Zeyu Fu
AI for Science · Science for AI

Zeyu Fu

Ph.D. Candidate · methods, systems, and research tooling

I work on two axes: AI for Science (methods, benchmarks, and research software) and Science for AI (harnesses, orchestration, telemetry, and evaluation). Domain papers are listed as published case work.

13 peer-reviewed papers highlighted on this homepage
10 published software projects with code or package distribution
7 public sites and tools linked from this homepage
Suggested route

Start broad, then open the detail you need

A simple path from the homepage overview to broader browsing and then benchmark-specific detail.

Overview Browse Compare

About

Direct statement of work: AI for Science and Science for AI.

See public tools

I work on two axes. AI for Science: machine learning methods, benchmarks, and open software. Science for AI: harnesses, orchestration, telemetry, and evaluation. Single-cell and other domain papers are published case work, not the full scope of the page.

AI for ScienceScience for AIMethodsBenchmarksAgent HarnessesOrchestrationTelemetryOpen Releases

On this site

AI for Science

Published methods, benchmarks, packages, and companion sites.

Science for AI

Agent harness work across Claude Code, Codex, Grok Build, Antigravity, OpenCode, and the oh-my-* series: skills, plugins, runtime loops, telemetry. Upstream contributor to oh-my-openagent (33 merged PRs on team mode, tmux subagent runtime, and runtime fallback; ranked 11th of 313 contributors by commits as of Sep 2026).

Published case work

Earlier single-cell papers remain listed as published case work.

Profiles

GitHub, ORCID, Scopus, and Web of Science for code, citations, and authorship tracking.

Research focus

Three direct lines

Methods work, agent-harness work, and published domain papers.

Match focus to software
01

AI for Science

Methods, benchmarks, and reproducible software releases.

Methods Benchmarks Packages
02

Science for AI

Harnesses, orchestration, telemetry, and evaluation for research and engineering agents.

Harness Orchestration Telemetry
03

Published case work

Single-cell and other domain papers are listed as published case work.

Case work Domain papers 13 papers

Publications

# Equal contribution   * Corresponding author   13 peer-reviewed papers

1

Islands and bridges: Momentum contrastive coupling unifies discrete and continuous structure in single-cell omics

Fu, Z.#,*, Chen, C.#, Zhang, K. · Biomedical Signal Processing and Control 122, 110376 · 2026

2

CCVGAE: A centroid-coupled variational graph attention autoencoder for stable and interpretable single-cell representation learning

Fu, Z.#,*, Liu, Y.#, Wang, J., Wang, S. · Array 30, 100808 · 2026

3

CLOP-DiT: Structured-metadata-conditioned single-cell latent generation via contrastive language-omics pretraining and Diffusion Transformers

Fu, Z.#,*, Liu, Y.#, Wang, J.*, Wang, S.* · Array 30, 100934 · 2026

4

GAHIB: Graph Attention VAE with a Hyperbolic Information Bottleneck for Biologically Structured Single-Cell Representations

Fu, Z.#,*, Fu, J.#, Wang, X.#, Liu, Y.*, Ran, T.* · Frontiers in Genetics · 2026

5

scCCVGBen for Benchmarking of Single-Cell Representation Learning Anchored on a Centroid-Coupled Variational Graph Attention Autoencoder across scRNA-seq and scATAC-seq

Fu, Z.#, Fu, J.#, Chen, C.#, Zhang, K., Wang, J.*, Ran, T.*, Wang, S.* · Frontiers in Genetics · 2026

6

LAIOR: A Hyperbolic Neural ODE Variational Framework for Interpretable Single-Cell Manifold Learning and Trajectory Inference

Fu, Z.#,*, Fu, J.#, Zhang, K., Ran, T.*, Chen, C.* · Frontiers in Genetics · 2026

7

iVAE: An Interpretable Representation Learning Framework Enhancing Clustering Performance for Single-Cell Data

Fu, Z.#,*, Chen, C.#, Wang, S., Wang, J.*, Chen, S.* · BMC Biology 23, 213 · 2025

8

iAODE for Benchmarking and Continuum Modeling of Single-Cell Chromatin Accessibility

Fu, Z.#,*, Chen, C.#, Wang, S., Wang, J.*, Chen, S.* · Communications Biology · 2026

9

Correlated Latent Space Learning for Structural Differentiation Modeling in Single Cell RNA Data

Fu, Z.#,*, Chen, C.# · Computers in Biology and Medicine 198(A), 111115 · 2025

10

GNODEVAE: A Graph-Based ODE-VAE Enhances Clustering for Single-Cell Data

Fu, Z.#,*, Chen, C.#, Wang, S., Wang, J.*, Chen, S.* · BMC Genomics 26, 767 · 2025

11

scFocus: Detecting Branching Probabilities in Single-cell Data with SAC

Chen, C.#, Fu, Z.#,*, Yang, J. et al., Wang, C.*, Hu, X.* · Computational and Structural Biotechnology Journal 27, 2243–2263 · 2025

12

Lorentz-Regularized Interpretable VAE for Multi-Scale Single-Cell Transcriptomic and Epigenomic Embeddings

Fu, Z.#,*, Fu, J.#, Chen, C.#, Zhang, K., Wang, S.* · Frontiers in Genetics 16, 1713727 · 2026

13

scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data

Fu, Z.#, Chen, C.#, Wang, S., Wang, J.*, Chen, S.* et al. · Biology 14(6), 679 · 2025

Released tools

Public tools

Public pages for browsing datasets, checking benchmarks, reading companion materials, and using focused utilities.

Browse Benchmarks Utilities
Local infrastructure Protocol

Model Router

Local-only model routing infrastructure; no public browser API.

Read public protocol →
Local-First Local-First boundary

Kept off the homepage because it is a local-first workspace rather than a public hosted app surface.

Landing-Only Landing-Only boundary

Maintained as a landing-only reference surface, so the homepage does not present it as an actively browsable public tool.

AI Usage & Stats

Live AI interaction statistics for the Science for AI harness loop behind methods, benchmarks, and pages.

Tokscale AI Profile

Track the AI-assisted build loop across Claude Code, Codex, Grok Build, Antigravity, and OpenCode, coordinated by the oh-my-* harness series.

View Full Stats →