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

Zewen Chi

Research Intern - Beijing Institute of Technology

Andreea-Ioana Deac

PhD - Université de Montréal

Research Intern - HEC Montréal

Michael Galkin

Collaborating researcher

Farzaneh Heidari

PhD - Université de Montréal

Principal supervisor :

Guillaume Rabusseau

Stephen Lu

Research Intern - McGill University

stephen.lu@mila.quebec

Jerry Lu

PhD - Université de Montréal

Meng Qu

PhD - Université de Montréal

Chence Shi

PhD - Université de Montréal

Walter Virany

Collaborating researcher

Chuanru Wang

Master's Research - Université de Montréal

Sophie Xhonneux

PhD - Université de Montréal

Principal supervisor :

lpxhonneux@gmail.com

Minghao Xu

Collaborating researcher

Xinyu Yuan

PhD - Université de Montréal

Zhihao Zhan

PhD - Université de Montréal

Zuobai Zhang

PhD - Université de Montréal

zhaocheng.zhu@umontreal.ca

Jianan Zhao

PhD - Université de Montréal

PhD - Université de Montréal

Publications

Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science

Xiangru Tang

Qiao Jin

Kunlun Zhu

Tongxin Yuan

Yichi Zhang

Wangchunshu Zhou

Meng Qu

Yilun Zhao

Zhuosheng Zhang

Arman Cohan

Zhiyong Lu

Mark Gerstein

2024-03-11

ICLR.cc/2024/Workshop/LLMAgents (poster)

F$^3$low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matching

Shaoning Li

Yusong Wang

Mingyu Li

Bin Shao

Nanning Zheng

Zhang Jian

2024-03-04

ICLR.cc/2024/Workshop/GEM (poster)

Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations

Jiarui Lu

Zuobai Zhang

Bozitao Zhong

Chence Shi

The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consum… (see more)ing molecular dynamics (MD) simulations *in silico*. Recently, generative models has been leveraged as a surrogate sampler to obtain conformation ensembles with orders of magnitude faster and without requiring any simulation data (a "zero-shot" inference). However, being agnostic of the underlying energy landscape, the accuracy of such generative model may still be limited. In this work, we explore the few-shot setting of such pre-trained generative sampler which incorporates MD simulations in a tractable manner. Specifically, given a target protein of interest, we first acquire some seeding conformations from the pre-trained sampler followed by a number of physical simulations in parallel starting from these seeding samples. Then we fine-tuned the generative model using the simulation trajectories above to become a target-specific sampler. Experimental results demonstrated the superior performance of such few-shot conformation sampler at a tractable computational cost.

2024-03-04

ICLR.cc/2024/Workshop/GEM (poster)

Structure-Informed Protein Language Model

Zuobai Zhang

Jiarui Lu

Vijil Chenthamarakshan

Aurelie Lozano

Payel Das

Protein language models are a powerful tool for learning protein representations through pre-training on vast protein sequence datasets. Ho… (see more)wever, traditional protein language models lack explicit structural supervision, despite its relevance to protein function. To address this issue, we introduce the integration of remote homology detection to distill structural information into protein language models without requiring explicit protein structures as input. We evaluate the impact of this structure-informed training on downstream protein function prediction tasks. Experimental results reveal consistent improvements in function annotation accuracy for EC number and GO term prediction. Performance on mutant datasets, however, varies based on the relationship between targeted properties and protein structures. This underscores the importance of considering this relationship when applying structure-aware training to protein function prediction tasks. Code and model weights will be made available upon acceptance.

2024-03-04

ICLR.cc/2024/Workshop/GEM (poster)

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

2024-03-01

Journal of Cleaner Production (published)

doi.org

Iterative Graph Self-Distillation

Hanlin Zhang

Shuai Lin

Weiyang Liu

Pan Zhou

Xiaodan Liang

Eric P. Xing

Recently, there has been increasing interest in the challenge of how to discriminatively vectorize graphs. To address this, we propose a met… (see more)hod called Iterative Graph Self-Distillation (IGSD) which learns graph-level representation in an unsupervised manner through instance discrimination using a self-supervised contrastive learning approach. IGSD involves a teacher-student distillation process that uses graph diffusion augmentations and constructs the teacher model using an exponential moving average of the student model. The intuition behind IGSD is to predict the teacher network representation of the graph pairs under different augmented views. As a natural extension, we also apply IGSD to semi-supervised scenarios by jointly regularizing the network with both supervised and self-supervised contrastive loss. Finally, we show that fine-tuning the IGSD-trained models with self-training can further improve graph representation learning. Empirically, we achieve significant and consistent performance gain on various graph datasets in both unsupervised and semi-supervised settings, which well validates the superiority of IGSD.

2024-03-01

IEEE Transactions on Knowledge and Data Engineering (published)

doi.org

Deep Equilibrium Models For Algorithmic Reasoning

Sophie Xhonneux

Yu He

Andreea Deac

In this blogpost we discuss the idea of teaching neural networks to reach fixed points when reasoning. Specifically, on the algorithmic reas… (see more)oning benchmark CLRS the current neural networks are told the number of reasoning steps they need. While a quick fix is to add a termination network that predicts when to stop, a much more salient inductive bias is that the neural network shouldn't change it's answer any further once the answer is correct, i.e. it should reach a fixed point. This is supported by denotational semantics, which tells us that while loops that terminate are the minimum fixed points of a function. We implement this idea with the help of deep equilibrium models and discuss several hurdles one encounters along the way. We show on several algorithms from the CLRS benchmark the partial success of this approach and the difficulty in making it work robustly across all algorithms.

2024-02-16

ICLR.cc/2024/BlogPosts (accepted)

Deep Equilibrium Models For Algorithmic Reasoning

Sophie Xhonneux

Yu He

Andreea Deac

2024-02-16

ICLR.cc/2024/BlogPosts (published)

In-Context Learning Can Re-learn Forbidden Tasks

Sophie Xhonneux

David Dobre