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

Zhuoxuan Du

Research Intern - McGill University

zhuoxuan.du@mila.quebec

farzaneh.heidari@mila.quebec

Farzana Heidari

PhD - Université de Montréal

Principal supervisor :

Guillaume Rabusseau

Xixian Liu

PhD - Université de Montréal

xixian.liu@mila.quebec

walter.virany@mila.quebec

Jiarui Lu

PhD - Université de Montréal

jiarui.lu@mila.quebec

Collaborating researcher

Sophie Xhonneux

PhD - Université de Montréal

Principal supervisor :

Gauthier Gidel

lpxhonneux@gmail.com

Jinxi Yang

Collaborating researcher - Wuhan University

jinxi.yang@mila.quebec

Xinyu Yuan

PhD - Université de Montréal

xinyu.yuan@mila.quebec

Zhihao Zhan

PhD - Université de Montréal

zhihao.zhan@mila.quebec

Ya-Shi Zhang

PhD - Université de Montréal

ya-shi.zhang@mila.quebec

Jianan Zhao

PhD - Université de Montréal

jianan.zhao@mila.quebec

Publications

TIPS: A Tiny Inference-time Policy for Scaling in Diffusion Language Models

Ya-Shi Zhang

Pierre-Andr Nol

Oleksiy Ostapenko

Masked diffusion language models expose an inference-time choice: which positions become visible after each denoising pass. Many existing de… (see more)coding methods use hand-designed rules or require visibility to grow monotonically. We present a

2026-09-27

BeNTo @ Neural Information Processing Systems (published)

openreview.net

A new BP-LSTM hybrid wind power forecasting model based on improved SFLA optimization

Houjian Zhan

Jiaqiu Hu

Le Qi

Xinran Liu

Xianliang Cheng

Yan Chen

Zhenhua Xiong

To improve the accuracy of short-term wind power prediction, this paper proposes a short-term wind power hybrid forecasting model, ISFLA-BP-… (see more)LSTM, based on an improved Shuffled Frog Leaping Algorithm (ISFLA) optimized Back Propagation network (BP) and Long Short-term Memory network (LSTM). This method introduces an adaptive differential evolution operator to optimize local search operations, enhancing the ISFLA optimization algorithm's ability to escape "local optimal solutions" and speeding up convergence. Finally, the improved ISFLA algorithm is used to optimize the weights and biases of BP and the hyperparameters of LSTM separately, establishing short-term wind power prediction submodels. The final short-term wind power prediction values are obtained by integrating the power outputs of ISFLA-BP and ISFLA-LSTM. This paper validates the performance of ISFLA and ISFLA-BP-LSTM on five commonly used test functions and wind energy data collected from the Xiyi Mountain Wind Farm in the Guangxi Zhuang Autonomous Region of China. The experimental results indicate that ISFLA in this paper outperforms the original SFLA in terms of performance, and the ISFLA-BP-LSTM hybrid model exhibits higher prediction accuracy compared to the initial model.

2026-09-07

Conference on Optics, Electronics, and Communication Engineering (published)

Engineered Nonheme Iron Enzymes Enable Asymmetric Hydrogenation of Alkenes

Yunfei He

Shuang-Yu Dai

Mei‐Yan Xu

Baixu Ma

Lizhi Tao

Zhen Liu

Developing biocatalytic systems capable of reducing simple alkenes is highly desirable for synthetic chemistry and biosynthesis, yet existin… (see more)g enzymes remain largely restricted to their ability to convert polarized, electron-deficient substrates. Here, we present a nonheme iron metalloenzyme platform that enables hydrogenation of styrenes, conjugated nitriles and amides, and nonconjugated olefins through a putative iron–hydride mechanism. Starting from the Fe(II)/ α -ketoglutarate-dependent dioxygenase GOX, iterative rounds of directed evolution produced an engineered “alkene hydrogenase” (AHase-6) containing 16 mutations and promoting NaBH 4 -driven reduction across diverse C═C bond motifs. Kinetic analysis indicates that this enzymatic hydrogenation process proceeds via formation of an enzyme–substrate ternary complex through a sequential mechanism. Mechanistic studies further reveal that alkene insertion occurs with regioselectivity governed primarily by substrate electronics and sterics. These findings establish nonheme iron enzymes as an unrecognized scaffold for metal–hydride-based hydrogenation and highlight their potential as sustainable, tunable alternatives to traditional catalytic systems.

