Zhiyu Wang | 王智语

I am an AI researcher advancing science and healthcare through innovative machine-learning methods. My interests span geometric deep learning, large language models, and AI for biology/healthcare.

Currently I work at Sapient Intelligence, applying Hierarchical Reasoning Models to healthcare scenarios in collaboration with Peking Union Medical College Hospital. I hold an M.Phil. from the University of Cambridge (Distinction, ranked 8/60) and a B.Sc. from UCL (First Class Honors).

I welcome opportunities to collaborate — feel free to reach out via email or LinkedIn. For more details, see my About page.

News

Jan 2026	Our PLASMA paper on protein local alignment via optimal transport has been accepted at ICLR 2026!
Jun 2025	I am delighted to announce that I have been awarded a £350 Churchill College Postgraduate Academic Travel Fund (PAT0062) to attend IJCAI25 and present my GraphAU-Pain paper.
Jun 2025	Our GraphAU-Pain paper has been accepted for presentation at the MiGA workshop at IJCAI25. Looking forward to seeing everyone in Guangzhou, China on August 28th!

Selected Publications

ICLR
Fast and Interpretable Protein Substructure Alignment via Optimal Transport

Zhiyu Wang, Bingxin Zhou, Jing Wang, and 4 more authors

In International Conference on Learning Representations (ICLR), 2026

Abs Paper Bib

PLASMA is a module based on optimal transport that can be seamlessly added to any protein representation learning model with minimal training or even no training to enable fast and highly accurate identification of locally similar substructures. Extensive experiments on 7 backbone protein language models across 3 tasks from the VenusX dataset demonstrated consistent and significant improvement (+10-30% ROC-AUC) across all backbones and tasks.
@inproceedings{wang2026plasma, title = {Fast and Interpretable Protein Substructure Alignment via Optimal Transport}, author = {Wang, Zhiyu and Zhou, Bingxin and Wang, Jing and Tan, Yang and Zhao, Weishu and Li\`o, Pietro and Hong, Liang}, year = {2026}, booktitle = {International Conference on Learning Representations (ICLR)}, eprint = {2510.11752}, archiveprefix = {arXiv}, primaryclass = {q-bio.BM}, github = {ZW471/PLASMA-Protein-Local-Alignment} }
arXiv
Topotein: Topological Deep Learning for Protein Representation Learning

Zhiyu Wang, Arian R. Jamasb, Mustafa Hajij, and 3 more authors

2025

Abs Paper Bib

Topotein introduces a comprehensive framework that applies topological deep learning to protein representation learning through the novel Protein Combinatorial Complex (PCC) and Topology-Complete Perceptron Network (TCPNet). TCPNet is an SE(3)-equivariant topological neural network that simultaneously learns protein residue-level and secondary structure-level features without losing any geometric information, demonstrating consistent improvements across four protein representation learning tasks.
@article{wang2025tcpnet, title = {Topotein: Topological Deep Learning for Protein Representation Learning}, author = {Wang, Zhiyu and Jamasb, Arian R. and Hajij, Mustafa and Morehead, Alex and Braithwaite, Luke and Li\`o, Pietro}, year = {2025}, eprint = {2509.03885}, archiveprefix = {arXiv}, primaryclass = {cs.LG}, github = {ZW471/TopoteinWorkshop} }