.

2023-12-18

Nature Machine Intelligence (publié)

arxiv.org

Pretrainable Geometric Graph Neural Network for Antibody Affinity Maturation

Huiyu Cai

Zuobai Zhang

Mingkai Wang

Bozitao Zhong

Yanling Wu

Tianlei Ying

In the realm of antibody therapeutics development, increasing the binding affinity of an antibody to its target antigen is a crucial task. T… (voir plus)his paper presents GearBind, a pretrainable deep neural network designed to be effective for in silico affinity maturation. Leveraging multi-level geometric message passing alongside contrastive pretraining on protein structural data, GearBind capably models the complex interplay of atom-level interactions within protein complexes, surpassing previous state-of-the-art approaches on SKEMPI v2 in terms of Pearson correlation, mean absolute error (MAE) and root mean square error (RMSE). In silico experiments elucidate that pretraining helps GearBind become sensitive to mutation-induced binding affinity changes and reflective of amino acid substitution tendency. Using an ensemble model based on pretrained GearBind, we successfully optimize the affinity of CR3022 to the spike (S) protein of the SARS-CoV-2 Omicron strain. Our strategy yields a high success rate with up to 17-fold affinity increase. GearBind proves to be an effective tool in narrowing the search space for in vitro antibody affinity maturation, underscoring the utility of geometric deep learning and adept pre-training in macromolecule interaction modeling.

2023-12-07

bioRxiv (preprint)

Pretrainable Geometric Graph Neural Network for Antibody Affinity Maturation

Huiyu Cai

Zuobai Zhang

Mingkai Wang

Bozitao Zhong

Yanling Wu

Tianlei Ying

2023-12-07

bioRxiv (prépublication)

PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design

Chuanrui Wang

Bozitao Zhong

Zuobai Zhang

Narendra Chaudhary

Sanchit Misra

Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years. However, a univers… (voir plus)ally accepted method for evaluation has not been established, since the wet-lab validation can be overly time-consuming for the development of new algorithms, and the

2023-10-25

NeurIPS.cc/2023/Workshop/AI4D3 (poster)

openreview.net

PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design

Chuanrui Wang

Bozitao Zhong

Zuobai Zhang

Narendra Chaudhary

Sanchit Misra