2026-06-12

Journal of the American Chemical Society (published)

GeneZip: Region-Aware Compression for Long Context DNA Modeling

Hongyu Guo

Genomic sequences span billions of base pairs (bp), posing a fundamental challenge for genome-scale foundation models. Existing approaches l… (see more)argely sidestep this barrier by either scaling relatively small models to long contexts or relying on heavy multi-GPU parallelism. Here we introduce GeneZip, a DNA compression model that leverages a key biological prior: genomic information is highly imbalanced. Coding regions comprise only a small fraction (about 2 percent) yet are information-dense, whereas most non-coding sequence is comparatively information-sparse. GeneZip couples HNet-style dynamic routing with a region-aware compression-ratio objective, enabling adaptive allocation of representation budget across genomic regions. As a result, GeneZip learns region-aware compression and achieves 137.6x compression with only 0.31 perplexity increase. On downstream long-context benchmarks, GeneZip achieves comparable or better performance on contact map prediction, expression quantitative trait loci prediction, and enhancer-target gene prediction. By reducing effective sequence length, GeneZip unlocks simultaneous scaling of context and capacity: compared to the prior state-of-the-art model JanusDNA, it enables training models 82.6x larger at 1M-bp context, supporting a 636M-parameter GeneZip model at 1M-bp context. All experiments in this paper can be trained on a single A100 80GB GPU.

2026-05-27

FM4LS @ International Conference on Machine Learning (poster)

openreview.net

PerturbDiff: Functional Diffusion for Single-Cell Perturbation Modeling

Yashi Zhang

Hongyu Guo

Building Virtual Cells that can accurately simulate cellular responses to perturbations is a long-standing goal in systems biology. A fundam… (see more)ental challenge is that high-throughput single-cell sequencing is destructive: the same cell cannot be observed both before and after a perturbation. Thus, perturbation prediction requires mapping unpaired control and perturbed populations. Existing models address this by learning maps between distributions, but typically assume a single fixed response distribution when conditioned on observed cellular context (e.g., cell type) and the perturbation type. In reality, responses vary systematically due to unobservable latent factors such as microenvironmental fluctuations and complex batch effects, forming a manifold of possible distributions for the same observed conditions. To account for this variability, we introduce PerturbDiff, which shifts modeling from individual cells to entire distributions. By embedding distributions as points in a Hilbert space, we define a diffusion-based generative process operating directly over probability distributions. This allows PerturbDiff to capture population-level response shifts across hidden factors. Benchmarks on established datasets show that PerturbDiff achieves state-of-the-art performance in single-cell response prediction and generalizes substantially better to unseen perturbations. See our project page (https://katarinayuan.github.io/PerturbDiff-ProjectPage/), where code and data will be made publicly available (https://github.com/DeepGraphLearning/PerturbDiff).

2026-04-29

International Conference on Machine Learning (accepted)

proceedings.mlr.press

Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics

Haocheng Tang

Liang Shi

Protein dynamics underlie many biological functions, yet remain difficult to characterize due to the high computational cost of molecular dy… (see more)namics simulations and the scarcity of dynamic structural data. This survey reviews recent advances in artificial intelligence for protein dynamics from three perspectives: learning from structural ensembles and trajectories, learning from physical energy signals, and learning to accelerate molecular simulations. We summarize representative methods for conformation ensemble generation, trajectory generation, Boltzmann generators, physics-aware adaptation, machine learning potentials, coarse-grained modeling, and collective variable discovery. We further discuss available datasets and key open challenges, such as scalability, thermodynamic consistency, kinetic fidelity, and integration with experimental constraints.

