2017 年高中毕业于河北保定一中，此后进入直博项目，攻读博士至今。

I graduated from Baoding No.1 High School, Hebei in 2017, and have since been enrolled in a direct Ph.D. program, pursuing my doctoral degree to this day.

My research develops machine learning methods for single-cell sequencing data (scRNA-seq and scATAC-seq), with published work in reinforcement learning, contrastive coupling, variational autoencoders, neural ODEs, graph neural networks, and hyperbolic geometry applied to cell fate analysis and representation learning.

🎓 Background     : 保定一中 (2017) → Direct Ph.D. program → Present
🔬 Current focus   : Machine learning methods for single-cell genomics
🛠️ Building        : Open-source packages and paper companion platforms for single-cell representation learning
🤝 Open to         : Research collaboration and reproducible analysis

^# Equal contribution   ^* Corresponding author

Fu, Z.^#,*, Chen, C.^#, Zhang, K. (2026). Islands and bridges: Momentum contrastive coupling unifies discrete and continuous structure in single-cell omics. Biomedical Signal Processing and Control, 122, 110376.

Fu, Z.^#,*, Chen, C.^#, Wang, S. et al. (2025). iVAE: An Interpretable Representation Learning Framework Enhancing Clustering Performance for Single-Cell Data. BMC Biology, 23, 213.

Fu, Z.^#,*, Chen, C.^#, Wang, S. et al. (2026). iAODE for Benchmarking and Continuum Modeling of Single-Cell Chromatin Accessibility. Communications Biology.

Fu, Z.^#,*, Chen, C.^# (2025). Correlated Latent Space Learning for Structural Differentiation Modeling in Single Cell RNA Data. Computers in Biology and Medicine, 198(A), 111115.

Fu, Z.^#,*, Chen, C.^#, Wang, S. et al. (2025). GNODEVAE: A Graph-Based ODE-VAE Enhances Clustering for Single-Cell Data. BMC Genomics, 26, 767.

Chen, C.^#, Fu, Z.^#,*, Yang, J. et al. (2025). scFocus: Detecting Branching Probabilities in Single-cell Data with SAC. Computational and Structural Biotechnology Journal, 27, 2243--2263.

Fu, Z.^#,*, Fu, J.^#, Chen, C.^# et al. (2026). Lorentz-Regularized Interpretable VAE for Multi-Scale Single-Cell Transcriptomic and Epigenomic Embeddings. Frontiers in Genetics, 16, 1713727.

Fu, Z.^#, Chen, C.^#, Wang, S. et al. (2025). scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data. Biology, 14(6), 679.

Published Tools

	Repository	Description	Links
	MCCVAE	Momentum contrastive coupling for single-cell omics (BSPC, 2026)
	iVAE	Interpretable VAE for single-cell clustering
	iAODE	Neural ODE-VAE for scATAC-seq trajectory inference
	CODE	Correlated latent space learning and continuum modeling
	GNODEVAE	Graph-based ODE-VAE for clustering and dynamics
	scFocus	SAC-based lineage branching probability analysis
	LiVAE	Lorentz-regularized VAE for transcriptomic & epigenomic data
	scRL	Reinforcement learning for cell fate decision analysis

Web Applications

	Repository	Description	Demo
	scportal	Single-cell analysis portal and discovery hub	Live
	liora-ui	LAIOR single-cell benchmarking dashboard	Live
	mrnapp-intersection	mRNA intersection visualization	Live

Archive / Legacy Entries

Older or exploratory project entries are kept discoverable here without competing with the current public pages above.

Repository	Status	Description
LAIOR	Accepted / legacy code entry	Hyperbolic Neural-ODE VAE for interpretable single-cell manifold learning and trajectory inference
GAHIB	Exploratory / legacy	Graph Attention VAE with Hyperbolic Information Bottleneck
PanODE-DPMM	Exploratory / legacy	Flow-matching refined DPMM prior autoencoder
PanODE-Topic	Exploratory / legacy	Flow-matching-refined Dirichlet-prior autoencoder
CLOP-DiT	Exploratory / legacy	Contrastive language-omics pretraining with diffusion transformer
HSDE	Exploratory / legacy	Hyperbolic stochastic differential equation modeling
MoCoO	Exploratory / legacy	Momentum contrastive optimization framework
scMetaIntel-Hub	Exploratory / legacy	Benchmarking local LLMs for single-cell dataset discovery