Jian Tang

farzaneh.heidari@mila.quebec

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

PhD - Université de Montréal

Principal supervisor :

Guillaume Rabusseau

Xixian Liu

PhD - Université de Montréal

xixian.liu@mila.quebec

Jiarui Lu

PhD - Université de Montréal

jiarui.lu@mila.quebec

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

Zuobai Zhang

PhD - Université de Montréal

zuobai.zhang@mila.quebec

PhD - HEC Montréal

ya-shi.zhang@mila.quebec

Jianan Zhao

PhD - Université de Montréal

jianan.zhao@mila.quebec

Publications

Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings

Kang Jin

Zuobai Zhang

Ke Zhang

Francesca Viggiani

Claire Callahan

Bruce J. Aronow

Jian Shu

Single-cell spatial transcriptomics such as in-situ hybridization or sequencing technologies can provide subcellular resolution that enables… (see more) the identification of individual cell identities, locations, and a deep understanding of subcellular mechanisms. However, accurate segmentation and annotation that allows individual cell boundaries to be determined remains a major challenge that limits all the above and downstream insights. Current machine learning methods heavily rely on nuclei or cell body staining, resulting in the significant loss of both transcriptome depth and the limited ability to learn latent representations of spatial colocalization relationships. Here, we propose Bering, a graph deep learning model that leverages transcript colocalization relationships for joint noise-aware cell segmentation and molecular annotation in 2D and 3D spatial transcriptomics data. Graph embeddings for the cell annotation are transferred as a component of multi-modal input for cell segmentation, which is employed to enrich gene relationships throughout the process. To evaluate performance, we benchmarked Bering with state-of-the-art methods and observed significant improvement in cell segmentation accuracies and numbers of detected transcripts across various spatial technologies and tissues. To streamline segmentation processes, we constructed expansive pre-trained models, which yield high segmentation accuracy in new data through transfer learning and self-distillation, demonstrating the generalizability of Bering.

2025-07-17

Nature Communications (published)

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… (see more)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… (see more)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 (published)

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… (see more)-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 … (see more)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 (preprint)

arxiv.org

Self-Evolving Curriculum for LLM Reasoning

Nicolas Gontier

Ehsan Kamalloo

2025-05-19

ArXiv (preprint)

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… (see more). 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 (published)

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 (published)

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 … (see 3 more)

Ji Feng

Luojun Du

Guangyu Zhang

2025-02-25

Physical Review Letters (published)

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 … (see 3 more)

Ji Feng

Luojun Du

Guangyu Zhang

2025-02-25

Physical Review Letters (published)

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 (published)