2026-04-27

arXiv (preprint)

RobotPan: A 360$^\circ$ Surround-View Robotic Vision System for Embodied Perception

Jiahao Ma

Qiang Zhang

Peiran Liu

Zeran Su

Pihai Sun

Gang Han

Wen Zhao

Wei Cui

Zhang Zhang

Zhiyuan Xu

Renjing Xu

Miaomiao Liu

Yijie Guo

Surround-view perception is increasingly important for robotic navigation and loco-manipulation, especially in human-in-the-loop settings su… (see more)ch as teleoperation, data collection, and emergency takeover. However, current robotic visual interfaces are often limited to narrow forward-facing views, or, when multiple on-board cameras are available, require cumbersome manual switching that interrupts the operator's workflow. Both configurations suffer from motion-induced jitter that causes simulator sickness in head-mounted displays. We introduce a surround-view robotic vision system that combines six cameras with LiDAR to provide full 360

2026-04-14

arXiv (preprint)

Antibody discovery technology: innovation and outlook from classic to leading edge

PengFei WANG

2026-03-19

Scientia Sinica Vitae (published)

Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics

Liang Shi

Jiarui Lu

Junqi Liu

Chence Shi

Zhi Yang

Understanding the dynamic behavior of biomolecules is fundamental to elucidating biological function and facilitating drug discovery. While … (see more)Molecular Dynamics (MD) simulations provide a rigorous physical basis for studying these dynamics, they remain computationally expensive for long timescales. Conversely, recent deep generative models accelerate conformation generation but are typically either failing to model temporal relationship or built only for monomeric proteins. To bridge this gap, we introduce ATMOS, a novel generative framework based on State Space Models (SSM) designed to generate atom-level MD trajectories for biomolecular systems. ATMOS integrates a Pairformer-based state transition mechanism to capture long-range temporal dependencies, with a diffusion-based module to decode trajectory frames in an autoregressive manner. ATMOS is trained across crystal structures from PDB and conformation trajectory from large-scale MD simulation datasets including mdCATH and MISATO. We demonstrate that ATMOS achieves state-of-the-art performance in generating conformation trajectories for both protein monomers and complex protein-ligand systems. By enabling efficient inference of atomic trajectory of motions, this work establishes a promising foundation for modeling biomolecular dynamics.

2026-03-17

arXiv (preprint)

GENERator: A Long-Context Generative Genomic Foundation Model

Q. Li

Wei Wu

Yong Zhang

Zhihao Zhan

Rui Chen

Mingyang Li

Kun Fu

Junyan Qi

Yongzhou Bao

Chao Wang

Yiheng Zhu

Zhiyun Zhang

Fuli Feng

Jieping Ye

Liu Yuwen

Hui Xiong

Zheng Wang

Zhang, Yuanyuan

Chen, Ruipu … (see 2 more)

Wang, Chao

Tang, Jian

2026-02-03

Research Square (accepted)

Enhancing link prediction in biomedical knowledge graphs with BioPathNet

Emy Yue Hu

Svitlana Oleshko

Samuele Firmani

Hui Cheng

Zhaocheng Zhu

Maria Ulmer

Matthias Arnold

Maria Colomé-Tatché

Sophie Xhonneux

Annalisa Marsico

Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) m… (see more)ethods are limited in capturing this complexity. We present BioPathNet, a graph neural network framework based on the neural Bellman–Ford network (NBFNet), addressing limitations of traditional representation-based learning methods through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability, and allowing visualization of influential paths and biological validation. BioPathNet leverages a background regulatory graph for enhanced message passing and uses stringent negative sampling to improve precision and scalability. BioPathNet outperforms or matches existing methods across diverse tasks including gene function annotation, drug–disease indication, synthetic lethality and lncRNA–target interaction prediction. Our study identifies promising additional drug indications for diseases such as acute lymphoblastic leukaemia and Alzheimer’s disease, validated by medical experts and clinical trials. In addition, we prioritize putative synthetic lethal gene pairs and regulatory lncRNA–target interactions. BioPathNet’s interpretability will enable researchers to trace prediction paths and gain molecular insights.

2026-01-19

Nature Biomedical Engineering (published)

Enhancing link prediction in biomedical knowledge graphs with BioPathNet

Emy Yue Hu

Svitlana Oleshko

Samuele Firmani

Hui Cheng

Zhaocheng Zhu

Maria Ulmer

Matthias Arnold

Maria Colomé-Tatché

Sophie Xhonneux

Annalisa Marsico

2026-01-19

Nature Biomedical Engineering (published)