Research Interests
Interdisciplinary studies of artificial intelligence and science (AI for Science), including bioinformatics, medical image analysis, cheminformatics, and the design of new materials.
Research Topics
- Molecular Representation Learning: Developing deep learning, self-supervised learning, and large language models to decipher the properties, functions, and interactions of compound molecules and biological macromolecules. Our methods are further extended to predict chemical reaction outcomes and facilitate retrosynthesis, aiming to speed up drug discovery and materials design.
- Biomedical image understanding: Designing semi-supervised and multi-modal learning approaches for pattern recognition and segmentation of biomedical images, with applications to biomarker identification and disease outcome prediction.
News
2024
| Date | News |
|---|---|
| Aug 19-22 | Lab members attended ICPOC 2024. Gufeng and Letian presented their posters on LLM for retrosynthesis and molecular chiral representation, respectively. |
| Aug 6-7 | Prof. Yang attended the 2nd RIKEN AIP-SJTU CS Joint Workshop on Machine Learning and Brain-like Intelligence (Agenda) at RIKEN AIP, delivering a talk on Uncertainty-based Semi-supervised Learning in Medical Image Segmentation. |
| July 19-21 | Lab members attended ISBRA 2024. Yifei and Yujia delivered oral presentations on their works on RNA-protein binding and RNA localization prediction, respectively. |
| July 5 | Lab members attended ACM TURC 2024, where Prof. Yang gave an invited talk "Deep Learning Revolutionizes Chemical Informatics: Model Evolution and Applications". |
| June 16 | Lab members attended the 2024 Bioinformatics and Intelligent Information Processing Conference (BIIP2024), where Prof. Yang and Runhan presented on "LLM in Chemistry" and "Chemical Reaction Prediction via Multi-view Learning," respectively. |
| May 30 | Our database CLC-DB containing 1800+ chiral ligands/catalysts of different chiral types is online! |
| May 30 | Yichong and Wenjing passed their PhD proposal defense. Congratulations! |
| May 24 | Prof. Yang gave a talk "Medical Image Analysis Driven by Large Models: From Single-Modality to Multi-Modal Learning" at the 13th Shanghai Jiao Tong University Vascular Disease Forum. |
| May 16 | Our collaborative work "Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method" is accepted accepted to the ACL Findings. Congratulations! |
| May 7 | Our proposed workshop, "Large Foundation Models for Multi-Modal Learning in Bioinformatics and Biomedicine Informatics," has been officially accepted for inclusion in BIBM'2024. We warmly invite researchers, academics, and industry professionals to contribution original works to our workshop! |
| Apr 17 | Yuyang's work ClusterDrop for addressing over-smoothing issue in GNNs is accepted by IJCAI 2024. Congratulations! |
| Apr 15 | Our GaCaMML paper is accepted by Artificial Intelligence in Medicine. Congratulations, Yuzhang and Guoshuai! |
| Mar 29 | Zebei, Guoshuai, Kuo, and Jingyao completed their Computer Science MS degree. Congratulations! |
| Feb 25 | Runhan's work RMVP for large-scale prepretraining of chemical reactions is published in Journal of Cheminformatics. Congratulations! |
| Feb 23 | Our CGMega paper is accepted by Nature Communications. Congratulations, Zebei! |
| Feb 22 | Our collaborative work CircSite on predicting circRNA-protein binding is published in Computers in Biology and Medicine. Congratulations! |
| Feb 10 | Guoshuai's work for designing new Al-Zn-Mg-Cu-Zr-Hf alloy is published in Materials Today Communications. Congratulations! |