Yuxuan Song「宋宇轩」

I am a Third-year Ph.D. student in the Institute for Artificial Industry Research at Tsinghua University, advised by Prof. Wei-Ying Ma. Previously, I used to be a researcher at Bytedance AI Lab, where I worked on deep generative models as well as their application in structured data with Prof. Hao Zhou and Prof. Lei Li. Before that, I received my bachelor and master degree from Shanghai Jiao Tong University (SJTU) where I am fortunate to be advised by Prof. Yong Yu.

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My ultimate goal is to integrate generalizable intelligence (LLMs) with advanced AI tools (e.g., AF3) to solve challenging real-world scientific problems and boost scientific discovery (LLM4Science). My Ph.D. research has focused on geometric generative models (bio/chemical/material structures), discrete diffusion models (biological sequences, Diffusion LLMs), and reasoning in LLMs.

Smooth Interpolation for Improved Discrete Graph Generative Models[coming soon]
Yuxuan Song*, Juntong Shi, Jingjing Gong, Minkai Xu, Stefano Ermon, Hao Zhou, Wei-Ying Ma
International Conference on Machine Learning (ICML), 2025.

Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule[coming soon]
Keyue Qiu*, Yuxuan Song*, Zhehuan Fan, Peidong Liu, Zhe Zhang, Mingyue Zheng, Hao Zhou, Wei-Ying Ma
International Conference on Machine Learning (ICML), 2025.

Steering Protein Family Design through Profile Bayesian Flow [pdf]
Jingjing Gong*, Yu Pei*, Siyu Long*, Yuxuan Song*, Zhe Zhang, Wenhao Huang, Ziyao Cao, Shuyi Zhang, Hao Zhou, Wei-Ying Ma
International Conference on Learning Representations (ICLR), 2025.
Oral Presentation [207/11672]

A Periodic Bayesian Flow for Material Generation [pdf][code]
Hanlin Wu*, Yuxuan Song*, Jingjing Gong, Ziyao Cao, Yawen Ouyang, Jianbing Zhang, Hao Zhou, Wei-Ying Ma, Jingjing Liu
International Conference on Learning Representations (ICLR), 2025.
Spotlight Paper

RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation[pdf]
Yiming Wang, Yuxuan Song, Minkai Xu, Rui Wang, Hao Zhou, Weiying Ma
International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.

MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space [pdf][code]
Yanru Qu*, Keyue Qiu*, Yuxuan Song*, Jingjing Gong, Jiawei Han, Mingyue Zheng, Hao Zhou, Weiying Ma
International Conference on Machine Learning (ICML), 2024.

Unified Generative Modeling of 3D Molecules with Bayesian Flow Networks [pdf][code]
Yuxuan Song*, Jingjing Gong*, Yanru Qu, Hao Zhou, Mingyue Zheng, Jingjing Liu, Weiying Ma
International Conference on Learning Representations (ICLR), 2024.
Oral Presentation [85/7262]

Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation [pdf][code]
Yuxuan Song*, Jingjing Gong*, Ziyao Cao, Minkai Xu, Stefano Ermon, Hao Zhou, Weiying Ma
Neural Information Processing Systems (NeurIPS), 2023.

Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D [pdf][code]
Bo Qiang*, Yuxuan Song*, Minkai Xu, Bowen Gao, Hao Zhou, Weiying Ma, Yanyan Lan
International Conference on Machine Learning (ICML), 2023.

Follow Your Path: a Progressive Method for Knowledge Distillation. [pdf]
Wenxian Shi*, Yuxuan Song*,Hao Zhou, Lei Li
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2021

Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation. [pdf]
Yuxuan Song, Ning Miao, Hao Zhou, Lantao Yu, Mingxuan Wang, Lei Li
Artificial Intelligence and Statistics (AISTATS) 2020.

Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip. [pdf][code]
Yuxuan Song, Minkai Xu, Lantao Yu, Hao Zhou, Shuo Shao, Yong Yu
AAAI Conference on Artificial Intelligence (AAAI), 2020.

I am a die-hard fan of Liaoning Flying Leopard. I adopted a lovely Ragdoll (many thanks to Changzhi Sun).