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

PhD - Université de Montréal

Principal supervisor :

Xixian Liu

PhD - Université de Montréal

Website

Jiarui Lu

PhD - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

Gauthier Gidel

Jinxi Yang

Collaborating researcher - Wuhan University

Xinyu Yuan

PhD - Université de Montréal

Github

Zhihao Zhan

PhD - Université de Montréal

Github

Zuobai Zhang

PhD - Université de Montréal

PhD - HEC Montréal

Jianan Zhao

PhD - Université de Montréal

Website

Github

Publications

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

2024-01-31

Journal of Cleaner Production (published)

Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source

Ariane J. Marelli

Chao Li

Aihua Liu

Hanh Nguyen

Harry Moroz

James M. Brophy

Liming Guo

David L. Buckeridge

Archer Y. Yang

Yue Li

2024-01-31

JACC: Advances (published)

Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled

Shengchao Liu

Chengpeng Wang

Jiarui Lu

Weili Nie

Hanchen Wang

Zhuoxinran Li

Bolei Zhou

2024-01-29

TMLR (accepted)

Evaluating Representation Learning on the Protein Structure Universe

Arian R. Jamasb

Alex Morehead

Chaitanya K. Joshi

Zuobai Zhang

Kieran Didi

Simon Mathis

Charles Harris

Jianlin Cheng

Pietro Lio

Tom L. Blundell

We introduce ProteinWorkshop, a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural … (see more)Networks. We consider large-scale pre-training and downstream tasks on both experimental and predicted structures to enable the systematic evaluation of the quality of the learned structural representation and their usefulness in capturing functional relationships for downstream tasks. We find that: (1) large-scale pretraining on AlphaFold structures and auxiliary tasks consistently improve the performance of both rotation-invariant and equivariant GNNs, and (2) more expressive equivariant GNNs benefit from pretraining to a greater extent compared to invariant models. We aim to establish a common ground for the machine learning and computational biology communities to rigorously compare and advance protein structure representation learning. Our open-source codebase reduces the barrier to entry for working with large protein structure datasets by providing: (1) storage-efficient dataloaders for large-scale structural databases including AlphaFoldDB and ESM Atlas, as well as (2) utilities for constructing new tasks from the entire PDB. ProteinWorkshop is available at: github.com/a-r-j/ProteinWorkshop.

2024-01-15

ICLR.cc/2024/Conference (poster)

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… (see more)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

2024-01-15

ICLR.cc/2024/Conference (poster)

Towards Foundation Models for Knowledge Graph Reasoning

Hesham Mostafa

Foundation models in language and vision have the ability to run inference on any textual and visual inputs thanks to the transferable repre… (see more)sentations such as a vocabulary of tokens in language. Knowledge graphs (KGs) have different entity and relation vocabularies that generally do not overlap. The key challenge of designing foundation models on KGs is to learn such transferable representations that enable inference on any graph with arbitrary entity and relation vocabularies. In this work, we make a step towards such foundation models and present ULTRA, an approach for learning universal and transferable graph representations. ULTRA builds relational representations as a function conditioned on their interactions. Such a conditioning strategy allows a pre-trained ULTRA model to inductively generalize to any unseen KG with any relation vocabulary and to be fine-tuned on any graph. Conducting link prediction experiments on 57 different KGs, we find that the zero-shot inductive inference performance of a single pre-trained ULTRA model on unseen graphs of various sizes is often on par or better than strong baselines trained on specific graphs. Fine-tuning further boosts the performance.

2024-01-15

ICLR.cc/2024/Conference (poster)

CO emission predictions in municipal solid waste incineration based on reduced depth features and long short-term memory optimization

Runyu Zhang

Heng Xia

Xiaotong Pan

Wen Yu

JunFei Qiao

2024-01-07

Neural computing & applications (Print) (published)

CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization

Wenzheng Hu

Ning Liu

Zhengping Che

Mingyang Li

Changshui Zhang

Jianqiang Wang

Deep convolutional neural networks are shown to be overkill with high parametric and computational redundancy in many application scenarios,… (see more) and an increasing number of works have explored model pruning to obtain lightweight and efficient networks. However, most existing pruning approaches are driven by empirical heuristics and rarely consider the joint impact of channels, leading to unguaranteed and suboptimal performance. In this article, we propose a novel channel pruning method via class-aware trace ratio optimization (CATRO) to reduce the computational burden and accelerate the model inference. Utilizing class information from a few samples, CATRO measures the joint impact of multiple channels by feature space discriminations and consolidates the layerwise impact of preserved channels. By formulating channel pruning as a submodular set function maximization problem, CATRO solves it efficiently via a two-stage greedy iterative optimization procedure. More importantly, we present theoretical justifications on convergence of CATRO and performance of pruned networks. Experimental results demonstrate that CATRO achieves higher accuracy with similar computation cost or lower computation cost with similar accuracy than other state-of-the-art channel pruning algorithms. In addition, because of its class-aware property, CATRO is suitable to prune efficient networks adaptively for various classification subtasks, enhancing handy deployment and usage of deep networks in real-world applications.

2023-12-31

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

arxiv.org

A Foundation Model for Zero-shot Logical Query Reasoning

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… (see more)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, the first foundation model for inductive reasoning 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 after finetuning 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 15 of them.

2023-12-31

NeurIPS (published)

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

Wen Xu

Heng Xia

Wen Yu

JunFei Qiao

2023-12-31

Eng. Appl. Artif. Intell. (published)

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

Cuili Yang

Danlei Wang

JunFei Qiao

Wen Yu

2023-12-31

Information Sciences (published)

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… (see more)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.

2023-12-31

IEEE Transactions on Industrial Informatics (published)